Artificial intelligence moving serious gaming:
Presenting reusable game AI components
&Pedro A. Santos
Received: 13 December 2018 /Accepted: 5 July 2019 /Published online: 30 July 2019
This article provides a comprehensive overview of artificial intelligence (AI) for serious
games. Reporting about the work of a European flagship project on serious game
technologies, it presents a set of advanced game AI components that enable pedagog-
ical affordances and that can be easily reused across a wide diversity of game engines
and game platforms. Serious game AI functionalities include player modelling (real-
time facial emotion recognition, automated difficulty adaptation, stealth assessment),
natural language processing (sentiment analysis and essay scoring on free texts), and
believable non-playing characters (emotional and socio-cultural, non-verbal bodily
motion, and lip-synchronised speech), respectively. The reuse of these components
enables game developers to develop high quality serious games at reduced costs and in
shorter periods of time. All these components are open source software and can be
freely downloaded from the newly launched portal at gamecomponents.eu. The com-
ponents come with detailed installation manuals and tutorial videos. All components
have been applied and validated in serious games that were tested with real end-users.
Keywords Serious games .Artificial intelligence .Software reuse .Game development .
Component-based architecture .Intelligent tutoring systems
Computer games have been linked with artificial intelligence (AI) since the first
program was designed to play chess (Shannon 1950). The challenge to defeat human
expert players in rule-based strategy games such as Chess, Poker and Go has greatly
Education and Information Technologies (2020) 25:351–380
*Wim Weste ra
Extended author information available on the last page of the article
The Author(s) 2019
advanced the domain of AI research, affecting breakthroughs in e.g. computational
intelligence, algorithms, machine learning, and combinatorial game theory (Fujita and
Wu 2012). In turn, such new AI methods have been used in computer games, for
instance to enhance graphical realism, to generate levels, sceneries and storylines, to
establish player profiles, to balance complexity or to add intelligent behaviours to non-
playing characters (NPC; Yannakakis and Togelius 2015,2018).
Over the years, however, various authors (Champandard 2004;Bourassaand
Massey 2012; Yannakakis 2012; Yannakakis and Togelius 2018) have pointed at the
marginal penetration of academic game AI methods in industrial game production. This
limited uptake has been attributed to 1) research projects largely focusing on advanced,
but non-scalable projects of little commercial or practical value, and 2) game studios
reluctant to adopt and include promising but risky AI techniques (such as neural
networks) rather than established, fully scripted technologies in their games. The game
industry’s reticence to embrace advanced AI may partly be explained by the manifest
failure of AI during the 1980’s and 1990’s to live up to their promises of enabling
expert systems and intelligent dialogue. Ever since, the marriage between AI and
gaming has appeared brittle, which is readily attributed to the limited interconnection
and exchange between research and industry.
Research policy makers and politicians both at national and international levels have
recognised that the transfer of knowledge and technologies from research and devel-
opment organisations to societal sectors in order to create economic and social value, is
a fundamental problem that should be urgently addressed. This failure is generally
known as the “knowledge paradox”(European Commission 1995), referring to the fact
that in many countries increased public investments in science and technology do not
translate into economic benefits and job creation, while leaving many scientific find-
ings unused. The process of knowledge valorisation often fails, which is painful as such
in the case of games for learning, because of their dual role in both innovating the
domain of education and contributing to raised skills levels in other content domains.
This failure has been manifestly the case for the game industry, in particular the serious
game industry (developing games for serious purposes rather than entertainment), since
the serious game industry sector is composed of a large number of small independent
studios (Stewart et al. 2013) that lack the scale and capacity to easily access new
research knowledge and technologies and include these in their projects. Having
recognised the potential of games for teaching and training and other societal sectors,
the European Commission has stimulated diverse collaborations between game re-
search and game industry. This article presents the main outcomes of the RAGE
project, which has been the principal and most sizable research and innovation project
in the European Horizon 2020 funding programme addressing serious games. Its goal
and research assignment has been to investigate how a framework of reusable, intelli-
gent game software components should be devised to structurally accommodate
technology transfer from game research to the game industry, to assess and validate
the outcomes, and propose measures for sustained societal impact. Rather than ad-
dressing AI per se, the research essentially focuses on the opportunities of the practical
application of AI in serious games.
This article is the very first aggregate publication of the RAGE research programme
carried out by over 130 researchers from all over Europe. We will first summarise
related work of recent advances in game AI. Next, we will briefly explain the proposed
352 Education and Information Technologies (2020) 25:351–380
game software component architecture. Then, we will describe and explain a selection
of AI-driven game software components. These address functionality for player model-
ling (real-time facial emotion recognition, automated difficulty adaptation, stealth
assessment), natural language processing (sentiment analysis and essay scoring on free
texts), and believable non-playing characters (emotional and socio-cultural, non-verbal
bodily motion, and lip-synchronised speech), respectively. In conclusion, key
results from the various application pilots will be summarised and discussed in
the light of the anticipatedknowledge andtechnology transfer mechanism for the serious
2 Related work in game AI
Game design is essentially about creating valuable interactive experiences for the
players. These experiences are effected by a variety of orchestrated game elements,
including narratives, challenges, graphical representations, sounds, timing of events
and phenomena, and the entities that directly interact with the player, be it opponents,
allies, or other objects in the game environment. AI techniques will become indispens-
able to coordinate the ever-growing complexity and dynamics of games. From a
pragmatic perspective, game developers are happy to use ad hoc cheats that offer
players the illusion of intelligence, instead of any deep intelligence (Rabin 2017). This
can work well until extended interaction reveals the tricks used breaking down the
game experiences. As hardware capabilities improve, new types of interaction will
emerge that would need better AI. In recent years, AI in games has improved
appreciably (Lewis and Dill 2015). Below, we refer to example usage of advanced
AI techniques in mainstream commercial games, mostly applied to control NPC
behaviours. Artificial Neural Networks with 3 layers were used in the real-time strategy
game Supreme Commander 2 [Gas Powered games, 2010] to control platoons’reaction
to encountering enemy units (Robbins 2013). From robotics research, systems for
collision avoidance based on Reciprocal Velocity Obstacle (RVO) techniques (Van
den Berg et al. 2011) have been made available as libraries and found their way into
Warhammer 40,000: Space Marine [Relic, 2011] and many other games. The game
Guild Wars 2: Heart of Thorns [ArenaNet, 2015] used an advanced utility-based
decision architecture to solve problems related to tactical movement and skill use
selection for its Non-Playing Characters (NPC) (Lewis 2017). Forza Motorsport 5
[Turn 10 Studios, 2013] and its successors gather data about how players drive, that
is then processed using Machine Learning techniques. This allows for the creation of
“drivatars”that mimic a specific player driving style, and can then be used to play
against. A similar goal was attained in the Killer Instinct fighting game [Iron Galaxy
Studios, 2014] using case-based reasoning. For other uses of Machine learning tech-
niques in games, the survey by Nguyen et al. (2015) provides a good start. Multi-agent
systems have been suggested as powerful solutions to intelligent NPCs (Dignum et al.
2009). However, real-time synchronisation of many agents acting autonomously, for
instance in battlefield games such as the Call of Duty series [Infinity Ward, from 2003],
easily produce performance problems. Several advanced techniques for optimisation
such as flow field and congestion concepts (Pentheny 2013,2015), context steering
(Fray 2015), and even robotics-inspired Velocity Obstacle techniques (Guy and
Education and Information Technologies (2020) 25:351–380 353
Karamouzas 2015) have been applied. The blockbuster game Grand Theft Auto V
[Rockstar North, 2013] uses multi-agent based architectures for the simulation of
subsystems, as do most of the real-time modern strategy games, like those based on
the Clausewitz Engine from Paradox Development Studios.
Adaptive gameplay has be accommodated with various algorithms used for
matching two human players, such as TrueSkill (Herbrich, Minka and Graepel 2006)
and variations of Elo (Elo 1978), or matching game task difficulty to players’skill, such
as the Computerized Adaptive Practice algorithm (Klinkenberg et al. 2011).
Hierarchical Task Networks (HTN) were used in the planner implemented for the third
person shooter Transformers: Fall of Cybertron [HighMoon, 2012] (Humphreys 2013).
Advances in Natural Language Processing (NLP) have opened up new opportunities
to support natural dialogues with NPCs, either companions or enemies, and to support
interactive storytelling (Yannakakis and Togelius 2018). In the multiplayer online battle
arena League of Legends [Riot Games] NLP trained models have been used to
recognise and remove toxic behaviour from the player chat channels (Maher 2016).
Generally, these applications of AI are proprietary solutions bound and tuned to a
particular game and not accessible and reusable by other parties. Also, their application
in serious games has been quite limited.
