Knowledge Representation and Scalable Abstract Reasoning for Simulated Democracy in Unity

Abstract

We present a novel form of scalable knowledge representation about agents in a simulated democracy, e-polis, where real users respond to social challenges associated with democratic institutions, structured as Smart Spatial Types, a new type of Smart Building that changes architectural form according to the philosophical doctrine of a visitor. At the end of the game players vote on the Smart City that results from their collective choices.Our approach uses deductive systems in an unusual way: by integrating a model of democracy with a model of a Smart City we are able to prove quality aspects of the simulated democracy in different urban and social settings, while adding ease and flexibility to the development. Second, we can infer and reason with abstract knowledge, which is a limitation of the Unity platform; third, our system enables real-time decision-making and adaptation of the game flow based on the player’s abstract state, paving the road to explainability.Scalability is achieved by maintaining a dual-layer knowledge representation mechanism for reasoning about the simulated democracy that functions in a similar way to a two-level cache. The lower layer knows about the current state of the game by continually processing a high rate of events produced by the in-built physics engine of the Unity platform, e.g., it knows of the position of a player in space, in terms of his coordinates x,y,z as well as their choices for each challenge. The higher layer knows of easily-retrievable, user-defined abstract knowledge about current and historical states, e.g., it knows of the political doctrine of a Smart Spatial Type, a player’s philosophical doctrine, and the collective philosophical doctrine of a community players with respect to current social issues.

Full text

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Knowledge representation and scalable abstract reasoning for simulated
democracy in Unity
ELEFTHERIA KATSIRI1,∗, ALEXANDROS GAZIS1, ANGELOS PROTOPAPAS2
1Department of Electrical and Computer Engineering, School of Engineering,
Democritus University of Thrace,
67100 Xanthi,
GREECE
2Department of Civil Engineering, School of Engineering,
Democritus University of Thrace,
67100 Xanthi,
GREECE
Abstract: We present a novel form of scalable knowledge representation about agents in a simulated democracy,
e-polis, where real users respond to social challenges associated with democratic institutions, structured as Smart
Spatial Types, a new type of Smart Building that changes architectural form according to the philosophical doctrine
of a visitor. At the end of the game players vote on the Smart City that results from their collective choices.
Our approach uses deductive systems in an unusual way: by integrating a model of democracy with a model of a
Smart City we are able to prove quality aspects of the simulated democracy in different urban and social settings,
while adding ease and flexibility to the development. Second, we can infer and reason with abstract knowledge,
which is a limitation of the Unity platform; third, our system enables real-time decision-making and adaptation
of the game flow based on the player’s abstract state, paving the road to explainability.
Scalability is achieved by maintaining a dual-layer knowledge representation mechanism for reasoning about the
simulated democracy that functions in a similar way to a two-level cache. The lower layer knows about the current
state of the game by continually processing a high rate of events produced by the in-built physics engine of the
Unity platform, e.g., it knows of the position of a player in space, in terms of his coordinates x,y,z as well as their
choices for each challenge. The higher layer knows of easily-retrievable, user-defined abstract knowledge about
current and historical states, e.g., it knows of the political doctrine of a Smart Spatial Type, a player’s philosophical
doctrine, and the collective philosophical doctrine of a community players with respect to current social issues.
Key-Words: Game-Based Democracy, Smart Spacial Types, Dilemmas, First-order-logic, Knowledge Base
Representation and Reasoning in Games, Smart City States, Immersive Gaming, Smart City Simulation,
Serious Games for Social Challenges, Unity Serious Game.
Received: May 31, 2019. Revised: May 4, 2020. Accepted: May 22, 2020. Published: May 29, 2020
(WSEAS will fill these dates in case of final acceptance, following strictly our data base and possible email
communication)
1
Introduction
This work reflects on the design and implementation
of an in-engine multi-agent simulation in Unity
that interacts with real players, e-polis. e-polis
is a serious game and a methodological tool that
integrates a novel knowledge representation and
reasoning framework in order to investigate a) the
way young people think and act in today’s social
and political arena and b) the way they imagine
the ideal community (polis) 1. In order to draw as
1This serious game and methodological tool is part of a wider research
project called ”e-polis of the future”. The research programme, its
representative conclusions as possible, such a serious
game needs to cater for diversity, equity and inclusion
(DEI), be able to scale out to large segments of the
population and be as engaging as possible, i.e., it
requires an interdisciplinary approach. Furthermore,
as society evolves it needs to be able to adapt
to current affairs. However developing a serious
game that acts as a research tool requires high level
by programming abstractions that are not supported
by modern game development environments, such
as Unity, which is a distributed system where
each game object lifecycle is programmable via C#
scripts that model the object’s monobehaviour
theoretical (sociological, epistemological and architectural) foundation,
the architectural drawings and the game’s implementation with data
extraction were carried out by a research team of the National and
Kapodistrian University of Athens, consisting of: Orestis Didimiotis,
Sara Gogou, Orestis Konstantas, Gerasimos Kouzelis and Despina
Paraskeva-Veloudogianni. The full description of the project has been
published in the volume by the same authors, Representations of
Democracy: Responses from a Youth Survey, Athens 2025, Nissos-EMEA
Publications.

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(Appendix A.4). As a result, the programming
environment has very limited support for global
variables and global classes, which, together with the
inherent lack of persistent storage, is not adequate
for reasoning with abstract global and historical
state. For example, Unity on its own cannot be
programmed in order to evaluate e.g., the consistency
of the players’ philosophical belief over a series of
actions (answering the political dilemmas) or infer the
prevailing philosophical doctrine of all players with
respect to religion.
Furthermore, the following issues further hinder
the development of a serious game:
Content management
•
Creating parametrical Smart City layouts out of
custom building blocks of different sizes and
types. For example, in the scope of this work,
for each combination of urban block area, road
width, density and height distribution of the
buildings, layout components (neighborhoods
of initially empty urban blocks) were created
manually, replicated and puzzled together in
predefined, calculated positions on the main
scene to form a Smart City. Second, for
each social institution of interest, five different
architectural versions were developed in Rhino,
exported as generic objects, imported and
extracted in Unity, edited, cleaned, re-packaged
as Unity components per Spatial Type and
philosophical doctrine.
•
Creating a large number of up-to-date social
and political challenges from custom textual and
multimedia content. For example, in the scope
of this work, a challenge template was created
manually as a UI object containing 5 buttons,
replicated 30 times, populated with images
and text) and placed in predefined, calculated
positions on the challenge scene for the user
to interact with. Other types of multimedia
such as video and animation were desirable. As
social and political issues evolve, new challenges
need to be created, others updated or removed
following the above process.
•
Creating scripts (in C#) for each game object,
i.e., layout components, smart spatial types, UI
buttons and attaching them to each object.
Game logic
•
Creating algorithms for calculating abstract game
state predicates that reflect research goals from
low level events produced by the Unity Physics
Engine. For example, the game needs to be able
to infer when one or all players have responded
to all challenges, which of the challenges they
were most interested it, what is a player’s
philosophical doctrine based on the choices they
provide to challenges, how happy are the players
from the resulting Smart City.
•
Creating algorithms for predicting abstract state
predicates for new or current players based on the
collected data.
Scalability and expressiveness
•
Managing big data generated by multi-player
versions of serious game. A very large number
of events are generated in real-time from tracking
user movements and their interactions with game
objects that are required by the game logic that
need to be processed in real-time and in order
to perform actions and produce the research
outcomes.
•
Exporting the game for accessibility. The
game needs to be made publicly available in
a repository, with all security patches applied,
from where it can be downloaded easily and
installed as an app.
•
Creating an analytical tool for processing user
choices and generating research outcomes.
The results should be properly annotated
and collected in a cloud service where they
can be analysed using advanced data science
techniques.
Tasks above are pedantic, time consuming and
prone to error and raise the need for a model-based
approach that will improve the reliability of the
serious game, while decreasing the burden of the
development. The approach implemented in this
work, decouples the abstract reasoning from the
game mechanics, allowing different implementations
of the same game in different domains, such as
cities with different density and island communities,
supports both current and historic predicates that can
be analysed to determine trends and other statistics,
and enables real-time decision-making based on the
player’s abstract state. In the rest of this paper, the
terms challenge, dilemma and pentalemma are used
as synonyms.
2
Related work
Although few approaches using smart cities in serious
games exist in the literature, their aim has been
to investigate Smart Cities, their aspects and their
problems, [1].
With the advancement of Digitalisation,
participation of citizens in governance started to

3
increasingly become electronically enabled since
the early 1990s, in hopes of facilitating a wider
reach and a better inclusion of marginalized groups
in governance and democratic practices, [2], [3],
[4]. E-participation, [5], [6], can holistically be
considered as technologically mediated interaction
between the government and civil society, [7],
towards the betterment of their community, [8].
Digital games are complex socio-technological
artifacts that are hard to define, [9]. They often
represent artifacts created for entertainment purposes,
where users are faced with artificial challenges,
rules for solving those challenges and outcomes
from engaging with the challenges, [10]. Playing
games has been associated with several cognitive,
emotional, motivational and social benefits, [11].
Hence, games, in a form or another, have been
utilized in policymaking and related activities for
decades so as to harness these benefits of games
in non-game contexts, [12], [13], [14], [15].
Gamification is an umbrella term for the use of video
game elements (rather than full-fledged games) to
improve user experience and user engagement in
non-game services, [16]. The growth of gamification
accompanied the expansion of smart phones and
mobile apps, which allow access to various services
via a digital interface, [17]. Gamification has
proliferated as a tool for democratic participation
– notable examples include participatory budgeting
in Argentina and Canada, [18], urban planning in
Finland, [19], and civic discussion of social issues in
Nigeria, [20]. Scholars of participatory democracy
argue that controlling contingencies through game
mechanics reduces the high levels of user boredom,
frustration, and disengagement while producing more
empowered democratic processes, [18], [21]. Decide
Madrid and vTaiwan integrated game elements into
their interface design to address also online ‘trolling’
(irritating other players), and polarisation caused by
social media, [22]. The policy gaming field, for one
field bridging games and governance, looks into, how
simulation games can assist in policy planning and
better organizational decision-making, [23].
Game-based democracy, utilises certain design
rules to lead and provoke users to become active
and engaged, and to feel enjoyment and fun. ‘Game-
based elements’, that are central to the process of
gamification, [17], [24], generally include two
characteristics: rule-based competition and
incremental rewards building toward a specific goal,
[16], [18], [24]. During a rule-based competition,
users are rewarded with points, badges or other
incentives for completing designated tasks towards a
clearly defined goal, [18], [25].
CityCare utilizes a point system, on top of a
problem reporting system, to encourage citizens to
actively communicate any problems they encounter in
their city to administrators, [26], TAB Sharing, [27],
is such a web-based and mobile platform where
citizens submit proposals and solutions to obtain
points and enter different kinds of leaderboards.
Additional points are received for each vote received
by other users and from positive feedback from
the administration. Medals enable citizens to
change their level while ”distinguished” citizens are
rewarded publicly every year.
Other gamification strategies include creation,
management and use of flashcards, games, quizz
games, maps, resources, quests, challenges,
simulations, presentations, infographics, posters,
catalogs, images, playsheets, etc., [28].
Gamification is often viewed as a form of
control of contingencies, construed in the context of
democracy as challenges, [18], [21]. A good game
design produces an ‘artificial conflict’ in which users
(either individuals or groups) compete against each
other based on predetermined rules, [18].
The elements of feedback, progress tracking, and
an ever-present sense of achievement present in
gamified environments contribute towards immersive
and enjoyable educational experiences, [29].
By design, in vTaiwan’s digital interface creation
of ‘artificial conflicts’, is produced through the
purposeful visual display of (different) Opinion
Groups on the user interface. The interface visualises
how many users support and oppose each Opinion
Group, positioning users in conflicting positions.
The design is supposed to ‘nudge’ participants to
explore and reflect on the opposite group’s opinions
(for each issue under discussion). These methods
are different from a traditional online forum, which
only rewards the most ‘liked’ opinion, [22]. Decide
Madrid’s design interface establishes a set of complex
goal-oriented rules and emphasises ‘competition’
amongst participants over generating a consensus
from e-petitions to a participatory budget process
funded by public revenue, [18].
Geographers refuse the reductionist framing of
videogames as a space where relationships between
users and videogame technologies are predetermined
by game designs. Rather, videogames are understood
to be open-ended environments/spaces of emergence,
unfolding, and becoming as they are experienced
by users, [30], [31], [32]. For [32], designers of
videogames have to manage and anticipate (but not
completely control) Contingencies, i.e., unpredictable
interplays between users and the gamified interface
are managed but not completely controlled as they
generate and encourage creative responses from
players. However, a gaming environment that is too
open might ‘fail to captivate and capture an audience
or community of users at all’, [32], [33].