3 Platform-independent game AI
3.1 Lightweight game software reusability framework
Given the diversity of software platforms, programming languages, browsers and
operating systems, favourable conditions for the reuse of software by game developers
should be accommodated by a shared architectural framework. The main starting points
for the architecture include: 1) Extendibility (The architecture should be robust over
extending the set of components with new software components, 2) Addressing
platform and hardware dependencies (Direct access to the operating system should
be avoided; a conservative approach as to avoid browser version issues as much as
possible), 3) Portability across game engines and programming languages, 4) Avoiding
dependencies on external software frameworks and libraries (such as jQuery or
methodologies (the development process), 6) Neutrality with respect to game genre,
design and style (avoiding direct access to the interface; components just provide smart
functionality under the hood), and 7) Truly lightweight (easy to use in different
operational contexts). In close collaboration with game industry representatives, a
component-based design framework (Bachmann et al. 2000) has been developed
(Van der Vegt et al. 2016a,b). Although the architecture is self-contained and supports
component-to-component communication its application context is generally driven by
a game engine that can access the component’s functionality once the component or its
service is declared and integrated in the engine. Client-side plug-in characteristics of
software components are created by relying on well-established coding practices and
software patterns that procure abstraction, viz. decoupling functionality and its imple-
mentation. Most notable software patterns used for communication with the game
engine are the Bridge pattern and the Broadcast/Publish/Subscribe pattern (Gamma
354 Education and Information Technologies (2020) 25:351–380
et al. 1994); Birman and Joseph 1987.). Remote communications with server-side
components are covered by web services. The architecture has been extensively tested
and validated in connection with a wide diversity of development tools, target platforms
and programming languages that are being used in practice (Van der Vegt et al. 2016a).
3.2 The gamecomponents.eu portal
The recently launched gamecomponents.eu portal funded by Horizon2020 is the
technical platform for exchanging advanced game technologies and associated re-
sources: it accommodates an open marketplace, which is driven by the RAGE
Foundation. Notably, the portal offerings are fully platform independent, while –in
contrast - existing game portals are either driven by commercial game platform vendors
(e.g., Unity, Unreal from Epic, CryEngine from CryTek), by vendors of other creative
software tools (e.g., Adobe), or by general media stock asset marketplaces (e.g.,
graphicriver.net). Moreover, existing portals focus mostly on media assets (e.g. 3D
objects, textures, sounds) rather than software. Also, the gamecomponents.eu portal
specifically targets serious games, while other platform primarily address leisure
games. Nevertheless, leisure games could also benefit from the technologies exposed
on our portal. Figure 1shows a screenshot of a software catalogue page, revealing
taxonomy-based filtering, keyword search and a results section displaying available
game components. Software developers can describe and submit their contribution
through a component-authoring widget that provides a stepwise guidance through the
submission process, allowing for entering the software or software references (e.g.
Education and Information Technologies (2020) 25:351–380 355
Fig. 1 A screenshot of the software catalogue at the marketplace portal (gamecomponents.eu)
Github), its metadata (a specific game component metadata schema was designed for
this (Georgiev et al. 2016)), and supporting artefacts such as technical documents
(installation guides), training materials (e.g. video tutorials), and marketing materials.
In addition, it offers an interactive stakeholder map, a set of tools for taxonomy
management, training course creation and eCommerce management, and it uses
open-ID user management, offers social API for the exchange with social networks
(e.g. Slideshare, Mendeley) and incudes a rating system based on scores by end-users.
The portal is available at gamecomponents.eu.
3.3 First batch of public game AI components
An initial set of about 40 game components are exposed on the portal. The components
exposed at the portal cover a wide range of AI-based functionalities that are relevant for
serious game development, including personalisation, game difficulty balancing, as-
sessment, player analytics, competence modelling, social gamification, language tech-
nologies and affective computing, among other topics. All components are open source
and free of charge. Most client-side components, which need to be directly
integrated with the game engine, are available in C#, while some also offer
the adoption and reuse of the software have been met: 1) successful integration
has been demonstrated with various game development environments (e.g.
Unity, Xamarin, Cocos, Mono), 2) the integration in games is easy, 3) all
components have been used and tested in real games with real end-users to
provide empirical evidence of practicability; 4) the components have been
enriched with ample documentation, tutorials, demos, research articles and
evaluation results; 5) they use the highly flexible Apache 2.0 license (white
label software), which allows for reuse by third parties both for commercial and
non-commercial purposes, either under open source or closed source conditions.
To further the viability and sustainability of the marketplace portal and to attain
critical mass of relevant game software, third-party providers –either game
research projects or IT-oriented companies - are expressly being invited to post
their game software, whether or not compliant with the component-based design
framework, onto the portal.
4 Selected game AI components
4.1 AI key areas
This section presents a selection of reusable game AI components that have been made
available at the gamecomponents.eu portal. The selection focuses on Player
Experience Modelling (PEM), Natural Language Processing (NLP), and ad-
vanced Non-Playing Character modelling (NPC), respectively, all of which are
among the flagships of game AI research (Yannakakis 2012; Yannakakis and
Tog eliu s 2015). Their relevance for serious games is readily explained by the
pedagogical frame of teaching, which assumes a teaching agent (cf. NPC) that
frequently probes and assesses the learner’s mental states (cf. PEM) and, when
356 Education and Information Technologies (2020) 25:351–380
needed, engages in a supportive dialogue with the player (cf. NLP) to provide
guidance or feedback.
4.1.1 PEM: Player experience modelling
PEM can be based on a variety of player data, including the player’s behavioural and
performance data from the game (e.g. speed, score, decisions) and multiple player-
related input modalities such as speech data (intonation, text), images (pupillometry,
gaze tracking, gesture and bodily movement tracking), or physiological signals (EEG,
respiration, blood volume pulse, skin conductance). So far, however, the capturing of
physiological signals has been problematic since it requires hardware (sensors) that are
often too obtrusive and unpractical for continuous application. In this article three
unobtrusive PEM-related AI components will be presented:
&Real-time facial emotion recognition
&Adaptation and assessment
4.1.2 NLP: Natural language processing
Natural language processing is the field of AI focused on the understanding,
interpretation and manipulation of human language by computers. It allows the
computer to assess any textual messages or documents sent by the player, and
thereby it allows to respond to these automatically in a meaningful way. So far,
NLP has scarcely been used in games. The following NLP services will be
&Natural language processing: sentiment analysis
&Natural language processing: automated essay grading
4.1.3 NPC: Non-playing characters
Game AI for NPCs has a longstanding history, in particular focused on navigation and
other low levels of control (Yannakakis 2012). Recent research, however, has been
focusing on a variety of high level NPC behaviours that should effect more flexible,
believable, knowledgeable, human-like, and intelligent behaviours, including realistic
bodily motion, NPC emotion modelling, and compliance with socio-cultural conven-
tions. This reflects a more holistic perspective on the NPC capable of flexible re-
sponses, as opposed to fully scripted applications. The following NPC components will
&Role play character: emotion appraisal and social importance dynamics
&Nonverbal bodily motion: behaviour mark-up language
&Nonverbal bodily motion: lip-synchronised speech
Education and Information Technologies (2020) 25:351–380 357
4.2 PEM: Real-time facial emotion recognition
4.2.1 Emotion recognition
Artificial Emotional Intelligence (AEI), which is also known as emotion recognition or
emotion detection, is a technology that extracts human emotions from displayed
behavioural or physiological features (Schuller and Schuller 2018). Human
facial expressions have demonstrated to produce the most informative data for
computer awareness of emotions (Sebe 2009), outperforming approaches that
make use of either speech and vocal intonations, physiological signals, body
gesture and pose, text, or combinations of two or more of these approaches
(Bahreini et al. 2016). So far, the use of emotion recognition functionality has
not been a feasible option well within reach of game developers, because of the
complexity of the implementation involved, limited accuracy, problems with
facial hair and glasses, specific requirements with respect to lighting conditions,
extensive post-processing and some more (Pantic et al. 2005). The real-time facial
emotion recognition component, created by the Open University of the Netherlands,
solves many of these problems (Bahreini et al. 2018).
4.2.2 Relevance for learning and teaching
Emotions are a significant influential factor in the process of learning, as they affect
memory and action (Pekrun 1992). Any classroom teacher would take into
account the emotional states of learners during the lessons. In computer-based
learning, however, the learner’s emotion has been systematically neglected as a
learner model variable, because it was hard, if not impossible to detect. Now
that emotion recognition technology is becoming available and accessible,
learner models in serious games can include the learners’emotions and thereby
improve the quality of personalised guidance and feedback. Also, the players’
emotions can become part of the learning content or game scenario, for instance
in games for communication training, conflict management, or actor training
(Bahreinietal.2017). Finally, emotion recognition can be used to collect
4.2.3 AI approach
This software component uses artificial emotional intelligence to unobtrusively
cover unbiased facial expressions of emotion from any image, either from a
still, a video file, a video stream or a webcam. The technology uses a
combination of fuzzy logic rules and machine learning. The fuzzy logic AI
algorithm uses unordered fuzzy rule induction (FURIA algorithm; Hühn and
Hüllermeier 2009), which is trained with a reference set of recorded emotions.
It detects happiness, sadness, surprise, fear, disgust, anger and the neutral face,
with accuracy above 80%, which compares or even outperforms human experts.
Alternative machine learning approaches, such as neural networks, Bayesian networks,
and decision trees are less practical for real-time operation as they require extensive
processing, while offering weaker performance.
358 Education and Information Technologies (2020) 25:351–380
4.2.4 Application cases
A usage example of the real-time facial emotion recognition component would be the
Jobquest game, which offers a job application interview training (Gutu et al. 2018).
During the job interview, the players should control their manifest emotions and never
display anger, fear or disgust. During the exercise, they receive direct on-screen
feedback about the displayed emotion through the player’s webcam shot and the
associated emoticon (upper left corner in Fig. 2).
Emotion recognition has also been used for communication training in the
Communication Advisor game (Bahreini et al. 2017). This game places the players
in a variety of real life situations to which they have to respond via a natural dialogue.
Feedback to players is based on their facial expressions.
4.2.5 Technical considerations
The real-time facial emotion detection is a client side software component that is to be
integrated in the game engine. While using the player’s personal webcam, it detects
emotions in real-time. It returns a string value representing the seven basic emotion
classes, which can be used for further processing in the game. It can also process a single
image file, or a recorded video file. Also, presence of multiple players in one shot can be
accommodated as it can detect multiple faces and interpret their emotions at the same
time. It can easily be integrated in many game engines, including, for instance Unity3D.
4.3 PEM: Adaptation and assessment (TwoA)
4.3.1 Game balancing
The TwoA software component offers a dynamic game difficulty balancing mecha-
nism, which automatically matches the difficulty of the player’s task to the player’sskill
Education and Information Technologies (2020) 25:351–380 359
Fig. 2 Direct emotion feedback in the Jobquest application interview game (upper left corner)
(Nyamsuren et al. 2017). Game difficulty balancing is deemed an essential mechanism
to preserve player engagement, improve player motivation, and improve the overall
gameplay experience. Gee (2003) suggests that the secret of a video game is not in the
fancy, high quality, immersive 3-D graphics, but it is in the underlying mechanism that
balances the challenges offered to the player with the players’abilities “…seeking at
every point to be hard enough to be just doable”. Dynamic game difficulty balancing
avoids both frustration of the player (when tasks are too complex) and boredom (when
tasks are too easy). This also holds for teams: difficulty balancing is commonly applied
in online multiplayer games such as the first-person shooter series Halo and multiplayer
online battle arena games like League of Legends to ensure that opposing teams are
evenly matched in terms of skills (Claypool et al. 2015). While the adaptation mech-
anism is often mistaken and confused with a simple if-then-else structure or a level
closure, it should incorporate a sophisticated self-adjusting optimisation algorithm that
frequently reiterates both task difficulty and skills mastery. The implementation and
testing of such algorithm is anything but straightforward. The adaptation and assess-
ment component created by the Open University of the Netherlands offers a fully
automated, self-adjusting balancing algorithm that exposes superior reliability and
stability. It comes as an easy to use software component that can be readily integrated
in various game engines.
4.3.2 Relevance for learning and teaching
The engaging capabilities of games are to be largely attributed to the process of game
balancing. By avoiding frustration and boredom and offering doable challenges a well-
balanced serious game enhances and preserves learner motivation, which is a principal
determinant of learning. In accordance with the Zone of Proximal Development theory
(Vygotsky 1978), a real-time adaptation of the game difficulty enables a smoother
learning experience. The AI algorithm controls difficulty so that the player is chal-
lenged to improve a skill or acquire new knowledge without facing overly difficult
tasks beyond player skill level. As a result it produces an optimised learning curve,
since it iteratively re-assesses the player’s skills mastery and continually adjusts task
difficulty to the appropriate level. This means that the learning process becomes highly
efficient: progression is optimised, while no time is wasted on tasks that do not
contribute to learning. Continued assessment of the player’s skill serves as a form of
formative learning analytics that can be used by the player or the teacher to monitor
learning progress and identify potential learning barriers (Hofman et al. 2018). Finally,
game difficulty assessment also enables the analysis and optimisation of the game’s
learning content (Nyamsuren et al. 2018a).
4.3.3 AI approach
The AI algorithm developed for this adaptation software component is strongly rooted
in the Elo rating system that was originally developed to assess chess players’skills
(Elo 1978). This also holds for one of the widely-known examples of such balancing
algorithms, namely TrueSkill (Herbrich et al. 2006) developed by Microsoft. However,
TrueSkill was designed specifically to assess and match players in large-scale com-
mercial online games. Another example would be the Computerized Adaptive Practice
360 Education and Information Technologies (2020) 25:351–380
system (CAP), which was specifically developed to assess player skill in a serious
game (Klinkenberg et al. 2011). It extends the Elo algorithm with methods from Item
Response Theory (IRT; Lord and Novick 1968). The methods from the Elo system
enable CAP to (re)estimate both the player skill and the game difficulty based on the
player’s real-time performance. In turn, the methods from IRT enable CAP to adapt the
game difficulty based on the player skill using the previously estimated ratings. The
Adaptation and Assessment component presented here adds several theoretical and
practical improvements to CAP. TwoA considerably improves CAP’s adaptive capa-
bilities by minimising selection bias that may be present while choosing an appropriate
difficulty level. This is achieved by expanding the IRT methods with fuzzy logic (Hühn
and Hüllermeier 2009). Multiple selection criteria can mitigate the selection bias, and
fuzzy logic allows to combine these criteria into a single selection rule. As a result, the
improved algorithm is more robust and accurate than CAP especially during the
calibration period when true skill and difficulty ratings are not well approximated
(Nyamsuren et al. 2018b).
4.3.4 Application case
The effectiveness of CAP was extensively demonstrated with its application in Math
Garden (www.mathsgarden.com), a popular Dutch serious gaming platform addressing
primary school children (Van der Maas and Nyamsuren 2017). Publicly available data
from a Math Garden game collected from over 1500 Dutch schools featuring 87,000
unique players were reused to validate the improved performance of the algorithm of
the adaptation and assessment component. The TwoA component has also been used in
an entrepreneurial skills training game (Hatch) at Hull College.
4.3.5 Technical considerations
From a practical perspective, this adaptation and assessment component offers an open-
source, highly portable, and easy-to-use implementation of the AI algorithm. As a
reusable component compliant with the RAGE architecture, it can be easily integrated
with the most modern game engines (Van der Vegt et al. 2016a). The component hides
all the complexities of the algorithm behind a simple interface. Apart from the
management of player and game data, its operation requires only two method calls
from the game to the component.
4.4 PEM: Stealth assessment
4.4.1 Using log game data for assessment
Stealth assessment is a promising methodology for applying formative assessments in
serious games to unobtrusively assess the players’knowledge or skills mastery based
on the player’s behaviours and decisions in the game (Shute 2011). This means that
behavioural data (e.g. log files) are analysed at a certain point in time to determine the
player’s mastery without the need for explicit tests, e.g. multiple choice questions. In
practice, however, the application of stealth assessment in serious games is a complex
and time-consuming process (Moore and Shute 2017). Therefore, its uptake has been
Education and Information Technologies (2020) 25:351–380 361
below par as yet. The generic tool provided by the Open University of the Netherlands
removes many of the practical barriers for applying stealth assessment, as it has largely
automated the many data processing steps that so far need to be handled manually
(Georgiadis et al. 2018).
4.4.2 Relevance for learning and teaching
Games are expressly suited for the acquisition of highly contextualised, tacit knowledge
and action-bound skills, which are notably hard to capture in formal tests and exams.
Cases in point would be social skills, communication skills, group moderation skills,
but also competencies such as persistence, creativity, self-efficacy, teamwork and the
wider collection of twenty-first century skills, all of which are deemed essential for
successful future careers and presupposing a strong link with concrete action (Dede
2010). Given this tacit knowledge dimension, the assessments should not be adminis-
tered (solely) as separate oral or written assignments, but instead should be directly
based on the activities displayed. Stealth assessment provides an attractive alternative to
the existing de-contextualised assessment methods by linking the assessment directly to
the practical use of knowledge and skills in relevant situations. Moreover, these
situations should entail scenarios that require the application of various competencies
at the same time. This is exactly what serious games are capable of providing.