4
Gamification can be implemented through heavily
relying on the introduction of design features unique
to games (known as game elements) to existing
systems or service, [16], [34].
Gamification implementations can also
extend beyond the digital, employing physical
props/hardware such as game controllers, such as
in, for example, an application intend to assist law
enforcement officers in the regulation of drones, [35].
Gamification can also be implemented through
stories and role-play that immerse players in imagined
realities, such as one where the players are mapping
a city to facilitate the development of accessible
mobility maps while fighting off zombies, [36]. Due
to these differences in its implementations, outcomes
from gamification can vary and hugely depend on the
implemented design and the use context, [37].
Studies of gamification offer one view of digital
democracy, but they tend to paint a rather linear
and techno-solutionist picture. They ‘do not
consider the impacts of various failures, glitches and
contestations in user-interface relationships known as
friction. Emotional predicaments such as frustration,
confusion, or disengagement are an unavoidable part
of gamified participation, and the digital interface
sometimes loses control of participants’ emotions and
behaviour.
Teachers and game designers can fail to establish
differences between Serious Games, Game-Based
Learning or Gamification. As [38], indicates, Serious
Games start from a real problem and include it in a
game to make it more fun and easier to understand.
In Game-Based Learning learners play games (digital
or non-digital) in order to learn contents, [39].
Examples of resources for Game-Based Learning are:
World Peace Game Foundation, World of Warcraft in
School, Minecraft Edu or Portal 2. Gamification is
not a game, but it applies the techniques and elements
of games in order to motivate, encourage changes
in attitudes, improve processes, etc. It is not based
only on rewards, points or rankings, but implies a
pre-action analysis process, [16].
The seamless integration of technologies,
ethical considerations, accessibility, education
for technological literacy, interoperability, user
trust, environmental sustainability, and regulatory
frameworks are becoming significant. These
challenges present opportunities for the future to
enrich human experiences while addressing societal
needs.
In parallel, though, a more disruptive interaction
paradigm is taking place, rooted in immersive
technologies such as augmented, virtual, and mixed
reality (AR, VR, and MR). Apart from gaming
special-purpose applications (e.g., in museums,
science centers, 4D cinemas, etc.), these interaction
paradigms exist in a newborn universe.
The advent of immersive technologies, [40],
[41], AR, [42], VR, [43], and MR, [44], made
possible the transition from traditional screens to
the digital three-dimensional space, supporting both
hybrid and pure digital training experiences. XR
applications in vocational education and training
exemplify the capacity of these technologies to
revolutionize experiential learning, [45], [46], [47].
In such applications, human motion capture, [48],
[49], and 3D digitization, [50], [51], [52], enable
users to interact with digital environments in
ways that mirror real-world movements and real-
world environments, [53], [54], [55]. Such realistic
simulations blur the boundaries between physical
and digital realities, fostering a deeper level of
engagement and personalization in digital
experiences. Immersive technologies can also enable
new forms of social interaction, more realistic and
engaging virtual meetings and conferencing, dynamic
virtual versions of physical places or objects, known
as “digital twins, [56], that can model complex
systems, enable dynamic, real-time design and
collaboration. Immersive games are video games
that transport the player into an alternative world
where techniques are used to make them feel more
like the character they’re playing. As the compute
power of smartphones increases and 5G becomes
prevalent, mobile latency and buffering issues are
diminishing, [57], allowing game studios to create
seamless gaming experiences for users, even as they
transition between consoles, laptops, and mobile
devices. Arm is at the center of immersive gaming
experiences from the cloud to the edge and endpoint
while companies such as Hatch, Google, Epic and
Unity optimize mobile gaming performance on
Arm-based SoCs.
Serious games can be considered as the domain
where user-centric principles and the innovation
described in AR, VR, and MR join forces to re-
purpose gaming and address educational, training,
and societal challenges. Serious games offer
purposeful play that transcends entertainment,
becoming powerful tools for knowledge and skill
acquisition and thus addressing broader societal
issues. Serious games employ XR technologies
and game design principles to address educational,
training, and societal challenges, [58]. These
games are based on a paradigm shift from
entertainment-focused gaming to skill development,
knowledge acquisition, and societal awareness, [59].
At the same time, the integration of artificial
intelligence (AI) into UIs, [60], [61], empowers
systems to understand user intent; process
information efficiently; and offer personalized,
anticipatory digital experiences, [62].

5
Ontologies, linked data, and semantic graphs
provide a rich framework for expressing relationships
between concepts, [63], allowing systems to
infer and connect pieces of data, creating a web
of contextual relevance. Semantic knowledge
representation lays the groundwork for a more
intuitive and context-aware service provision, [64].
NLP algorithms, powered by semantic models,
enable systems to comprehend user queries in a more
human-like manner, [65]. Beyond representation,
the presentation of information is an important
part of user experience. Semantic technologies
should influence how information is visually and
contextually communicated to users, [66], ensuring
that users receive information in a format that
aligns with their cognitive processes and preferences
and has the potential to provide a more coherent
and holistic user experience, reducing information
overload and enhancing overall satisfaction.
3
Background
Human–computer interaction (HCI) and information
and communication technologies (ICT) are
experiencing a transition due to the emergence
of innovative design paradigms that are affecting
the way we interact with digital information.
Immersive technologies, such as augmented reality
(AR), [67], [68], virtual reality (VR), [43], and
mixed reality (MR), [69], are considered the
building blocks of modern digital experiences.
Knowledge representation in a machine-interpretable
format supports reasoning on knowledge sources
to shape intelligent, user-centric information
systems. From a human-centered perspective,
these systems have the potential to dynamically
adapt to the diverse and evolving needs of
users. Simultaneously, the integration of artificial
intelligence (AI) into ICT creates the foundation for
new interaction technologies, information processing,
and information visualization. On the other hand
cities of tomorrow need to adopt a holistic model of
sustainable urban development. The implementation
of a smart city is strongly related to the process of
social (geo)participation—according to PAS 180:
2014 (Publicly Available Specification under the
British Standards Institution), this process means
an “effective integration of physical, digital and
human systems in the built environment to deliver a
sustainable, prosperous and inclusive future for its
citizens.” Also, the new urban agenda stresses the
need to empower all individuals and communities
and to promote and broaden inclusive platforms
that allow full and meaningful participation in the
decision-making and planning process, [70], [71]. In
recent decades, the crisis of participatory democracy
has been particularly severe in urban centers and areas
subject to urbanization. Its outcome is the weakening
of the sense that the residents have a real impact
on co-creating the vision for a city’s development,
revitalization of the neglected districts, or spatial
order. When interpreting the term “the right to the
city”, [72], emphasizes that it is not only the right to
access its resources, but also the right to decide jointly
on the direction of the city’s development. A smart
and sustainable city engages all of its residents in
the most critical decision-making processes, making
the process of creating spatial order more social,
and encouraging the development of participatory
and deliberative democracy. This work discusses
a framework and a novel methodological approach
for game-based democracy in a Smart City State
context that abides by the above principles. The
open problem to address is to examine the way in
which youth, as a critical agent of social change,
conceptualise democracy today, both as a concept
and as a set of political practices, thus anticipating
the future society. To this end, it examines in
combination a) the way young people think and act
in today’s social and political arena and b) the way
they imagine the ideal community (polis). More
specifically, the purpose of this article is to develop
the concept of smart cities’ residents as active urban
sensors represented by player avatars. Consequently,
both city residents and spatial types are considered
elements of a specific geospatial immersive system.
The impact the “human agents” have on spatial
objects in the long-term influences the the spatial
planning of the city, while mutual interactions of the
residents, between the residents, combined with a
visual feedback loop over the resulting ”ideal” city
state, influences further their democratic viewpoint.
Therefore, the goal of the authors is to model complex
social interactions between the urban residents and
the smart City-State and to map the level of young
people’s attitudes towards established institutions,
practices and perceptions, while freeing them to
envision and create the ideal society, the expected
social relations and the governance practices within
it. The conclusions of the research are based on the
analysis of the ”ideal city-states” to be developed
in the game in combination with the findings of the
mapping. Based on this approach, the project further
explores which aspects of youth’s socio-political
daily life prefigure trends of transformation - and
in which direction - how young people tend to
resolve political crises, how they evaluate the
concepts of inclusion and solidarity, how reality
influences fantasy and vice versa, and finally how
the concept of utopia is currently conceptualised.
To this extent, this article introduces a novel game
element: a Smart Spatial Type that changes its
architectural shape according to the political doctrine