4.4.3 AI approach
Stealth assessment uses machine learning technology to provide probabilistic reasoning
over the learners’knowledge and skills levels by exploiting meaningful data which is
collected during gameplay. Stealth assessment combines two main ingredients: 1) the
Evidence-Centered Design (ECD; Mislevy 2011), and 2) machine-learning (ML)
algorithms. ECD is a conceptual assessment framework that can be used to express
the statistical relationships between competency constructs, in-game observables, and
in-game tasks. As for the machine learning algorithms, originally Bayesian Networks
were used (Shute 2011) although alternative solutions have also been examined
(Decision Trees, Support Vector Machines, and Deep Learning) (Sabourin et al.
2013; Min et al. 2015). The new, generic application for stealth-assessment presented
here allows the user to 1) define and configure ECDs, 2) import numerical data from
log files deriving from any serious game, and 3) declare desirable machine learning
optimisations (e.g. select the preferred machine learning algorithm type and its inner
options). Thereby the need for specific machine learning expertise is minimised as the
tools cover machine learning functions automatically. The tool produces detailed output
for both students’performances and the machine learning algorithms’performances for
4.4.4 Application case
So far, stealth assessment has been proven to be robust for assessing several compe-
tencies in serious games, such as qualitative physics (Shute et al. 2013), persistence
(Ventura et al. 2014), and problem-solving skills (Shute et al. 2016). The application
has been rigorously tested and validated with a large volume of simulation data sets
362 Education and Information Technologies (2020) 25:351–380
including different competencies, in particular with respect to stability, accuracy and
robustness under conditions of normality violation. All accuracies are well in the 95%
range. As a next step practical validation with authentic game data is anticipated.
4.4.5 Technical considerations
The stealth assessment component is currently available as a console application. It was
coded in C# using the. NET framework and it functions as a stand-alone client-side
console application. It includes various data reformatting procedures. It makes use of
ML libraries from the Accord.NET framework. On top of the console application a
graphical user interface is being developed, including a wizard that supports the
workflow and assists the user (e.g. serious game developer, educator) at tuning and
optimising assessment settings.
4.5 NPC: Sentiment analysis
4.5.1 ReaderBench sentiment analysis
The ReaderBench framework (Dascalu et al. 2013), developed by University
Politehnica of Bucharest, is a multi-lingual, advanced text analysis framework that
offers a wide variety of NLP functionalities. Sentiment analysis (also referred to as
opinion mining) consists of the automated extraction of subjective information related
to human feelings and opinions from natural language texts (Liu 2012). It provides
insights towards users’perceptions by interpreting information about the polarity of a
text (i.e., how positive or negative) and by identifying emotions expressed within it.
4.5.2 Relevance for learning and teaching
In the context of serious games, sentiment analysis can be used, for instance, in
dialogues, commonly available either in multi-player communication or in discussions
with a virtual character. The arising insights about how people feel and interact during
these interactions can then be fed back to the game for further usage in the game
scenario, or can be provided as feedback for the game development team. Alternatively,
sentiments can be extracted from written free text or spoken assignments in the game,
such as reports, pitches or answers to open-ended questions.
The state of the art in sentiment analysis is represented by deep learning with either
Convolutional Neural Networks (CVN; Kim 2014), Recurrent Neural Networks
(Socher et al. 2013), the Dependency Tree Long Short-Term Memory Networks
(Tree-LSTM; Tai et al. 2015) or Bi-directional LSTM (BiLSTM; Graves and
Schmidhuber 2005). The LSTM networks are probably the most used type of text
encoder for the majority of tasks involving text comprehension. ReaderBench’ssenti-
ment analysis service is based on a BiLSTM network. In a comparative study using
data from a corpus of 201,552 games reviews crawled from Metacritic (http://www.
metacritic.com/game), the BiLSTM network achieved an overall accuracy of 74%,
Education and Information Technologies (2020) 25:351–380 363
thereby outperforming various Dependency Tree Networks, Support Vector Machine
approaches, Universal Sentence Encoder (USE; Cer et al. 2018), and Multinomial
Naive Bayes models with 3% to 7%.
4.5.4 Application case
A practical example of using sentiment analysis in a serious game is the Jobquest game,
referred to above. Users are requested to prepare and optimise their Curriculum Vitae
(CV) in French language, in view of a specific job opening. The textual content of the
uploaded CV is then analysed with ReaderBench services, which returns specific
feedback, including sentiment valence scores, indicators of emotions, textual complex-
ity factors and general statistics related to visual or contents quality (Gutu et al. 2018).
A French corpus consisting of a collection of articles published by the Le Monde
newsarticle was used to train the system. The CV model was trained with a training set
of around 100 CVs, manually assessed on a set of characteristics that define a good
commercial CV, which then produced an accuracy of 67%.
4.5.5 Technical considerations
The sentiment analysis service provided by ReaderBench can be accessed as a
remote web service through a dedicated endpoint exposed within the
ReaderBench API. The service is open and does not require authentication.
As the ReaderBench framework is provided as an open-source framework,
developers can install it on their servers and can develop their own services
by extending the facilities of the framework, which can be cloned from a
GitLab server (https://git.readerbench.com/ReaderBench/ReaderBench). In terms of
semantic models, developers can either use pre-trained corpora, or they can train a
custom model for their specific scenarios. The sentiment analysis service currently
supports multiple languages, namely English, French, and Dutch.
4.6 NPC: Automated essay scoring
4.6.1 ReaderBench essay scoring
The ReaderBench framework also incorporates an essay scoring functionality which is
capable of assigning comprehension scores to open text inputs, for instance students’
assignments (or reports), or answers to open-ended questions.
4.6.2 Relevance for learning and teaching
In serious games writing assignments and open-ended questions are scarce, because of
the intensive manual effort needed for assessing the learner productions. Also, writing
assignments may be considered too schoolish as opposed to the fun of playing games
and they are deemed a potential disruption of the player’sflow(Shute2011). Writing
assignments, however, accommodate deeper knowledge processing since they require
explicit consideration of learned concepts, principles and their relationships, reflection
about the significance and appraisal of the experiences, and the creative synthesis of
364 Education and Information Technologies (2020) 25:351–380
argumentation (Westera et al. 2018). In addition, writing assignments would provide an
excellent diagnostic tool for detailed assessment of learning progress. That is why
schools and universities often require students to write reports or theses as
proofs of mastery. Also, most professions require excellent writing skills, for
instance in journalism, health, education, marketing, business consultancy and
many other areas. Now that automated processing is becoming available,
writing assignments need no longer be omitted in serious games. The very
method of essay scoring can also be used to inform the game development
team about the complexity of instructional texts and other textual learning
materials exposed in the game, which allows their adjustment for a better fit with the
player characteristics and needs.
4.6.3 AI approach
For various languages a separate NLP pipeline model was created, using language
specific dictionaries, stop words elimination, word lemmatisation, and part-of-speech
tagging. Latent Semantic Analysis (LSA; Landauer and Dumais 1997), Latent Dirichlet
Allocation (LDA; Blei et al. 2003) and word2vec (Mikolov et al. 2013)modelswere
trained on extensive corpora adapted to specific scenarios. In addition, the
WordNet lexical ontology was used to identify lexical chains (Budanitsky and
set of example essays and their assigned scores to the system. ReaderBench
services provide various textual complexity indices such as (Dascalu et al.
2018): the length of sentences and paragraphs in word and character counts,
statistics with regard to the use of different parts of speech and syntactic
dependencies, semantic cohesion scores, and discourse structure. After training
the essay scoring model, the testing and validation of additional student essays
can be performed. Accuracy scores strongly depend on the text volumes and number of
example documents used for training.
4.6.4 Application case
An example of essay scoring in French language involves the classification of docu-
ments from primary school manuals into five complexity classes (Dascalu et al. 2014).
A successful example of essay scoring in a serious game would be the VIBOA game
(Westera et al. 2008). In this video game, master students adopt the role of an
environmental policy consultancy charged with the investigation of authentic environ-
mental problem cases. As part of the game scenario, students have to summarise and
explain their findings, obtained from a variety of legal documents, calculations and
(simulated) stakeholder interviews (cf. Fig. 3) in scientific reports and upload these to
the game server. Teachers are supposed to manually assess these reports and return the
outcomes to the students in the game. In practice, the manual assessment of many
reports generates an unacceptably high teacher workload. In this game, the
ReaderBench essay scoring software has demonstrated to offer an excellent
replacement, offering high accuracy and considerable workload reduction. It
was shown that the teachers’work load reduces to 68%, while a lower limit
of 90% precision is preserved.