6
of the person that interacts with it, while this user
strolls in the Smart City; Physical world Spatial
types, an urban planning term, is where human
activities, the equitable access of citizens to services
and infrastructure and the prudent management
of natural resources and cultural resources are
placed; these are supported by legislative provisions
and the development of a comprehensive set of
policies. The Smart Spatial Type therefore, acts as
a digital interface to physical building blocks, such
as Government buildings, Parks, Schools, Theatres,
Temples, Markets. A socio-political dilemma, linked
to the Smart Spatial type, i.e., a depiction of a ”social
scenario” with five choices each representing a
different philosophical doctrine. The Smart Spatial
Type is transformed to a different architectural form,
as a result of the player’s position in the dilemma.
User positions are recorded in a cloud database for
further analysis. At the end of the game the users
can watch the city they have created from above and
contemplate on how their answers contributed on
reforming their city. A Smart City-State is a smart
city that is structured as a collection of Smart Spatial
Types. It represents a city that can be considered a
smart one when it, in parallel, invests in technology
and human capital to actively promote sustainable
economic development and high quality of life
through civic participation. The e-polis concept is
analysed in detail in the next section.
Figure 1: Smart Spatial Type concept
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Concept
In this work we introduce the concept of a
SmartSpatialType that has a state and two active
zones and uses the following modus operandi: The
outer zone includes approximately the whole building
block while the inner zone contains approximately the
building infrastructure. Initially, the state is equal
to disabled, i.e., the building block appears to be
empty. The first time the user enters the outer zone, a
default instance of the SmartSpatialType is enabled,
i.e., appears suddenly. If the users continues their
approach, once they have entered the inner active
zone, they get transported to a linked location where
they are faced with a social five-choice dilemma. In
order to continue the game they have to select at
least one of the available choices and then press the
Continue button. The choice, which is logged both in
the cloud database and persistently within the game
Player Preferences, causes the SmartSpatialType to
change to a predefined shape that is linked with the
UserChoice. Last, the user is transported to the
original location to inspect the result and continue the
game. This sequence of user-building interactions is
described schematically in Figure 1.
5
Novelty
Our modelling approach in this work leverages
a dual-layer knowledge representation architecture
for Sentient computing, developed in previous
work, [73]. Sentient computing, [74], is the
proposition that applications can be made more
responsive and useful by observing and reacting
to the physical world. The dual-layer knowledge
representation architecture makes it possible for
the user to perform easily complex computations
involving spatial and temporal notions of a dynamic
changing environment. This work builds on
the previous system, where Sentient Computing
predicates represent location-awareness, extending
it with first order logic predicates that represent
knowledge about the simulated democracy, such
as when one or all players have responded to all
challenges, which of the challenges they were most
interested it, what is a player’s philosophical doctrine
based on the choices they provide to challenges, how
happy are the players from the resulting Smart City.
More specifically, the Deductive Abstract Layer
in this work has abstract knowledge about the
player’s preferred political position in specific socio-
political dilemmas. It also has knowledge on user
intent, i.e., whether the player passes by a Smart
Spatial Type, or visits it. Using AI on historical
knowledge predicates, the overall political position of
a player can be inferred, including similarities among
players creating opinion groups such as the ones in
the vTaiwan. Furthermore, the popularity of a Smart
Spatial Type can be inferred, both for a specific user,
and for all users.
6
System architecture
In this work, Smart Spatial Types were modeled
as urban sensors (Figure 2). Smart Spatial Types
periodically generate events, that are asserted in the
Sensor Abstract layer (SAL) as low-level predicate
instances that represent low-level knowledge of

7
Figure 2: System reference architecture
•
player collision with one of the two boundaries
of the Smart Spatial type either at the outer
boundary (the outdoor area) or the inner
boundary (the indoor area) where the challenge
is triggered, at the current instant
•
player interaction with a UI button that provides
an answer to a given challenge, associated with
the Smart Spatial Type that the player has
entered.
•
player voting on the current Smart City state at
the end of the session
A new predicate instance is created every-time
a player enters a Smart Spatial Type, triggers a
challenge, provides a choice or a vote. For each Smart
Spatial Type, SAL knows of all recent collisions of
a player with each boundary as well as all given
choices to a challenge. Every time an assert action
is performed the SAL layer matches consecutive
predicate instances against patterns that indicate
abnormal behaviour. For example, random random
movement, long periods of inactiveness, repeated
re-entering of the Spatial Type, repeated choices
for the same challenge, that may indicate a special
interest on the player’s side for the specific building
or associated challenge or give other insights.
With respect to the Deductive Abstract Layer
(DAL), users register recurring query specifications
that represent high-level knowledge about the
outcomes of the players interactions with the
Smart Spatial Types. Each query is structured as a
Rete network described in Section 7. It monitors
continually low-level predicates residing in SAL
causing new instances of the DAL predicates to
be asserted, using a callback mechanism. DAL
predicates are available for further querying by
applications and they can be composed into recurring
or single queries to meet application requirements.
For example, for a given Smart Spatial Type, the
DAL layer knows the philosophical doctrine of each
choice provided by each player for each challenge,
the most popular philosophical doctrine for each
challenge and for all the instances of the Smart Spatial
Type. Accross Smart Spatial types, for a given
player, the DAL layer knows of the most frequent
(predominant) philosophical position for this player,
whether a choice diverges from it and it can predict
upcoming choices, which can give useful insights in
the research. Last, for all players, it knows of the
prevailing philosophical doctrine of their choices as
well as the prevailing opinion on the state of the Smart
City an any time.
With respect to the Unity platform 2 it
complements the knowledge base to which it is
linked, by offering two services:
•
It generates low level events using the Unity
event system and the UI event system and asserts
them into the SAL layer.
•
It adapts the flow of the game to the context
of the player. By listening for callbacks from
the knowledge base and responding with an
appropriate action, it is able for example, to
transport the player to the top of the main scene
to see the city from above or transport them to
a location i in the game where they can vote on
an particular challenge for example if additional
information is required.
7
Technical description
The inference is based on the Rete algorithm, a very
efficient algorithm for matching facts against patterns
in rules. It is implemented by building a network of
nodes. It is designed in such a way that it saves the
state of the matching process from cycle to cycle and
re-computes changes only for the modified facts. The
state of the matching process is updated only as facts
are added and removed and the fewer they are the
faster the matching will be. Each rule has an inference
cycle consisting of three phases: match, select and
execute. In the matching phase, the conditions of
the rule are matched against the facts to determine
which rules are to be executed. The rules whose
conditions are met are stored in a list called agenda
for firing. Form the list of rules, one of the rules is
selected to execute or fire, according to a selection
strategy, e.g., based on priority, recency of usage,
specificity of the rule or other criteria. The rule
selected from the list is executed by carrying out
the actions in the right hand side of the rule. The
action may be an assertion, executing a user-defined
or built-in function. The main advantage of Rete is
speed as it takes the advantage of structural similarity
in rules. On the other hand, its main drawback is

8
−
that it is memory intensive as saving pattern matches
and partial matches requires considerable amounts of
memory.
A Rete network is a direct acyclic graph that
consists of nodes representing patterns in the
conditions of the rules. The nodes behave like filters,
testing the incoming tokens and sending on only
those that pass the test. The Rete network consists of
two parts: alpha network, consisting of nodes known
as alpha nodes and beta network, consisting of beta
nodes. Alpha nodes have one input and evaluate
the conditions of the rule where beta nodes have
two-inputs and define inter-element (fact) conditions.
Each alpha node is associated with an alpha memory
that stores the matching facts. Alpha nodes are then
joined by beta nodes that only have two inputs thus
supporting partial matching. Each beta node has a
memory to store joined patterns. Assertion of a fact
creates a token that initially enters the root node.
The network then splits a branch for each token type.
Each kind node gets a copy of the token and preforms
a SELECT operation to select tokens of its kind. It
then passes a copy of the token to the alpha nodes. On
receiving the token the alpha nodes do a PROJECT
operation and and extract components from the
token that match the variables of the pattern, thus
evaluating the pattern. Beta nodes then determine the
possible cross-products of the rule. Finally, actions
in the rule will be executed.
A custom embedded middleware component
running on the player device where the serious
game is installed, links the system components
together using a text-based interface which
involves exchanging text via a shared file space,
PlayerPreferences. Unity writes the events on the
shared space making a callback to the knowledge
base that converts them to the correct format in order
to assert them in the knowledge-base. Vice versa, the
knowledge base exports predicate instances in the
form of C# boolean variables that are picked up by
Unity scripts causing the desirable action.
The clipspy bindings, a “pythonic” thin layer built
on top of the CLIPS native C APIs was used to
analyse some of the collected data providing tools
such as plots and clustering. Furthermore, Real-time
game data is collected in the Firebase noSQL cloud
database. The data can be accessed through the
Firebase console, at the FirebaseEPolis project. To
interface Firebase with the game, the Firebase Unity
SDK was integrated into the e-polis bundle. The
SDK supports both a desktop workflow and mobile
workflows in Android and IOS.
8
Knowledge Representation
The basic element of the model is the
SmartSpatialType, that comprises of:
•
a SpatialType.
•
a 2-order CompositeLocation.
•
a set of n (Politicised)SpatialTypes, each are
located at the CompositeLocation and that only
one instance is enabled at each time.
•
An AtomicLocation that is not associated with
the Composite location.
•
A Dilemma that is located at the above
AtomicLocation.
An AtomicLocation is a polyhedral region
that has a centre and a polyhedron. A
SphericalAtomicLocation is a version that has
a spherical region instead of a polyhedron. A
CompositeLocation is a set of two or more nested
AtomicLocations that have the same centre and
different polyhedrons, such that one contains the next,
and so on. A 2-order CompositeLocation contains
2 nested polyhedrons. A SpatialType represents
a type of public space with infrastructure and a
function, e.g., a factory spatial type. A SpatialType
instance, e.g., the Cadbury’s factory in Birmingham,
has a state that can be either enabled or disabled.
A ( Politicised)SpatialType is a spatial type that is
influenced by a given philosophical dogma. For
example a Monoblock type of appartment building
is a politicised specialisation of the SpatialType
AppartmentBuilding associated with Communism.
A PolyhedralSpatialType instance is a 3D object
were the x-z sides are significantly larger than any
x-y or z-y side. That is the height is significantly
smaller than the width and the length making it a flat
3D object. It is used to model public locations such
as roads that do not belong to any SpatialType. A
PoliticalType represents a philosophical doctrine such
as Conservatism. A Dilemma is an AtomicLocation
where a UserChoice predicate instance can be
asserted. A UserChoice represents the user’s
preferred philosophical position, e.g., CL, on a social
issue at hand, represented by the dilemma, e.g., a
forgiving a theft in the marketplace as inevitable due
to social injustice. UserChoice is also associated
with a SpatialType instance. Given a UserChoice
as above, the associated SpatialType instance that
corresponds to the User’s preferred theory is enabled
and the previous instance is disabled. This has the
effect that the user can see the impact of their choices
on the city.
A PoliticisedSpatialType is essentially a
specialisation of aSpatialType with an extra attribute
that corresponds to a PoliticalType. The size of a
SmartSpatialType is equivalent to the x z area of
the smallest polyhedral of the CompositeLocation it
contains. There are three sizes of SmartSpatialTypes:

9
Single, Double, Quadraple. A RoadBlock is a
SpatialType that comprises of:
•
a 2-order CompositeLocation.
•
an instance of a PolyhedralSpatialType.
The RoadBlock can be further gamified , leveraging
the CompositeLocation element, to by enable it
only as user approaches it, creating the illusion of
appearance.
An e-polis is a SmartCity that is structured as
a collection of SmartSpatialTypes, RoadBlocks and
a layout. A layout is a map of the centres of the
CompositeLocations of the SmartSpatialTypes and
the Dilemmas. The dimensions (size) of the Single
SmartSpatialType can be parameterised initially to
create different building density and shape of the
Smart City.
9
Formal specification
The key logical predicates are shown in this Section.
9.1
Sensor Abstract Layer
( L_ Atomic Location ( u i d ? u s e r _ i d )
( x ? x ) ( y ? y ) ( z ? z ) )
( L_ Atomic Location ( r i d ? r e g i o n _ i d )
( s i d ? s i d ) ( p o l y h e d r o n ) )
( L _ N e s t e d L o c a t i o n
( n r i d ? n e s t e d _ r e g i o n _ i d )
( p r i d ? p a r e n t _ r i d ) ( s i d ? s i d )
( p o l y h e d r o n ) )
( L _ S p a t i a l T y p e ( s i d ? s p a t i a l _ i d ) )
( t y p e ? t y p e ) )
( L _ P o l i t i c i s e d S p a t i a l T y p e ( s i d ? s i d )
( s i d ? s p a t i a l _ i d ) ( p i d ? dogma_ id ) )
9.2
Deductive Abstract Layer
( H_ User At Nested Location ( u i d ? u i d )
( r i d ? r i d ) ( s t a r t _ t i m e ? t i m e _ v a l u e ) )
( H_ User At Smart Spatial Type ( u i d ? u i d )
( s i d ? s i d ) ( s t a r t _ t i m e ? t i m e _ v a l u e ) )
( H_ User In Dilema ( u i d ? u i d )
( d i d ? dilemma_ id )
( s t a r t _ t i m e ? t i m e _ v a l u e ) )
( H_UserAnsweredDilemma ( u i d ? u i d )
( d i d ? d i d ) ( c h o i c e ? c h o i c e _ i d )
( s t a r t _ t i m e ? t i m e _ v a l u e ) )
( H _ S m a r t S p a t i a l T y p e T r a n s f o r m e d
( s i d ? s i d ) ( p i d ? dogma_ id
( s t a r t _ t i m e ? t i m e _ v a l u e ) )
( L_Dilemma ( d i d ? dilemma_ id )
( s i d ? s p a t i a l _ i d ) )
9.3
Historical predicates
( H _ U s e r I n L o c a t i o n H i s t ( u i d ? u i d )
( s i i d ? s i i d ) ( s i d ? s i d )
( s t a r t _ t i m e ? t i m e _ v a l u e )
( end_ t ime ? t i m e _ v a l u e ) )
( H_ User In Dilema Hist ( u i d ? u i d )
( d i d ? d i d ) ( s t a r t _ t i m e ? t i m e _ v a l u e )
( end_ t ime ? t i m e _ v a l u e ) )
( H_ User Choice Hist
( u i d ? u i d ) ( d i d ? d i d )
( c h o i c e ? c h o i c e _ v a l u e )
( s t a r t _ t i m e ? t i m e _ v a l u e )
( L_ User Choice ( c i d ? c h o i c e _ i d )
( u i d ? u s e r _ i d ) ( d i d ? dilemma_ id )
( s i d ? s p a t i a l _ i d ) )
( L _ P o l i t i c a l D o g m a ( p i d ? dogma_ id )
( name ? name ) )
( L _ P o l i t i c i s e d U s e r C h o i c e
( c i d ? c h o i c e _ i d )
( p i d ? dogma_ id ) )
10
Example
The prevailing philosophical doctrine for a challenge
is the most frequent ”political flavor” of the players’
choices for that challenge. The following CLIPS
code illustrates how the prevailing philosophical
doctrine for the challenge it can be computed by the
DAL layer. Assuming that the following predicate
instances are asserted in DAL as a result of the choices
of different users for a given challenge (did 1), the
most prevailing philosophical doctrine is democratic
realism that is represented by the two instances of
choice 1.
( H_UserAnsweredDilemma ( u i d 1 ) ( d i d 1 )
( c h o i c e 1 ) ( s t a r t − t ime 1000 )
( H_UserAnsweredDilemma ( u i d 2 ) ( d i d 1 )
( c h o i c e 1 ) ( s t a r t − t ime 1020 )
( H_UserAnsweredDilemma ( u i d 3 ) ( d i d 1 )
( c h o i c e 3 ) ( s t a r t − t ime 1080 )
( H_UserAnsweredDilemma ( u i d 4 ) ( d i d 1 )
( c h o i c e 2 ) ( s t a r t − t ime 1100 )
( H_UserAnsweredDilemma ( u i d 5 ) ( d i d 1 )
( c h o i c e 4 ) ( s t a r t − t ime 1200 )
( L _ P o l i t i c i s e d U s e r C h o i c e ( ( c h o i c e 1 )
( p i d 1 ) )
( L _ P o l i t i c a l D o g m a ( p i d 1 )
( name d e m o c r a t i c r e a l i s m )
( L _ P o l i t i c i s e d U s e r C h o i c e ( c h o i c e 2 )
( p i d 2 )
( L _ P o l i t i c a l D o g m a ( p i d 2 )
( name c r i t i c a l r a d i c a l i s m )
( L _ P o l i t i c i s e d U s e r C h o i c e ( c h o i c e 3 )
( p i d 3 )
( L _ P o l i t i c a l D o g m a ( p i d 3 )

10
( name a p o l i t i c i s m )
( L _ P o l i t i c i s e d U s e r C h o i c e ( ( c h o i c e 4 )
( p i d 4 ) )
( L _ P o l i t i c a l D o g m a ( p i d 4 )
( name c o n s e r v a t i s m )
In order to calculate the most prevailing choice the
following program first counts the instances of each
type of choice and stores the count in instances of the
H_SameChoiceDifferentPlayer predicate. Next, the
instances are compared with respect to to the count
slot in order to find the dogma that corresponds to the
max count.
( d e f r u l e p r e v a i l i n g − c h o i c e
( H_UserAnsweredDilemma ( u i d ? u i d 1 )
( d i d ? d i d 1 ) ( c h o i c e ? c i d 1 )
( s t a r t − t ime ? s t 1 ) )
( H_UserAnsweredDilemma ( u i d ? u i d 2 )
( d i d ? d i d 2 ) ( c h o i c e ? c i d 2 )
( s t a r t − t ime ? s t 2 ) )
(= ? c i d 1 ? c i d 2 )
=> modify
( H _ S a m e C h o i c e D i f f e r e n t P l a y e r
( d i d ? d i d ) ( c h o i c e ? c i d 1 )
(+ ? c o u n t 1 ) ( s t a r t − t ime ? s t 1 ) )
( d e f r u l e f i n d −max− v a l u e
( H _ S a m e C h o i c e D i f f e r e n t P l a y e r
( d i d ? d i d ) ( c h o i c e ? c i d 1 )
( c o u n t ? c o u n t 1 )
( s t a r t − t ime ? s t 1 ) )
( not
( H _ S a m e C h o i c e D i f f e r e n t P l a y e r
( c o u n t ? c o u n t 2 &:
( > ? c o u n t 2 ? c o u n t 1 ) ) ) )
=>
( a s s e r t ( H _ P r e v a i l i n g C h o i c e
( d i d ? d i d ) ( c h o i c e ? c i d )
( s t a r t − t ime ? s t 1 ) )
( p r i n t o u t t ” Choice ” ? c i d
” i s
␣
t h e ␣maximum” c r l f ) )
11
Prototype Implementation
This section discusses two different prototype
implementations using the knowledge-base
framework. The final version of the game is multi-
player and includes 20 5-choice dilemmas which
have been placed in the 20 empty (unbuilt) blocks,
while in the remaining blocks that are not active, a
selected PoliticisedSpatialTypeInstance is
permanently visible as shown in Figure 3. The
spatial types implemented are shown in Equation
(1). To illustrate the gameplay with an example,
let us assume that the player approaches enough
the Factory 1 empty block: an apolitical version
Figure 3: Main Scene
of a factory pops up as shown in Figure 4(a); as
the player approaches more, a dilemma is triggered
depicting a strike of factory workers with the options:
”Without you, the Earth won’t rotate!”, ”Strikes are
so last year!”, ”Damn, there is no public transport; I
will be late for work”, ”Amidst the economic crisis
some people just keep asking for more” and ”These
trade unionists suck!”(Figure 5). Assuming the user
selects the last option the spatial type is transformed
to the one shown in Figure 4(b). After the player has
answered all 20 dilemmas, they can see the city-state
from above (Figure 6) and vote on the final outcome.
The second prototype is in an island and contains
three challenges, associated with an elementary
school, a cultural centre and an orthodox church.
Integrating the knowledge base improved the time
needed to develop the business logic as the same
CLIPS programs in the SAL and DAL layers were
used and the Unity scripts associated with each game
objects in the first prototype were replicated with
small changes in the naming scheme and linked to
the knowledge base. However, as the island terrain
was significantly different to the urban one, a large
number of the layout components were developed
from scratch. One approach to address this issue
trade-off between realistic depiction and usability is
to use abstract layout components that can be reused
in different settings.
12
Research results
The research evaluation of the game included two
phases: the pilot and the main phase. The
pilot implementation was carried out on a sample
of eighteen (18) students of the Department of
Sociology of the University of Athens. After
taking the feedback into account, it was decided
to reduce the number of episodes to twenty-nine
(29) and the spatial types to fourteen (14), as well
as to rewrite the descriptions and the proposals
of the available options-reactions. One hundred
and twenty-eight (128) people participated in the

11
(a)
A-political Factory instance.
(b)
Conservative Factory instance.
Figure 4: Factory instances
Figure 5: Factory Dilemma 1
main phase of the survey: students of higher
education institutions, students of general high
school and vocational high schools, students of
technical schools. The sample consists of 50.4%
men, 41.6% women, 4.8% people who identify
themselves as ”other” and 3.2% people who do
not wish to identify themselves in terms of gender
identity. It should be noted that the interpretation
and analysis of the reaction-response was carried
out on the basis of an initial approximate typology
that distinguished between logothetic formations that
reflect political-social attitudes. In the episodes
Figure 6: City-state from above
with the five possible reaction-response formations,
these formations are: Democratic Radicalism (DR),
Liberalism (F), Apoliticalism (A), Conservatism (C),
and Authoritarianism (AUT). In the episodes with
the six possible reaction-response, the formations
are: Humanism, Communitarianism, Meritocracy,
Technocracy, Realism, Cultural Reductionism and
Authoritarianism (Autarchism). Following are some
key points from the analyses of the data for issues that
were in the public eye at the time of writing the paper.
12.1
Prevailing reaction-responce
The vast majority of responses belong to the
Democratic Radicalism and Liberalism formations.
More specifically, with respect to sex and sexuality:
about 49% of the sample, agree with marriage
between same-sex couples, 48.4% welcomes the
”immediate reaction” of female students following
a complaint of sexual harassment, 88.3%, has a
positive attitude about public breastfeeding while
84.3%, exhibit progressive, feminist reflexes in
relation to a disclosure of a woman’s decision to
terminate a pregnancy. With respect to religion: 54%
argue that it is the right of each pupil to do as he
or she wishes about the morning prayer at school
(F), 38.1% reacts to the priest’s unscientific sermon
by demanding an effective separation of church and
state(F) while 25.4% support the right of everyone to
say what they want (A). 49.2% attribute the motives
of an attack on a church to fanaticism, which they
denounce ”wherever it comes from” (F). With respect

12
to policing and state violence: 53.3% disagree with
the presence of police in universities(PP), 38.3%
show empathy and understanding in the face of
pursuing a person who has committed petty theft
in the market (PP), 48.8% express anger at the
repeated incidents of police violence. Faced with the
issue of being checked when entering the park (PP)
32.8% resent it (DP) while 37.5% want discretionary
security (F). In relation to rights in public spaces:
54.7% defend the right of all to coexist in public
space (PP), 41.7% prioritise the protection of
open public spaces (PP), 29.9% recognise both
the importance of supporting business (e.g., the
extension of table seating for a private catering
business in the square) and the need for children to
have space to play (F),68% of the sample supports
the right of owners and pets to play freely (PP).
Regarding social demands, and more specifically
the gathering of leftist and feminist organizations
to protest in the square, 47.2% accept the right of
citizens to protest (F). 47.2% accept the right of
citizens to protest (F), as for the factory strike, 44%
consider it obsolete, supporting the need for new
forms of struggle (F), 33.6% support the squat at the
university and the struggles of the students (PP) while
29.6% do not agree with the squat but understand
the reasons for the protest (F). Regarding migration:
when viewing the image of a street sponger in the
market 63% of the sample stresses the importance
of social and economic integration of marginalized
groups (DP). When facing the image of a queue
of people waiting to apply for asylum, 29.9% of
the sample supports the ideal of harmonious and
equal multicultural coexistence (communitarianism)
while 28.3% focus on the fact that migrants and
refugees are tortured people (humanitarianism).
Similarly, 62.5% of participants feel regret about the
exclusion of refugee children at school (humanism).
Ragarding conflicts: in relation to youth violence,
35.2% of the sample prioritises the cultivation of
empathy at school (humanism) while 34.4% pin their
hopes for dealing with the phenomenon on school
counsellors (technocracy). In the face of reactions
outside the theatre over the ”blasphemous” theme
of the show, 38.1% defend tolerance of freedom
of artistic expression (F) while 27.8% oppose
censorship, considering such reactions regressive
(PP). Regarding economy: Faced with a company’s
decision to staff the management with well-paid
managers from abroad at the expense of staff salaries,
28.6% of players react by expressing solidarity
with the workers (communitarianism) while 27.8%
agree with the view that everyone should be paid
according to their contribution (meritocracy).
Regarding the eviction of small business owners,
42.1% worry about the future of people losing their
jobs (humanitarianism). Regarding Environment
and Culture: In the episode of environmentalists
protesting outside a factory that pollutes the
environment, 33.1% of participants put forward
community responsibility (of each neighbourhood)
(communitarianism) while 24.4% support the green
transition of the economy (meritocracy). Faced with
the issue of archaeological finds during excavations
raise the need to stop redevelopment plans, 31.2%
support the need for public consultation (PP) while
29.6% point to the multiple benefits to the country
from the enhancement of cultural heritage (F).
12.2
Clustering
During a second experiment, player responce-
reaction was first explored with histograms e.g.,
(Figure 7) and spider plots e.g., (Figure 8); next,
data was clustered using the k-means clustering
algorithm e.g., (Figure 9). First, Principal Component
Analysis (PCA) was used for dimentionality
reduction, i.e., in order to identify the minimum
number of components required to explain the
majority of the variance (97%) in the data, which
returned a silhouette score of 0.4165, which
corresponds to 9 clusters. K-Means, was employed
next to identify patterns on the PCA-transformed
data. Each data point is assigned to a cluster,
providing insights into group behaviors within the
data as shown in Figure 9.
To cross-check the results, the data was also
processed by the Uniform Manifold Approximation
and Projection (UMAP), a non-linear dimensionality
reduction technique, which, compared to PCA,
often provides more intuitive visualizations,
particularly when dealing with highly complex
datasets. K-Means, was employed next to identify
patterns on the UMAP-transformed data as shown in
Figure 10. To compute the number of clusters, first
the dataset was reduced to three dimensions using
UMAP and next both the elbow and silluette were
calculated resulting in 10 clusters.
12.3
AI
A third experiment explored an AI solution capable
of classifying data to predict the gender of each
participant based on their answers. To validate
our analysis, we used lazypredict, a Python library
designed to simplify the process of model selection
by training and evaluating multiple machine learning
models with minimal setup. It provides a quick
baseline for comparison and insights into model
performance both for the initial dataset and the
normalized PCA/UMAP vectors. More specifically,
we defined three classes for prediction: man, woman,
and other and reduced the dimentionality to three
dimensions. We relabeled the data classes and

13
Figure 7: Factory challenge response-reaction
histogram.
Figure 9: e-polis response-reaction k-means clusters
(PCA)
Figure 8: Factory challenge response-reaction spider
plot
adjusted their frequencies using np.unique values
and then executed our AI system simulation across
various models. The results and accuracy of
our AI system are displayed in Table 1 achieving
approximately 70% accuracy.
13
Towards Big Data
In order to be able to test our research hypotheses
using a large number of players we created e-polis
sim, a simulation model of e-polis that belongs to
the category of multi-agent models. The Netlogo
Figure 10: e-polis response-reaction k-means clusters
(UMAP)
program was chosen for the simulation as it has
a simple and flexible programming language, easy
syntax and the ability to create commands from the
user. The model is built through fixed elements
”patches” and mobile ”turtles” the number of which
is unlimited. Both mobile and fixed elements are
graphically displayed on the program desktop. The
user has the ability to interact with the model directly
from the control center as well as with buttons and

14
Model Accuracy Balanced Accuracy ROC AUC F1 Score Time Taken
RandomForestClassifier
0.78
0.58
None
0.69
0.01
BernoulliNB
0.75
0.55
None
0.73
0.01
SVC
0.72
0.53
None
0.69
0.01
ExtraTreesClassifier
0.72
0.53
None
0.69
0.05
GaussianNB
0.72
0.52
None
0.7
0.01
PassiveAggressiveClassifier
0.69
0.51
None
0.66
0.01
XGBClassifier
0.69
0.51
None
0.67
0.11
ExtraTreeClassifier
0.66
0.49
None
0.64
0.01
Perceptron
0.66
0.48
None
0.63
0.01
RidgeClassifierCV
0.62
0.46
None
0.6
0.01
Table 1: Evaluation of AI model predicting the gender of e-polis participants
scroll bars during iterations.
Agents are independent entities with different
characteristics that represent the members of a
community roaming in the Smart City. Agents move
freely in space in random directions and interact with
each other and with Smart Spatial Types. It was
assumed that when an agent of a given philosophical
doctrine meets a Smart Spatial Type it ”infects” it by
causing it to change colour.
Breeds, i.e., classes of turtles (of different
philosophical doctrine as well as classes of Smart
Spatial Types (e.g., factories) were created in the
simulation and represented visually using shapes.
The game is parameterised with respect to the inputs:
•
the number and distribution of political breeds of
agents, the number and distribution of simulated
institutions (Smart Spatial Types) and the number
and distribution of challenges
•
the pattern of agent movement that ranges from
random walk to pedestrian simulation
•
the ”dice”, i.e., the probability with which an
agent of breed Liberalism (F) causes a Smart
Spatial Type to change colour to yellow (the
colour representing Liberalism in the simulation)
after approaching it.
The game has the following outputs:
•
the state to which the smart city converges after
a sufficient number of iterations; it shows the
impact of the agents philosophical belief on the
Smart City
•
the distribution of politicised Smart Spatial Types
•
the descriptive statistics of the random function
of the simulation
•
the distribution of the ”happiness” function of the
agents.
The desired number of instances of these classes were
created programmatically at the setup phase of each
experiment and they populate the ”scene” according
to some initialisation scenarios. For example, in one
scenario, their initial positions are at the bottom left
of the scene, while in another they start moving from
initial locations that are scattered in the city.
In one version of e-polis-sim, in addition to
ordinary residents, there are also so-called leaders:
Social activists who want to influence society so that
many citizens gain influence in shaping the urban
fabric through mutual interactions. Positive leaders
are those with extreme and positive trust in people,
they have a beneficial influence on social trust in
others. Negative leaders show extreme and negative
trust in people, thus reducing the trust of the people
with whom they interact. Furthermore, there is
the assumption that the mere presence of a citizen
in a given neighbourhood or building affects their
trust in other people. Being in an office, healthcare
facility, workplace, school, or industrial or office
area reduces trust (temporarily or permanently). On
the other hand, being in a park, museum, highways,
or in a historical or recreational area increases trust
(temporarily or permanently). These changes are
called location influence. The simulations carried
out aim to determine how the long-term change in
people’s trust (mutual and towards institutions) over
time affects the level of social participation and,
indirectly, the development of open civil society and
deliberative democracy.
14
Conclusions and future work
Serious game development requires a multitude of
skills both in the fields of creativity and IT; in one
word, an interdisciplinary approach. At the front
end and, in depth knowledge of concept modelling,
UI design, animation, 3D modelling; at the back
end, event-based programming, AI, databases, cloud

15
computing, web services and desktop and mobile
computing workflows are essential elements required
to build serious games. Furthermore, although
tools such as Unity and Unreal, [75], platforms are
available, they require a steep learning process while
end products take up substantial computing resources
such as RAM and CPU acceleration, requiring
updated operating systems and advanced security.
Lack of standardisation in this area forces researchers
to develop games from scratch instead of reusing
existing components.
This work tackles the issue of scalable, system-
level, computationally-efficient abstract modeling of
the simulated physical world in the context of the e-
polis serious game. Its contributions are a formal
definition of a knowledge representation schema for
Smart City-States as well as a mechanism for
reasoning with such knowledge using logical
deduction that combines expressiveness, scalability
and performance, by leveraging the dynamic
knowledge-base maintenance system introduced
in [73]. The deductive layer of this system stores
abstract knowledge on players’ preferred political
position in specific socio-political dilemmas, as well
as on the level of interest of players. for smart spatial
types. Furthermore, historical predicates of such
knowledge mined with AI techniques can generate
knowledge on the overall political position of a user,
as well as a characterisation of the most prevailing
political flavour of significant Smart Spatial Types.
In this view, player location, duration of visit,
political choice act as digital biomarkers for both
players and Spatial Types.
Furthermore, we have created the e-polis Game
that comprises the following game assets:
•
a library of 3D Smart Spatial Types, that
represent selected institutions, namely: Agora
(Market), Archaeological site, Cinema, Factory,
Office building, Government building, Police
station, Park, School, Theatre, Temple,
University, Urban void.
•
Five political categories of content
corresponding to the philosophical doctrines:
Apoliticism, Democratic Realism, Critical
Radicalism, Conservatism and Autocracy.
•
a set of Dilemmas, namely: travelling
salesman, small businesses, theft, dispute,
play, strike, ecology, gay wedding, leave
to remain, immigrants, pets, surveillance, hook-
up, violence, prayer, bullying, censorship,
demonstration, abortion, preaching, squatting,
asylum, abuse of power.
•
a customisable blueprint of a smart city-state.
•
a customisable blueprint of a socio-political
dilemma.
•
an open-source implementation for the Unity
Game. Engine.
In terms of future work, we are working towards
integrating more AI analytics over the collected data
in order to derive richer knowledge such as the
clustering of the users to opinion groups. We have
also developed a agent-based approach of e-polis
which is the subject of a forthcoming publication. An
interesting research direction is also the integration of
machine-readable semantically annotated politicised
material, both 3D Spatial Types and statements. Such
material is available through research teams that work
on IR techniques such as Sentiment Analysis and used
to keep the dilemmas up to date with most current
affairs, [76]. Although this approach is focused in the
requirements of the e-polis Game, it can be used as a
base for serious game development in the context of
Smart City States in general.
Acknowledgments
This work was funded in part by the research
project “e-polis of the future”, supported by the
Hellenic Foundation of Research and Innovation
(H.F.R.I.) in the context of the “1st Call for
H.F.R.I. (http://www.elidek.gr) Research Projects to
Support Faculty Members & Researchers and Procure
High-Value Research Equipment” (Project Number:
2617).
References:
[1]
Olszewski R., Palka P., Turek A., Kietlinska B.,
Platkowski T., Borkowski M., Spatiotemporal
Modeling of the Smart City Residents’
Activity with Multi-Agent Systems, Applied
Sciences 9(10), 2019, pp.2059.
[2]
Lee-Geiller, S. and Lee, T., Using government
websites to enhance democratic E-governance:
A conceptual model for evaluation, Government
Information Quarterly 36(2), 2019, pp.208-225.
[3]
Macintosh, A., Characterizing E-participation in
policy-making, Proceedings of the 37th Hawaii
international conference on system sciences
(HICSS-37), 2004, pp. 117–126, IEEE.
[4]
Supendi, K. and Prihatmanto, A. S., Design
and implementation of the assessment of public
officers web base with gamification method,
Proceedings of the 2015 4th international
conference on interactive digital media (ICIDM
2015), 2015 pp. 1–6, IEEE.