Education and Information Technologies (2020) 25:351–380 365
4.6.5 Technical considerations
The essay scoring service provided by ReaderBench can be accessed in a similar
manner as the sentiment analysis service. Developers can either use pre-trained corpora
and textual complexity models, or they can create their tailored models specific for their
learning requirements. The essay scoring service provides a wide range of textual
complexity indices, freely available for English, French, Dutch, Spanish, Romanian,
and Italian languages.
4.7 NPC: Role play character (FAtiMA)
4.7.1 The FAtiMA toolkit
The FAtiMA Toolkit is an emotion engine for AI Characters (Dias et al. 2014;
Mascarenhas et al. 2018). It is a collection of open-source tools that help researchers,
game developers and roboticists to incorporate a computational model of emotion and
decision-making in their projects. In particular, it enables developers to easily create
Role Play Characters. These are socially intelligent characters with detailed AI modules
that makes them autonomous regarding social interactions.
4.7.2 Relevance for learning and teaching
The added value of socially intelligent characters in a serious game is twofold. First, game
characters that expose believable emotional responses give the illusion of interactions with
real human participants, which deepens the (learning) experiences. This is especia lly
relevant for games that aim to address social skills and communication skills.
In recent years, these skills have been re-established as crucial generic skills for
meeting the demands of the digital age: the so-called twenty-first century skills
366 Education and Information Technologies (2020) 25:351–380
Fig. 3 Screenshot of attending a meeting with experts and stakeholders in the VIBOA game
(Dede 2010). Another most promising application area is the therapy and
training of people with special social needs, for example, children with autism,
who can use games with artificial social characters as a safe environment to
mitigate the anxiety associated with social interactions (Bernardini et al. 2014).
4.7.3 AI approach
The FAtiMAToolkit facilitates the inclusion of a dynamic model of emotions that affects
not just how the character looks and acts but also how the player’s responses are evaluated.
For this, it follows a character-centred approach rather than a plot-centred approach. The
authoring is focused on defining general profiles (a set of rules) of how characters should
respond emotionally in their games across different scenarios and contexts. The main
advantage of this approach is that the characters’behaviours are consistent across different
contexts and no elaborate hard-coding is needed. Reasoner components are used in
conjunction to augment the capabilities of the decision making and emotional responses
of each agent in different socio-cultural contexts. An example is a reasoner
addressing social importance; it allows to create groups of agents that would act
and feel according to different cultural values (Mascarenhas et al. 2016). A second
reasoner, which is named CiF-CK, is based on a model that describes different social
exchanges and its consequences within a social environment (Guimaraes et al. 2017).
The toolkit is modular allowing other types of reasoners to be easily added to the system.
4.7.4 Application case
The FAtiMA Toolkit has been used in various case studies. In the Space Modules Inc.
game, which addresses customer communication skills, the player takes on the role of a
customer services representative working at the helpdesk of a spaceship parts manu-
facturer (Mascarenhas et al. 2018). Customers with a variety of starting moods and
emotional dispositions get in touch with the helpdesk about problems they are
experiencing. The player has to manage diverse situations and has to decide how best
to respond. The FAtiMA Toolkit is used to model the decisions and emotional reactions
of the diverse (virtual) customers, the outcomes of which can be used to change their
on-screen appearances (Fig. 4).
A similar application is the Sports Team Manager game, which is about composing and
managing the best performing sailing team. The player first interviews the various virtual
characters to identify their skills and personalities and then must communicate with the
team, deciding which members are placed into each position per race and resolve conflict
situations as they arise. Again, the FAtiMA Toolkit is used to model the characters’
emotions. Other usage examples are in a Virtual Reality experience designed as police
interrogation exercise, and in robotics: controlling the decisions of two social robots
playing the card game Sueca with two human players, while exposing group-based
emotions (having each robot appraising both its own actions and the actions of its partner).
4.7.5 Technical considerations
The FAtiMAToolkit does not require installation. To facilitate its integration with game
engines it works as a C# library. Each of the toolkit’s component is able to
Education and Information Technologies (2020) 25:351–380 367
fully load and save its internal state to a JSON file, which may be used for
further processing by the game. Although any text editor of choice can be used
for authoring, each component included in the role play character comes with a
dedicated editor, providing a graphical user interface, syntax error detection and
the capability to edit the complex intertwined data structures needed for cover-
ing the characters emotions, autobiographical memory, and appraisal rules,
among other things.
4.8 NPC: Behavior mark-up language realizer
4.8.1 Nonverbal bodily motion: Behaviour mark-up language
The Behavior Mark-up Language (BML) Realizer created by Utrecht University
defines and controls the on-screen representation of virtual characters, in particular
their non-verbal behaviours: facial expressions, body movements, gestures, and gaze,
368 Education and Information Technologies (2020) 25:351–380
Fig. 4 A screenshot from the Space Modules Inc. game, showing one of the customers
respectively. The importance of non-verbal behaviours either from avatars or non-
playing virtual characters should not be underestimated. For inducing intense, realistic
game experiences the challenge is not only to make virtual characters just look like
humans but also to make them behave like humans. The behaviours should provide an
illusion of realism, firstly by demonstrating responsiveness to the actions of players and
other agents in the game, secondly by taking into account the context of operation, and
thirdly, by securing that the behaviours are meaningful and interpretable. In other
words, the displayed behaviours should reflect the inner state of the artificial character
(Thiebaux et al. 2008). Thus, virtual characters should be equipped with properties such
as personality, emotions and expressive non-verbal behaviours in order to engage the
users to the game.
4.8.2 Relevance for learning and teaching
As many serious games rely on experiential learning, which means they aim to provide
intense and meaningful learning experiences and allow the active participation of
players in contexts that in many cases mimic professional practice, a large degree of
realism or authenticity is indicated (Westera et al. 2018). Moreover, the realism
supports the acquisition of tacit, implicit knowledge bound to the experiences and
helps to promote successful transfer to the real world situations. In this respect, the
believability of virtual characters is evident, either as personas in realistic game
scenarios (for instance in a job interview training) or as virtual tutors that guide students
during their game sessions.
4.8.3 AI approach
There are two main approaches for modelling non-verbal behaviours and animations:
rule-based (procedural) and machine learning (Beck et al. 2017; Yumak and Magnenat-
Thalmann 2015), respectively. Rule-based approaches are based on findings from
social sciences and biomechanics. These rules are typically obtained through empirical
analysis of human behaviour. The disadvantage of such methods is that they might not
capture the full complexity of the motion trajectories. However, they provide greater
level of control, while keeping the realism at a sufficient level. Machine learning
approaches automate this process and find regularities and dependencies between
factors using statistics, and they learn from a larger amount of data to cover various
cases. However, obtaining good annotated data is problematic. Moreover, these data
typically apply to the specific conditions of the context where they were collected, but
do not necessarily generalise well. Therefore, a rule-based (procedural) approach was
chosen for the realisation of non-verbal behaviours, providing maximum control in
various application contexts. The rule-based coding approach of the BML Realizer
allows to efficiently define a controlled set of the non-verbal behaviours, while
avoiding the laborious job of separately coding the animations of all non-verbal
behavioural attributes. Behavior Mark-up Language (BML; Kopp et al. 2006)isan
XML based language that is used to model and coordinate speech, gesture, gaze and
body movements (cf. Fig. 5). Each behaviour is divided into six animation phases
bounded with seven synchronisation points: start, ready, stroke-start, stroke, stroke-end,
relax, and end, respectively. Synchrony is achieved by assigning the sync-point of one
Education and Information Technologies (2020) 25:351–380 369
behaviour to the sync-point of another. The behaviour planner that produces the BML
also gets information back from the behaviour realizers about the success and failure of
the behaviour requests.
4.8.4 Application cases
The Virtual Human Controller has been successfully used in various applications. An
example would be the Job-Quest game, which is a full-3D application interview
training game. Also, it has been used for controlling the Virtual Receptionist character
at the entrance of the computer science building at Utrecht University. This set-up
includes a microphone to capture the visitors’speech and a Kinect camera to capture
4.8.5 Technical considerations
The BML realizer can be used in the Unity 3D game engine and allows to define
speech, gaze and gesture animation for a conversational character. The animation
pipeline includes the following steps: 1) Importing a 3D character that supports
animation from an .fbx-file editor, for instance the DAZ3D Editor (or other tools
that can export .fbx files), together with blendshapes for speech and facial
animation and adding it to the Unity project, 2) adding separate animation
controllers for speech, facial animation, gestures and gaze, 3) linking the separate
controllers to the BML Realizer, and 4) writing a BML script to generate multi-
modal synchronised animations. Beyond these functionalities, we have added
Google speech recognition and chatting functionalities using AIML Pandorabots.
Furthermore, we developed a novel autonomous gaze control module based on
Kinect to drive the “look at”behaviour of the virtual character in group-based
interactions using a data-driven approach (Yumak et al. 2017). The BML realizer
has been successfully integrated with the Communicate! dialogue manager from
Utrecht University, allowing for a direct connection between dialogue authoring
and non-verbal expression, and with the Emotion Appraisal component, which is
part of the FATiMA toolkit described above.
370 Education and Information Technologies (2020) 25:351–380
Fig. 5 BML-modelled virtual human capable of speech, gaze and gesture control
4.9 NPC: LipSync generator
4.9.1 Lip-synchronised speech
portant element of believable NPCs and contributes significantly to the illusion of
realism and to accommodating more natural human-computer dialogues.
4.9.2 Relevance for learning and teaching
As explained before, the believability of virtual characters is a relevant contribution to
provoking authentic and effective learning experiences. Lip-synchronised speech
thus readily enhances the quality of either virtual tutors or any virtual charac-
ters in the game scenario.
4.9.3 AI approach
Different approaches to lip-synchronised speech animation have been proposed
over the years. Procedural approaches are a better choice in terms of the control of
the animation, although they may not reach the level of naturalness obtained by
performance-capture and data-driven approaches (Edwards et al. 2016). So far,
none of the proposed procedural approaches (Taylor et al. 2017) explicitly takes
into account the effect of emotions on the mouth movement. Our current contri-
bution entails an audio-driven speech animation method for interactive game
characters where the control aspect is high priority. While doing this, we aim to
push the boundaries of naturalness by introducing the effect of emotions. The
work includes an expressive speech animation model that takes into account
emotional variations in audio and a co-articulation model based on dynamic
linguistic rules varying among different emotions. The component takes as input
text, sends it to a text-to-speech (TTS) system, parses the phonemes, maps them to
visemes (visual counterparts of phonemes) and finally blends them for smooth
speech animation (Fig. 6).
4.9.4 Application cases
The LipSync Generator is mostly used in conjunction with the BML Realizer to setup a
joint Virtual Human Controller. Application cases are the Job-Quest game, mentioned
before, and the Virtual Receptionist character at the entrance of the computer science
building at Utrecht University.
Education and Information Technologies (2020) 25:351–380 371
Fig. 6 Phoneme-to-viseme mapping: “hello”- > h @ l @U - > GK AHH L OHH UUU
4.9.5 Technical considerations
The LipSync Generator is implemented as a Unity 3D plug-in. It currently uses an off-
the-shelf text-to-speech (CereVoice) library. It is also possible to link it with other text-
to-speech systems. Since Unity 3D is multi-platform, the component can be deployed
on different platforms including Web and mobile applications. Similar to the BML
Realizer it directly works with the Communicate! dialogue manager from Utrecht
University, allowing for a direct connection between dialogue authoring and non-
verbal expression, and with the Emotion Appraisal component, which is part of the
FAtiMA toolbox described above.
As was explained in the previous chapter, a set of real-world application pilots
were arranged to accommodate empirical evaluation of the overall concept of
game software reuse. The application pilots were centred around various real-
world games created by professional game studios. The designs of these games
were on the one hand guided by the specific educational contexts, target groups
and learning objectives of the cases, but were also informed by the new function-
alities that RAGE components offer. The evaluations covered all critical aspects of
the process, with a main focus on experiences, usability and (learning) outcomes.
Various detailed publications of these studies are being prepared, while back-
ground information and some key results can already be found in two recent,
technical reports (Bazzanella et al. 2018; Steiner et al. 2018). Feedback on the set
of components was collected from game developers and external users from
academia and game industry, demonstrating good overall usability and confirming
their usefulness, perceived benefits and cost effectiveness for applied game de-
velopment. Benefits-costs ratios for the components were found to be in the 10–
100 range (detailed analysis available at www.gamecomponents.eu/content/604
/cost-benefit-analysis-of-the-rage-case-studies): while development from scratch
would take weeks or months –if possible at all-, the integration of existing
components is a matter of days or even hours. Both component developers and
game developers (23 subjects) appreciated the usability of the component-based
architecture and indicated to keep using the architecture in future projects: the
architecture is lightweight indeed, allowing for the easy conversion of software
into reusable components and making the integration of components with the
game engine an easy job. The application pilots indicated for each component in
chapter 4 involved over 1,500 end-users in various contexts. Overall, game
experiences obtained positive feedback from teachers and players involved; the
games’potential to support learning was well recognised. Significant evidence of
learning gains was demonstrated. The evaluation of search, collaboration, and
course authoring features within the gamecomponents.eu portal among game
developers showed overall positive results and provided useful inputs for the
provisional launch of the portal (2018). Online course authoring, which is sup-
ported by the portal, was even rated as being easier and more comfortable than
courseauthoringinexistinglearningmanagement systems. Overall, the RAGE
372 Education and Information Technologies (2020) 25:351–380
research programme to support the serious game development community by
accommodating the reuse of intelligent software components has been demon-
strated to be fit for purpose. After its launch, traffic to the gamecomponents.eu
portal steadily grew, doubling every two months up to over 6000 visitors by
January 2019 and leading to many hundreds of component downloads.
Sustainable exploitation of the system is carried by the recently launched
RAGE Foundation, which is a not-for-profit alliance of serious game stake-
holders, including key players from industry, education and academia.
This article presented a comprehensive overview of AI advances for serious
games. It described a set of concrete game AI software artefacts that have been
made available on the platform-independent gamecomponents.eu portal. For
each component, a brief explanation was provided, including technical consid-
erations, benefits for serious games, and a description of one or more applica-
tion cases. Three game AI key areas covered are: Player Experience Modelling,
Natural Language Processing and Non-Playing Characters, respectively.
With respect to Player Experience Modelling, it is important to refer to the
ethical issues that may arise when creating detailed user profiles and having
these analysed with smart algorithms that may uncover sensitive personal traits,
behaviours, preferences, capabilities, opinions. Although player modelling is a
key activity for any teacher in a classroom situation, continually checking how
pupils are doing and whether or not they would need specific support, it takes
place in a natural, largely implicit or even intimate way, based on the direct
personal relationships and trust that are developed during the interactions
between teachers and pupils. In contrast, computer traces of pupils’behaviours
are not based on personal relationships and trust, but reflect the relentless
recording of events, indelibly available for mining algorithms and computerised
judgements, the outcomes of which are easily mistaken for absolute truths. As a
consequence, qualitative aspects of teaching and learning are likely to be
overlooked in favour of quantifiable aspects. From a psychological or pedagog-
ical perspective, the idea of being under permanent computer-surveillance may
induce unwanted player behaviours, for instance risk avoiding behaviours or
reduced initiative, which may affect the quality of learning. Important concerns
have been raised because analytics could severely disempower and demotivate
learners when they are provided with continuous feedback about their weak
performances as compared with other students (Westera et al. 2014). Consequently,
ethical, legal and pedagogical considerations draw the line to the practical application of
player experience modelling.
Natural Language Processing applications have demonstrated to greatly en-
hance the quality of assessing the significance of the player’s verbal and written
utterances. Still, as Siri, Google, Assistant, Alexa and other popular virtual
assistants demonstrate, NLP is not without flaws. In addition, NLP applications
may require substantial pre-processing or post-processing that assume specific
Education and Information Technologies (2020) 25:351–380 373
NLP expertise, for instance for the cautious training, testing and validation of
machine learning models, which may pose a barrier for adoption.