16
[5]
Bingham, L. B., Nabatchi, T., and O’Leary, R.,
The new governance: Practices and processes
for stakeholder and citizen participation in the
work of government. Public Administration
Review 65(5), 2005, pp.547–558.
[6]
Gurstein, M., Effective use: A community
informatics strategy beyond the digital divide.
First Monday 8(12), 2003.
[7]
S?bo, O., Rose, J., and Skiftenes Flak, L.,
The shape of eParticipation: Characterizing an
emerging research area, Government Information
Quarterly 25(3), 2008, pp.400–428.
[8]
Islam, M., S., Towards a sustainable e-
participation implementation model, European
Journal of EPractice 5(10), 2008, pp.1–12.
[9]
Stenros, J., The game definition game: A review,
Games and Culture 12(6), 2017, Springer.
[10]
Salen, K., Tekinbas, K. S., and Zimmerman, E.,
Rules of play: Game design fundamentals., 2004,
MIT press.
[11]
Granic, I., Lobel, A., and Engels, R. C. .
The benefits of playing video games, American
Psychologist 69(1), 2014, pp.66–78.
[12]
Duke, R. D., Gaming: An emergent discipline,
Simulation & Gaming 26(4), 1995, pp.426–439.
[13]
Duke, R. D., A personal perspective on
the evolution of gaming, Simulation &
Gaming 31(1), 2000, pp.79–85.
[14]
Duke, R. D., Origin and evolution of policy
simulation: A personal journey, Simulation &
Gaming 42(3), 2011, pp.342–358.
[15]
Mayer, I. S. The gaming of policy and the
politics of gaming: A review, Simulation &
Gaming 40(6),2009,pp.825–862.
[16]
Deterding, S., Dixon, D., Khaled, R.
and Lennart, N., From game design
elements to gamefulness: defining
”gamification”,Proceedings of the 15th
International Academic MindTrek Conference:
Envisioning Future Media Environments
(MindTrek ’11), 2011, pp. 9–15, ACM.
[17]
Morozov, E. To save everything, click here:
Technology, solutionism, and the urge to fix
problems that don’t exist, 2013, The penguin
books.
[18]
Lerner, J., Making democracy fun: How game
design can empower citizens and transform
politics, 2014, MIT Press.
[19]
Thiel, S. K., Reisinger, M., and Roderer, K., I’m
too old for this!: Influence of age on perception
of gamified public participation, Proceedings
of the 15th international conference on mobile
and ubiquitous multimedia (MUM ‘16),2016,pp.
343–346, ACM.
[20]
Fisher, J., Digital games, developing
democracies, and civic engagement: A study of
games in Kenya and Nigeria, Media, Culture and
Society 42(7–8), 2020, pp.1309–1325.
[21]
Hassan, L., Governments should play games:
Towards a framework for the gamification
of civic engagement platforms, Simulation &
Gaming, 48(2), 2017, pp.249–267.
[22]
Simon, J., Bass, T., Boelman, V., and
Mulgan, G., Digital Democracy: The tools
transforming political engagement. Nesta, 2017,
https://www.nesta.org.uk/report/digital-democracy
-the-tools-transforming-political-engagement/.
[23]
Geurts, J. L., Duke, R. D., and Vermeulen, P. A.,
Policy gaming for strategy and change. Long
Range Planning 40(6), 2007, 535–558.
[24]
Bateman, C., Playing work, or gamification
as stultification, Information, Communication &
Society 21(9), 2018, pp.1193–1203.
[25]
McGonigal, J., Reality is broken: Why games
make us better and how they can change the
world, 2011, Penguin Press.
[26]
Bousios, A., Gavalas, D., and Lambrinos, L.,
CityCare: Crowdsourcing daily life issue reports
in smart cities, Proceedings of the 2017 IEEE
symposium on computers and communications
(ISCC), 2017, pp. 266–271, IEEE.
[27]
Bianchini, D., Fogli, D. and
Ragazzi, D.,Promoting Citizen Participation
through Gamification, Proceedings of the
9th Nordic Conference on Human-Computer
Interaction (NordiCHI ’16), 2016.
[28]
Delgado-Algarra, E. J., Gamification and
Game-Based Learning: Motivating Social
Sciences Education, In Research Anthology on
Developments in Gamification and Game-Based
Learning, 2022, pp. 932-956.
[29]
Hamari, J., Koivisto, J., Sarsa, H., Does
Gamification Work?—A Literature Review
of Empirical Studies on Gamification, In
Proceedings of the 47th Hawaii International
Conference on System Sciences (HICSS), 2014,
pp. 3025–3034, IEEE.

17
[30]
Ash, J., Emerging spatialities of the screen:
Video games and the reconfiguration of spatial
awareness, Environment & Planning A 41(9),
2009, pp.2105–2125.
[31]
Shaw, I. G. R., and Sharp, J. P., Playing with
the future: Social irrealism and the politics of
aesthetics, Social and Cultural Geography 14(3),
2013, pp.341–359.
[32]
Ash, J., Technology and affect: Towards
a theory of inorganically organised objects,
Emotion, Space and Society 14 , 2015, pp.84–90.
[33]
Vanolo, A., Cities and the politics of
gamification, Cities 74(2018), 2018, pp.320–326.
[34]
Bogost, I., Why gamification is bullshit, The
gameful world, 2014, pp. 65–79, MIT press.
[35]
Lindley, J., and Coulton, P., Game of drones,
Proceedings of the 2015 annual symposium on
computer-human interaction in play , 2015,
pp.613–618, ACM.
[36]
Prandi, C., Roccetti, M., Salomoni, P., Nisi, V.,
Nunes, N. J., and Tools Appl, M., Fighting
exclusion: A multimedia mobile app with
zombies and maps as a medium for civic
engagement and design, Multimedia Tools and
Applications 76(4), 2017, pp.4951–4979.
[37]
Koivisto, J., and Hamari, J., The rise of
motivational information systems: A review of
gamification research, International Journal of
Information Management 45, 2019, pp.191–210.
[38]
(text in Spanish) Ayen F., ?Que es la
gamificacion? Iber: Didactica de las Ciencias
Sociales. Geografia e Historia 86, 2017, pp.7–15.
[39]
Keeler, A., Beyond the worksheet: playsheets,
GBL, and gamification, Edutopia, 2014, pp:1-3.
[40]
Suh, A., Prophet, J., The state of immersive
technology research: A literature analysis,
Comput. Hum. Behav. 2018 86, 2018, pp.77–90.
[41]
Handa, M., Aul, E.G., Bajaj, S., Immersive
technology–uses, challenges and opportunities,
Int. J. Comput. Bus. Res. 6, 2012, pp.1–11.
[42]
Azuma, R.T., A survey of augmented reality,
Presence Teleoperators Virtual Environ. 6,1997,
pp.355–385.
[43]
Burdea, G.C.; Coiffet, P. Virtual Reality
Technology, 2003.
[44]
Speicher, M., Hall, B.D., Nebeling, M., What
is mixed reality? Proceedings of the 2019 CHI
Conference on Human Factors in Computing
Systems, 2019, pp. 1–15.
[45]
Doolani, S., Wessels, C., Kanal, V.;
Sevastopoulos, C., Jaiswal, A., Nambiappan, H.,
Makedon, F., A review of extended reality
(xr) technologies for manufacturing training,
Technologies 8, 2020, pp.77.
[46]
Ringas, C., Tasiopoulou, E., Kaplanidi, D.,
Partarakis, N., Zabulis, X., Zidianakis, E.,
Patakos, A., Patsiouras, N., Karuzaki, E.,
Foukarakis, M., Traditional Craft Training and
Demonstration in Museums, Heritage 5, 2022,
pp.431–459.
[47]
Hauser, H., Beisswenger, C., Partarakis, N.,
Zabulis, X., Adami, I., Zidianakis, E.,
Patakos, A., Patsiouras, N., Karuzaki, E.,
Foukarakis, M., Multimodal narratives for the
presentation of silk heritage in the museum.
Heritage 5, 2022, pp.461–487.
[48]
Lei Q., Du J.-X., Zhang H.-B., Ye S.,
Chen D.-S., A Survey of Vision-Based Human
Action Evaluation Methods, Sensors 19(19),
2019, pp.4129.
[49]
Beddiar, D.R., Nini, B., Sabokrou, M., Vision-
based human activity recognition: a
survey, Multimed Tools Appl 79, 2020,
pp.30509–30555.
[50]
Laidlow, T., Czarnowski, J., and Leutenegger,
S., DeepFusion: Real-Time Dense 3D
Reconstruction for Monocular SLAM using
Single-View Depth and Gradient Predictions,
arXiv,2022.
[51]
Kang, Z., Yang, J., Yang, Z., Cheng, S., A review
of techniques for 3d reconstruction of indoor
environments, ISPRS Int. J. Geo-Inf. 9, 2020,
pp.330.
[52]
Chen, X., Zhu, X., Liu, C., Real-Time 3D
Reconstruction of UAV Acquisition System for
the Urban Pipe Based on RTAB-Map, Applied
Sciences 13(24),2023, pp.13182.
[53]
Hasan, S.M., Lee, K., Moon, D., Kwon, S.,
Jinwoo, S., Lee, S., Augmented reality and digital
twin system for interaction with construction
machinery, J. Asian Archit. Build. Eng. 21, 2022,
pp.564–574.
[54]
Ma, X.; Tao, F.; Zhang, M.; Wang, T.;
Zuo, Y., Digital twin enhanced human-machine