Realistic, believable Non-Playing Characters procure a natural fluency in
human-computer interaction, which may lead to engaging learning experiences
and a sense of authenticity offered by the learning environment. But this pursuit
of realism should be put in perspective. Experiments based on media equation
theory (Reeves and Nass 1996) have demonstrated that human individuals
respond socially and naturally to a variety of non-human objects such as robots
and avatars, but also to computers or any graphical objects (cf. the simple ghost
characters in Pacman, which may still raise exciting, dramatic, if not thrilling
experiences). This tendency toward anthropomorphism is explained by the fact
that the human brain simply cannot distinguish between inter-human and
mediated or symbolic interactions, whereby it is not capable of suppressing
natural interpersonal responses in any interaction. A complementary explanation
at the level of conscious thought would be the “willing suspension of disbe-
lief”, which is the well-considered acceptance of unrealistic hypotheses, present
in many fictional works in literature, cinema, and games. Accepting the
(unrealistic) presupposition that Superman can fly, pays off with the rewarding
experience of being carried away by the adventures in the Superman movie,
while its rejection would have spoiled the experience. Likewise, in Pacman we
are ready to accept the idea that a handful of pixels are real ghosts. These
considerations suggest that the level of realism is not always critical. It should be
decided upon at design time, given the specific context, content and purpose of the
For practical reasons, this study has been restricted to the game AI fields of
PEM, NLP and NPC’s. From the wider domain of artificial intelligence various
additional high-potential game AI areas have been identified (Yannakakis and
Togelius 2015), which would open up many new possibilities. These areas
include search and planning (for pathfinding, adaptation and computer playing
strength), procedural content generation (creating design-tailored game contents,
e.g. cities, furnished rooms, people), computational narrative (optimisation pro-
cedures for game storytelling, event generation, generating sequences of game
events, deciding about camera angles), and AI-assisted game design (smart tools
to support creative game design and development, e.g. level design, simulating
playthroughs, game rule design). Altogether, it seems the era of AI is just starting. New
AI concepts and technologies will continue to foster and innovate the domain
of serious gaming.
While focusing on game AI, the initiative described in this article has been a
substantial attempt to bridge the gap between research and industry, resulting in
a knowledge and technology valorisation and transfer mechanism instantiated
by the gamecomponents.eu marketplace portal that has the potential to become
a favourable example of structural research-industry collaboration.
Acknowledgements This work has been partially funded by the EC H2020 project RAGE (Realising an
Applied Gaming Eco-System); http://www.rageproject.eu/; Grant agreementNo 644187 and by national funds
through Fundação para a Ciência e a Tecnologia (FCT-UID/CEC/500 21/2013).
374 Education and Information Technologies (2020) 25:351–380
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
Bachmann, F., Bass, L., Buhman, C., Comella-Dorda, S., Long, F., Robert, J., Sea-Cord, R., & Wallnau, K.
(2000). Technical concepts of component-based software engineering, volume II. Pittsburg: Carnegie
Mellon University, Software Engineering Institute.
Bahreini, K., Nadolski, R., & Westera, W. (2016). Data fusion for real-time multimodal emotion recognition
through webcams and microphones in E-learning. International Journal of Human Computer Interaction,
32(5), 415–430. https://doi.org/10.1080/10447318.2016.1159799.
Bahreini, K., Nadolski, R., & Westera, W. (2017). Communication skills training exploiting multimodal
emotion recognition. Interactive Learning Environments, 25(8), 1065–1082. https://doi.org/10.1080
Bahreini, K., Vegt, W. van der & Westera, W. (2018). A fuzzy logic approach to reliable real-time recognition
of facial emotions. Multimedia Tools and Applications. (Accepted).
Bazzanella, B., Casagranda, M., Molinari, A., Humphreys, S., Sleightholme, G., Lepoivre, O., Halbeher, M.,
Romana, A., Falua, C., Soeiro, C., Guerra, R. & Kommeren, R. (2018). D5.4 –Pilots quality report round
2. RAGE project. https://research.ou.nl/en/publications/d54-pilots-quality-report-round-2. Accessed 12
Beck, A., Yumak, Z. & Magnenat-Thalmann, N. (2017) Body movement generation for virtual characters and
social robots. In: J. K. Burgoon, N. Magnenat-Thalmann, M. Pantic & a. Vinciarelli. Social Signal
Processing (pp. 273-286). Cambridge, MA: Cambridge University press. https://doi.org/10.1017
Bernardini, S., Porayska-Pomsta, K., & Smith, T. J. (2014). ECHOES: An intelligent serious game for
fostering social communication in children with autism. Information Sciences, 264,41–60.
Birman, K., & Joseph, T. (1987). Exploiting virtual synchrony in distributed systems. In Proceedings of the
eleventh ACM symposium on operating systems principles (SOSP '87) (pp. 123–138). New York: ACM.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning
Research, 3(4–5), 993–1022.
Bourassa, M. A. J., & Massey, L. (2012). Artificial intelligence in games. A survey of the state of the art.
Ottawa: Defence R&D Canada Retrieved from http://cradpdf.drdc-rddc.gc.ca/PDFS/unc120/p536670_A1
b.pdf. Accessed 12 July 2019.
Budanitsky, A., & Hirst, G. (2006). Evaluating WordNet-based measures of lexical semantic relatedness.
Computational Linguistics, 32(1), 13–47.
Cer, D., Yang, Y., Kong, S.-Y., Hua, N., Limtiaco, N., St John, R., Constant, N., Guajardo-Cespedes, M.,
Yuan, S., Tar, C., Sung, Y.-H., Strope, B. & Kurzweil, R. (2018). Universal Sentence Encoder. Retrieved
from https://arxiv.org/pdf/1803.11175.pdf. Accessed 12 July 2019.
Champandard, A. J. (2004). AI Game Development. San Francisco: New Riders Publishing.
Claypool, M., Decelle, J., Hall, G. & O'Donnell, L. (2015). Surrender at 20? Matchmaking in league of
legends. In: Proceedings of the IEEE Games Entertainment Media Conference 2015, pp. 1–4.
Dascalu, M., Dessus, P., Trausan-Matu, S., Bianco, M., & Nardy, A. (2013). ReaderBench, an environmentfor
analyzing text complexity and reading strategies. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.),
16th Int. Conf. On artificial intelligence in education, AIED 2013 (pp. 379–388). Memphis, USA:
Dascalu, M., Stavarache, L. L., Trausan-Matu, S., Dessus, P., & Bianco, M. (2014). Reflecting comprehension
through French textual complexity factors. In: 26th International Conference on Tools with Artificial
Intelligence, ICTAI 2014,Limassol, Cyprus (pp. 615–619). New York: IEEE. https://doi.org/10.1109
Dascalu, M., Crossley, S., McNamara, D. S., Dessus, P., & Trausan-Matu, S. (2018). Please ReaderBench this
text: A multi-dimensional textual complexity assessment framework. In S. Craig (Ed.), Tutoring and
intelligent tutoring systems (pp. 251–271). Hauppauge, NY, USA: Nova Science Publishers, Inc..
Dede, C. (2010). Comparing frameworks for 21st century skills. In J. Bellanca & R. Brandt (Eds.), 21st
century skills: Rethinking how students learn (pp. 51–75). Bloomington, IN: Solution Tree Press
Education and Information Technologies (2020) 25:351–380 375
Retrieved from http://sttechnology.pbworks.com/f/Dede_(2010)_Comparing%20Frameworks%20
for%2021st%20Century%20Skills.pdf. Accessed 12 July 2019.
Dias, J., Mascarenhas, S., & Paiva, A. (2014). Fatima modular: Towards an agent architecture with a generic
appraisal framework. In T. Bosse, J. Broekens, J. Dias, & J. van der Zwaan (Eds.), Emotion modeling (pp.
44–56). Cham: Springer.
Dignum, F., Westra, J., Van Doesburg, W. A., & Harbers, M. (2009). Games and agents: Designing intelligent
gameplay. International Journal of Computer Games Technology, 837095,1–18. https://doi.org/10.1155
Edwards, P., Landreth, C., Fiume, E. & Singh, K. (2016). JALI: An animator-centric viseme model for
expressive lip synchronization. ACM Trans. Graph. 35(4), article 127, 1-11.
Elo, A. E. (1978). The rating of chess players, past and present. New York: Arco Pub.
European Commission (1995). EC green paper on innovation. Brussels: European Commission. Retrieved
from http://europa.eu/documents/comm/green_papers/pdf/com95_688_en.pdf. Accessed 12 July 2019.
Fray, A. (2015). Context steering - behavior-driven steering at the macro scale. In: Game AI Pro,Vol.2(pp.
183-193). New York: CRC press.
Fujita, H., & Wu, I.-C. (2012). A special issue on artificial intelligence in computer games: AICG. Knowledge-
Based Systems, 34,1–2. https://doi.org/10.1016/j.knosys.2012.05.014.
Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns: Elements of reusable object-
oriented software. London: Pearson Education.
Gee, J. P. (2003). What video games have to teach us about learning and literacy? New York: Palgrave
Georgiadis, K., van Lankveld, G., Bahreini, K., & Westera, W. (2018). Accommodating stealth assessment in
serious games: Towards developing a generic tool. In: 10th IEEE International Conference on Virtual
Worlds and Games for Serious Applications (VS-Games 2018), pp. 1–4.