18
interaction in product lifecycle, Procedia
Cirp. 83, 2019, pp.789–793.
[55]
Onaji, I., Tiwari, D., Soulatiantork, P., Song, B.,
Tiwari, A., Digital twin in manufacturing:
Conceptual framework and case studies., Int. J.
Comput. Integr. Manuf. 35, 2022, pp.831–858.
[56]
Sanz Rodrigo, M., Rivera, D., Moreno, J.
I., Alvarez-Campana M., and Lopez, D. R.,
Digital Twins for 5G Networks: A Modeling
and Deployment Methodology, IEEE Access 11,
2023, pp. 38112-38126.
[57]
Hazarika, A., Rahmati M., Towards an
Evolved Immersive Experience: Exploring
5G- and Beyond-Enabled Ultra-Low-Latency
Communications for Augmented and Virtual
Reality, Sensors 23(7), 2023, pp.3682.
[58]
Shabir A., Umirzakova, S., Jamil, F.,
Whangbo, T-K., Internet-of-things-enabled
serious games: A comprehensive survey, Future
Generation Computer Systems 136, 2022,
pp.67-83.
[59]
Smith, M.O., and Wellman, M.P., Co-Learning
Empirical Games and World Models, arXiv,
2023.
[60]
Planas, E., Daniel, G.,Brambilla, M.,
Cabot, J., Towards a model-driven approach
for multiexperience AI-based user interfaces,
Softw. Syst. Model. 20, 2021, pp.997–1009.
[61]
Sousa, R., Miranda, R., Moreira, A., Alves, C.,
Lori, N., Machado, J., Software tools for
conducting real-time information processing and
visualization in industry: An up-to-date review,
Appl. Sci. 11, 2021, pp.4800.
[62]
Escotet, M.A., The optimistic future of Artificial
Intelligence in higher education, Prospects, 2023,
pp.1–10.
[63]
Patkos, T., Bikakis, A., Antoniou, G.,
Papadopouli, M., Plexousakis, D., A semantics-
based framework for context-aware services:
Lessons learned and challenges, Proceedings of
the International Conference on Ubiquitous
Intelligence and Computing, 2007, pp. 839–848,
Springer.
[64]
Abdulsalam, N. H.,Muhammad, S., and
Mohamed, R., A Review of Semantic-Based
Reasoning Framework for Context-Aware
Mobile-Crowdsourcing, 13th International
Conference on Information Technology in Asia
(CITA), 2023, pp. 94-99.
[65]
Kocaballi, A.B., Laranjo, L., Coiera, E.
Understanding and measuring user experience in
conversational interfaces, Interact. Comput. 31,
2019, pp.192–207.
[66]
Villegas-Ch, W., Garcia-Ortiz, J., Enhancing
Learning Personalization in Educational
Environments through Ontology-Based
Knowledge Representation, Computers 12(10),.
2023,pp.199.
[67]
Kipper G. and Rampolla, J., Augmented Reality:
An Emerging Technologies Guide to AR (1st.
ed.), 2012, Syngress Publishing.
[68]
Miller, M. R., and Bailenson, J. N., Augmented
reality, The handbook of listening,2020, pp.
409–417.
[69]
Speicher, M., Hall, B. D., and Nebeling, M.,
What is Mixed Reality? Proceedings of the
2019 CHI Conference on Human Factors in
Computing Systems (CHI ’19), 2019, pp.1–15,
ACM.
[70] United
Nations
Habitat
III:
New
Urban
Agenda.
Available
online:
http://habitat3.org/wp-content/uploads/
NUA-English.pdf (accessed on 5 May 2019).
[71]
Manville, C., Cochrane, G., Cave, J.,
Millard, J.,Pederson, J.K., Thaarup, R.K.,
Liebe, A., Wissner, M., Massink, R.,
Kotterink, B., Mapping Smart Cities in the
EU, European Parliament 2014.
[72]
Harvey, D., The right to the city, Int. J. Urban
Reg. Res. 27, 2003, pp.939–941.
[73]
Katsiri E. and Mycroft A., Knowledge-
Representation and Scalable Abstract Reasoning
for Sentient Computing using First-Order Logic,
Proceedings of Workshop on Challenges and
Novel Applications of Automated Reasoning,
(CADE-19), 2003.
[74]
Hopper, A., The Royal Society Clifford Paterson
Lecture: Sentient Computing, 1999.
[75]
Epic Games, Unreal Engine [Internet]. 2019.
Available from: https://www.unrealengine.com.
[76]
Sandoval-Almazan R., and Valle-Cruz.,D.,
Sentiment Analysis of Facebook Users Reacting
to Political Campaign Posts, Digit. Gov.: Res.
Pract. 1,2, 2020, Article 12.

19
Appendix
A
Unity basic components
A prototype implementation of the specified system
was developed using the Unity game engine. Scenes
are assets that contain all or part of a game or
application. Cameras are used to display the
game world to the player. In Unity, you create
a camera by adding a Camera component to a
GameObject. GameObjects are the fundamental
objects in Unity that represent characters, props and
scenery. They act as containers for Components,
which implement the functionality. Unity has
lots of different built-in component types, while
custom components can be developed using the Unity
Scripting API. Scripts allow for triggering game
events, modifying Component properties over time
and responding to user input. Unity supports the
C# programming language natively. The Transform
component determines the Position, Rotation, and
Scale of each object in the scene. Every GameObject
has a Transform. A Prefab Asset acts as a template
from which new Prefab instances can be created in the
Scene. It is a resusable asset that stores a GameObject
complete with all its components, property values,
and child GameObjects. An Asset is any item used
in a Unity project to create a game or app. Assets can
represent visual or audio elements, abstract items such
as color gradients, animation masks, or arbitrary text
or numeric data for any use. Some types of asset can
be created in the Unity Editor, such as a ProBuilder
Mesh, an Animator Controller, an Audio Mixer, or a
Render Texture.
A.1
Unity UI
The Unity User Interfaceis a GameObject-based UI
system that uses Components and the Game View
to arrange, position, and style user interfaces. The
Canvas is the area that all UI elements should be
inside. The Canvas is a Game Object with a Canvas
component on it, and all UI elements must be children
of such a Canvas. Every UI element is represented as
a rectangle for layout purposes. Visual Components
help you create GUI specific functionality. The Text
component, which is also known as a Label, has a
Text area for entering the text that will be displayed.
It is possible to set the font, font style, font size
and whether or not the text has rich text capability.
An Image has a Rect Transform component and an
Image component. Interaction components handle
interaction, such as mouse or touch events and
interaction using a keyboard or controller. They are
not visible on their own, and must be combined with
one or more visual components in order to work
correctly. A Button has an OnClick UnityEvent to
define what it will do when clicked. A Toggle has an
Is On checkbox that determines whether the Toggle is
currently on or off. A Slider has a decimal number
Value that the user can drag between a minimum
and maximum value. A Scrollbar has a decimal
number Value between 0 and 1. When the user
drags the scrollbar, the value changes accordingly.
A Dropdown has a list of options to choose from.
All three also have a OnValueChanged UnityEvent to
define what it will do when the value is changed. An
Input Field is used to make the text of a Text Element
editable by the user. It has a UnityEvent to define
what it will do when the text content is changed, and
an another to define what it will do when the user has
finished editing it.
A.2
Unity Event System
The Event System is a way of sending events to objects
in the application based on input, be it keyboard,
mouse, touch, or custom input. The Event System
consists of a few components that work together
to send events and its primary roles are managing
which GameObject is considered selected, which
Input Module is in use and updating all Input Modules
as required.
A.3
Unity Physics Engine
In Unity, a collision happens when two GameObjects
that are configured for collision occupy the same
physical space. Collision is a foundational part
of most games, and many interactive applications
and simulators. To handle collision between
GameObjects, Unity uses colliders. A collider is
a Unity component that defines the shape of a
GameObject for the purposes of physical collisions.
Colliders are invisible, and do not need to be
the same shape as the GameObject’s mesh. The
OnTriggerEnter() function is a widely used function
in the Unity game engine [68]. It is used to detect
when an object enters a trigger collider attached to a
GameObject in a Unity scene. The OnTriggerEnter()
function is part of Unity’s scripting system, which
allows game developers to add interactivity to their
games by writing code in various programming
languages. When a GameObject with a trigger
collider enters the area defined by the trigger collider,
the OnTriggerEnter() function is called. This
function is typically used to initiate some kind of
action or behavior in response to the trigger event.
For example, a trigger collider could be used to
detect when a player enters a particular area of a
game level, triggering an event such as a cutscene,
the appearance of enemies, or the revelation of a
hidden item. In order to use the OnTriggerEnter()
function in Unity, a script must be written that
associates the GameObject object with the trigger
configurator. The script must include a method called

20
OnTriggerEnter(), which takes a unique parameter
of type Collider. This parameter represents the
collider of the object that has entered the trigger zo
Overall, the OnTriggerEnter() function is Overall,
the OnTriggerEnter()function is a powerful tool in
the Unity game engine that allows developers to
create dynamic and interactive game experiences.
Using trigger colliders and the OnTriggerEnter()
function, game developers can create complex game
mechanics and events that respond to player actions
in real-time. Unity also provides the OnTriggerExit(),
OnTriggerStay() related functions.
A.4
Scripting concepts
Running a Unity script executes a number of event
functions in a predetermined order. Figure 11
describes those event functions and shows how they
fit into the execution sequence. A script makes its
connection with the internal workings of Unity by
implementing a class which derives from the built-in
class called MonoBehaviour.The Update() function is
the place to put code that will handle the frame update
for the GameObject. To enable the Update() function
to do its work, it is often useful to be able to set
up variables, read preferences and make connections
with other GameObjects before any game action
takes place. The Start() function will be called by
Unity before gameplay begins (ie, before the Update
function is called for the first time) and is an ideal
place to do any initialization. The construction of
objects is handled by the editor and does not take
place at the start of gameplay as one might expect and
therefore constructors cannot be defined for a script
component.
B
e-polis front-end
The game consists of three Scenes:
•
the Welcome scene, that contains basic
instructions, a Play Button and a Quit Button
•
the Main scene that hosts the Smart City-State
•
the Dilemma scene that hosts all Dilemmas.
B.1
Main Scene
For the purpose of constructing the Smart City-State
layout that is part of the Main Scence, SpatialTypes,
RoadBlocks and Dilemmas are the main building
blocks and therefore they were constructed as
Prefabs. SmartSpatialTypes comprise of Asphalts and
Sidewalks as well as five PoliticisedSpatialTypes that
are 3D objects representing the building infrastructure
of the SmartSpatialTypes. The latter were developed
in the Rhino software and were exported in object
format in order to be imported into Unity where
Figure 11: Monobehaviour lifecycle
they appeared as Prefabs. Because each Prefab was
a complex object that had a hierarchical structure
consisting of a title, spatial type, material and as
a result it was not recognized by Unity, they were
deconstructed into a flat hierarchy and only the 3D
objects were retained, as GameObjects with a
Transform. Asphalts and Sidewalks and Roadblocks
are also PolyhedralSpatialTypes, i.e., gameobjects
with a Transform that represents their

21
location and size. Neighborhouds are Prefabs
that contain six SmartSpatialTypes with roads in
between. These prefabs were copied to create a Smart
City State with thirty-six (36) building blocks, i.e.,
SmartSpatialTypes.
Next, in order to add interaction, each
PoliticisedSpatialTypes was added two BoxCollider
components, one nested in the other. The Box
collider is a built-in cube-shaped collider. The Is
Trigger property was enabled to use the collider as
a trigger for events. When Is Trigger is enabled,
other colliders pass through this collider, and trigger
the messages OnTriggerEnter, OnTriggerStay, and
OnTriggerExit.
B.2
Dilemma Scene
Each Dilemma is a Prefab that contains a Canvas with
a back, a top and a bottom Panel, an Image, Five
Buttons for each availabe choice and one Button for
Continuing the Game. The Canvas has a transform
that stored its position. When the Scene is loaded,
the Main Camera Transfer is passed as a parameter so
that the user has a full-frontal view of the Dilemma
and can interact with it.
C
e-polis back-end
The e-polis programming logic is based on C# scripts
that belong to three categories depending on their
scope.
•
Game scope:
QuitManager.cs, RetrievePlayerPosition2.cs,
RetrieveCameraPosition2.cs,
<
SmartSpatialType
>
DilemmaManager
.
cs,
•
SmartSpatialType scope:
<
SmartSpatialType
>
InstanceManager
.
cs
.
•
Dilemma scope: DilemmaCanvasManager.cs
Game scope is required for a) Quit() and
b)ViewFromAbove and in general for functionality
specified in Rules 2c, 3b, 3c, 3d, 3e, 4 (Section D)
where scripts from one scene have to coordinate
with scripts of another. Game scope scripts are
attached an inactive GameObject e.g., a SideWalk
GameObject in the Main Scene. As the default
scope for all scripts attached to GameObjects
is GameObject scope, in order to implement
Game scope, it was necessary to define global
variables stored in the PlayPreferences data structure.
These global variables implement the e-polis
predicates: L_UserChoice() as well as global constant
predicates such as predefined L_AtomicLocation()
both for SmartSpatialTypes and Dilemmas. For
example SmartSpatialType > DilemmaManager.cs
coordinates the state of the five collocated
PoliticisedSpatialTypes so that only the one that
corresponds to the most recent user choice is enabled.
SmartSpatialType scope is required
for
functionality specified in Rules 1a, 2a, 2b
(Section D). Scripts in this category are attached
to PoliticisedSpatialTypes.
Scripts in this category determine what happens
whenever the player enters each of the active zones
(both when the spatial type is enabled or disabled)
by calling OnTriggerEnter(), OnTriggerExit(). It
also determines what happens when the dilemma is
triggered and when the dilemma has been completed
and player returns home.
Dilemma scope include the functionality
specified in Rule 3b and it is implemented by
the DilemmaCanvasManager.cs that is attached to
the Canvas GameObject of each Dilemma. Last,
but not least, the player’s view is through the eyes
rather than from above, and was implemented with
the First Person Controller (FPC) library of the Unity
StarterAssets package.
As such, regarding e-polis Monobehaviour, the
scripts here invoke the Start() function every time the
Main Scene is loaded. This happens in the beginning
of the game as well as every time the user responds
to a Dilemma.
C.1
Main Scene: Start()
The tasks that take place are:
•
the coordination of the object references of all
five Politicised instances of the same Smart
Spatial type and setting their state so that only
the one that matches the latest user choice of
the linked dilemma is enabled and the rest are
disabled.
•
the retrieval and setting of the user
location/rotation (Transform) from the
UserPrefereces so that it looks at the Smart
Spatial Type transformation. When the
user has asnswered all Dilemmas, his/her
location/rotation is set to a ViewFromAbove
predefined location to look at the city from
above.
C.2
Main Scene: OnTriggerEnter()
The tasks that take place are:
•
Detection of the player location with respect to
the two active zones.
•
If located in the outer zone and the spatial type
instance is invisible, the latter becomes visible.
•
If located in the inner zone, a dilemma is
triggered

22
C.3
Main Scene: OnTriggerExit()
The tasks that take place are:
•
Detection of the player location outside the smart
spatial type. The visibility of the spatial type
instance is not affected.
C.4
Main Scene:
Load < SpatialTypeDilemma > ()
The tasks that take place are:
•
setting player’s return position and rotation
(PlayerPrefs)
•
setting the dilemma camera next position
(PlayerPrefs)
•
SceneManager.LoadScene(”Dilemma_scene”);
Dilemma Scene scripts invoke the Start() function
every time the Dilemma Scene is loaded. This
happens when a Dilemma is triggered by the user
entering the inner zone of a smart spatial type.
C.5
Dilemma scene: Start()
The tasks that take place are:
•
the retrieval and setting of the Main Camera
location/rotation (Transform) from the
UserPreferences to portray the Canvas of
the current Dilemma.
C.6
Dilemma scene: OnButtonClick()
The tasks that take place are:
•
the assertion of a UserChoice predicate in the
PlayerPreferences.
•
the logging of the UserChoice predicate in
Firebase.
•
SceneManager.LoadScene(”Main_scene”
With respect to high-level logical predicates, their
most recent instance is made persistent in the User
Preferences data structure. With respect to historical
predicates, a time-series of instances are stored in the
Firebase cloud Database. Proximity interaction is a
collision between two rigid body game objects, i.e.,
the player avatar and the Politicised Spatial Type. To
achieve this, two box colliders are wrapped around
each PoliticisedSpatialType.
D
Rules
Rule 1a: User entered the SmartSpatialType
outer zone, the first ”active” zone. If this is
the first time the user approaches, the default
instance has been enabled inside a previously
empty building block.
(L_UserAtLocation(uid ?uid)(x ?x)(y ?y)(z ?z) ∧
(
L
_
SpatialType
(
sid
?
sid
)(
type
?
type
))
∧
(L_AtomicLocation(rid ?rid)(sid ?sid)) ∧
(
L
_
PoliticisedSpatialType
(
siid
?
siid
)
(
sid
?
sid
)(
pid
?
pid
))
∧
IsDisabled
(
siid
?
sid
)(
sid
?
sid
)
∧
(
L
_
UserChoice
(
cid
?
cid
)(
did
?
did
)(
sid
?
sid
)
=⇒
(H_UserInLocation(uid ?uid)(sid ?sid)(st ?st))
Rule 1b: User interacts with the existing
PoliticisedInstance of the SmartSpatialType.
(
H
_
UserInLocation
(
uid
?
uid
)(
sid
?
sid
)(
st
?
st
))
=⇒
(
H
_
UserAtSmartSpatialType
(
uid
?
uid
)
(
sid
?
sid
)(
st
?
st
))
Rule 2a: User entered the SmartSpatialType
outer zone
(H_UserInLocation(uid ?uid)(rid ?rid) ∧
(L_InRegion(x ?x)(y ?y)(z ?z)(rid ?rid)) ∧
(L_InRegion(nrid ?nrid)(rid ?rid)) ∧
(
L
_
NestedLocation
(
nrid
?
nested
_
rid
)
(prid?rid)(sid ?sid)) ∧
(
L
_
PoliticisedSpatialType
(
siid
?
siid
)
(
sid
?
sid
)(
pid
?
pid
))
∧
(
L
_
SpatialType
(
sid
?
sid
)(
type
?
type
))
∧
(
L
_
Dilemma
(
did
?
did
)(
sid
?
sid
))
=⇒
(
H
_
UserInNestedLocation
(
uid
?
uid
)
(
rid
?
nested
_
rid
)(
prid
?
rid
)(
sid
?
sid
)(
st
?
st
))
Rule 2b: User has triggered the Dilemma
(
H
_
UserInNestedLocation
(
uid
?
uid
)
(
rid
?
nested
_
rid
)(
prid
?
rid
)(
sid
?
sid
))(
st
?
st
))
=⇒
(H_UserInDilema(uid ?uid)(did ?did)(st ?st))

23
Rule 2c: User is transported to the Dilemma
location
H
_
UserInDilema
(
uid
?
uid
)(
did
?
did
)(
st
?
st
))
=⇒
(H_UserInLocation(uid ?uid)
(
drid
?
dilemma
_
region
_
id
)(
st
?
st
))
Rule 3a: Dilemma was answered by user
(
H
_
UserInLocation
(
uid
?
uid
)
(
drid
?
dilemma
_
region
_
id
)(
st
?
st
))
∧
(
L
_
SpatialType
(
sid
?
sid
))(
type
?
type
)
∧
(
L
_
Dilemma
(
did
?
did
)(
sid
?
sid
))
∧
(
L
_
UserChoice
(
cid
?
cid
)
(
uid
?
uid
)(
did
?
did
)(
sid
?
sid
)
=⇒
(
H
_
UserAnsweredDilema
(
uid
?
uid
)
(
did
?
did
)(
sid
?
sid
)(
st
?
st
))
Rule 3b: User is transported back to the
SmartSpatialType to see the resulting
transformation
(
H
_
UserAnsweredDilemma
(
uid
?
uid
)
(did ?did)(sid ?sid)(st ?st)) ∧
∃
(
did
)
|
¬
(
H
_
UserAnsweredDilemma
(
uid
?
uid
)
(
did
?
did
)(
st
?
st
))
=⇒
(H_UserInLocation(uid ?uid)
(
rid
?
rid
)(
sid
?
sid
)(
st
?
st
))
Rule 3c: The SmartSpatialType is
transformed.
(
H
_
UserAnsweredDilemma
(
uid
?
uid
)
(did ?did)(sid ?sid)(st ?st)) ∧
∃
(
did
)
|
¬
(
H
_
UserAnsweredDilemma
(
uid
?
uid
)
(did ?did)(st ?st)) ∧
(L_UserChoice(cid ?cid)
(
uid
?
uid
)(
did
?
did
)(
sid
?
sid
))
∧
(
L
_
PoliticisedSpatialType
(
siid
?
siid
)
(
sid
?
sid
)(
pid
?
pid
))
∧
(
L
_
PoliticisedUserChoice
(
cid
?
cid
)(
pid
?
pid
)
=⇒
(
H
_
SmartSpatialTypeTransformed
(
tid
?
tid
)
(
sid
?
sid
)(
siid
?
siid
)(
pid
?
pid
)(
st
?
st
))
Rule 3d: The PoliticisedSmartSpatialType
instance that corresponds to the UserChoice is
enabled.
(
H
_
SmartSpatialTypeTransformed
(
tid
?
tid
)
(
sid
?
sid
)(
siid
?
siid
)(
pid
?
pid
)(
st
?
st
))
=
⇒
IsEnabled(siid ?siid)(sid ?sid)(pid ?pid)(st ?st))
Rule 3e: The rest n-1co-located
PoliticisedSmartSpatialType instances are
disabled.
(
H
_
SmartSpatialTypeTransformed
(
tid
?
tid
)
(
sid
?
sid
)(
siid
?
siid
)(
pid
?
pid
)(
st
?
st
))
=
⇒
∀
(
siid
)
|
¬
(
L
_
PoliticisedUserChoice
(
cid
?
cid
)(
pid
?
pid
)
IsDisabled
(
siid
?
siid
)(
sid
?
sid
)(
pid
?
pid
)(
st
?
st
))
Rule 4: When all dilemmas have been
answered, the user is transported to a
PolyhedralSpatiaType that represents a
hovering balcony with a view above the
city to check the final landscape.
∀
(
did
)
|
(
H
_
UserAnsweredDilemma
(
uid
?
uid
)(
did
?
did
)(
st
?
st
)
=⇒
(H_UserinLocation(uid ?uid)(”Sky”)(st ?st))
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflicts of Interest
The authors have no conflicts of interest to
declare that are relevant to the content of this
article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International , CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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