Georgiev, A., Grigorov, A., Bontchev, B., Boytchev, P., Stefanov, K., Bahreini, K., Nyamsuren, E., Van der
Vegt, W., Westera, W., Prada, R., Hollins, P. & Moreno, P. (2016). The RAGE Software Asset Model and
Metadata Model. In: Tim Marsh, Minhua Ma, Manuel Fradinho Oliveira, Jannicke Baalsrud Hauge and
Stefan Göbel (Eds.), Serious Games, Proceedings of the Second Joint International Conference, JCSG
2016, Brisbane, QLD, Australia, September 26–27, Lecture Notes in Computer Science 9894 (pp. 191–
203). Cham, Switzerland: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other
neural network architectures. Neural Networks, 18(5), 602–610. https://doi.org/10.1016/j.
Guimaraes, M., Santos, P. & Jhala, A. (2017). CiF-CK: An architecture for social NPCs in commercial games.
In: Proceedings of the IEEE Conference on Computational Intelligence and Games, CIG 2017 (pp. 126–
Gutu, G., Paraschiv, I. C., Dascalu, M., Cristian, G., Trausan-Matu, S., & Lepoivre, O. (2018). Analyzing and
providing comprehensive feedba ck for French CVs with ReaderBench. Scientific Bulletin, University
Politehnica of Bucharest, Series C, 80(2), 17–28.
Guy, S. J. & Karamouzas, I. (2015). Guide to anticipatory collision avoidance, Game AI Pro, Vol. 2 (pp. 195-
208). New York: CRC Press.
Herbrich, R., Minka, T., & Graepel, T. (2006). TrueSkill™: A Bayesian skill rating system. In B. Schöpkopf,
H. C. Platt, & T. Hoffman (Eds.), Advances in neural information processing systems (pp. 569–576).
Cambridge, MA: MIT Press.
Hofman, A. D., Jansen, B. R., de Mooij, S. M., Stevenson, C. E., & van der Maas, H. L. (2018). A solution to
the measurement problem in the idiographic approach using computer adaptive practicing. Journal of
Intelligence, 6(1), 14.
Hühn, J., & Hüllermeier, E. (2009). FURIA: An algorithm for unordered fuzzy rule induction. Data Mining
and Knowledge Discovery, 19(3), 293–319.
Humphreys, T. (2013). Exploring HTN planners through example. In Game AI Pro (pp. 149–167). New York:
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014
Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 (pp. 1746–1751).
Stroudsburg, PA: ACL. 21, 1120, 1142 https://doi.org/10.1109/LSP.2014.2325781.
Klinkenberg, S., Straatemeier, M., & van der Maas, H. L. (2011). Computer adaptive practice of maths ability
using a new item response model for on the fly ability and difficulty estimation. Computers & Education,
376 Education and Information Technologies (2020) 25:351–380
Kopp, S., Krenn, B., Marsella, S., Marshall, A. N., Pelachaud, C., Pirker, H., Thorisson, K. R., &
Vilhjalmsson, H. (2006). Towards a common framework for multimodal generation: The behavior
markup language. In Proceedings of the 6th international conference on intelligent virtual agents,
IVA’06 (pp. 205–217). Berlin/Heidelberg: Springer.
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato's problem: The latent semantic analysis theory of
acquisition, induction and representation of knowledge. Psychological Review, 104(2), 211–240.
Lewis, M. (2017.). Choosing effective utility-based considerations. Game AI Pro, Vol. 3 (pp. 167-178). New
York: CRC press.
Lewis, M. & Dill, K. (2015): Game AI appreciation, revisited. In: Game AI Pro,Vol.2(pp.3-17).NewYork:
CRC press. Retrieved from http://www.gameaipro.com/GameAIPro2/GameAIPro2_Chapter01_Game_
AI_Appreciation_Revisited.pdf. Accessed 12 July 2019.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies,
5(1), 1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016.
Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores; with contributions by Allan
Birnbaum. Reading, MA: Addison-Wesley.
Maher, B. (2016). Can a video game company tame toxic behaviour? Nature, 531(7596), 568–571.
Mascarenhas, S., Degens, N., Paiva, A., Prada, R., Hofstede, G. J., Beulens, A., & Aylett, R. (2016). Modeling
culture in intelligent virtual agents. Autonomous Agents and Multi-Agent Systems, 30(5), 931–962.
Mascarenhas, S., Prada, R., Dias, J., Santos, P. A., Star, K., Hirsh, B., Spice, E., & Kommeren, R. (2018). A
virtual agent toolkit for applied game developers. In C. Browne, M. Winands, J. Liu, & M. Preuss (Eds.),
Proceedings of the IEEE Conference on Computational Intelligence and Games, CIG 2018, Maastricht
(pp. 1–7). New York: IEEE.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representation in vector
space.In Workshop at ICLR. Scottsdale, AZ.
Min, W., Frankosky, M. H., Mott, B. W., Rowe, J. P., Wiebe, E., Boyer, K. E., & Lester, J. C. (2015).
DeepStealth: Leveraging deep learning models for stealth assessment in game-based learning environ-
ments. In C. Conati, N. Heffernan, A. Mitrovic, & M. Verdejo (Eds.), Artificial Intelligence in Education.
AIED 2015. Lecture notes in computer science, Vol 9112 (pp. 277–286). Cham: Springer.
Mislevy, R. J. (2011). Evidence-Centered Design for Simulation-Based Assessment. CRESST Report 800. Los
Angeles, CA: National Center for research on evaluation, standards, and student testing (CRESST).
Moore, G. R., & Shute, V. J. (2017). Improving learning through stealth assessment of conscientiousness. In
A. Marcus-Quinn & T. Hourigan (Eds.), Handbook on digital learning for K-12 schools (pp. 355–368).
Cham: Springer. https://doi.org/10.1007/978-3-319-33808-8.
Nguyen, T. D., Chen, Z. & El-Nasr, M. S. (2015). Analytics-based AI techniques for a better gaming
experience. Game AI Pro, Vol. 2 (pp. 481–500). New York: CRC Press.
Nyamsuren, E., van der Vegt, W., & Westera, W. (2017). Automated adaptation and assessment in serious
games: A portable tool for supporting learning. In M. Winands, H. van den Herik, & W. Kosters (Eds.),
Advances in Computer Games, ACG 2017. Lecture notes in computer science, Vol. 10664 (pp. 201–212).
Cham: Springer. https://doi.org/10.1007/978-3-319-71649-7_17.
Nyamsuren, E., van der Maas, H. L. J. & Maurer, M. (2018a). Set-theoretical and combinatorial instruments
for problem space analysis in adaptive serious games. International Journal of Serious Games, 5 (1), 5-
Nyamsuren, E., van der Vegt, W. & Westera, W. (2018b). A fuzzy rule for adaptive gaming to reduce problem-
selection Bias and improve automated difficulty rating. IEEE Transactions on Learning Technologies
(accepted for publication).
Pantic, M., Sebe, N., Cohn, J. F., & Huang, T. (2005). Affective multimodal human-computer interaction. In
H. Zhang, T.-S. Zua, R. Steinmetz, M. Kankanhalli, & L. Wilcox (Eds.), Proceedings of the 13th Annual
ACM International Conference on Multimedia, Hilton, Singapore (Vol. 5, pp. 669–676). New York:
Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/
motivational mediators. Journal of Applied Psychology, 41,359–376.
Pentheny, G. (2013). Efficient crowd simulation for Mobile games. In Game AI Pro (pp. 317–323). New
Yor k : C R C P r ess.
Pentheny, G. (2015). Advanced techniques for robust, efficient crowds. In: Game AI Pro, Vol. 2 (pp. 173-181).
New York: CRC press.
Rabin, S. (2017): The illusion of intelligence. In: Game AI Pro, Vol. 3 (pp. 3-9). New York: CRC press.
Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media
like real people and places. Cambridge: Cambridge University Press.
Education and Information Technologies (2020) 25:351–380 377
Robbins, M. (2013): Using neural networks to control agent threat response. In: Game AI Pro (pp. 391-398).
New York: CRC press. Retrieved from http://www.gameaipro.com/GameAIPro/GameAIPro_Chapter30_
Using_Neural_Networks_to_Control_Agent_Threat_Response.pdf. Accessed 12 July 2019.
Sabourin, J. L., Shores, L. R., Mott, B. W., & Lester, J. C. (2013). Understanding and predicting student self-
regulated learning strategies in game-based learning environments. International Journal of Artificial
Intelligence in Education, 23(1–4), 94–114.
Schuller, D., & Schuller, B. W. (2018). The age of artificial emotional intelligence. Computer, 51(9), 38–46.
Sebe, N. (2009). Multimodal interfaces: Challenges and perspectives. Journal of Ambient Intelligence and
Smart Environments, 1(1), 23–30.
Shannon, C. E. (1950). Programming a Computer for Playing Chess. The London, Edinburgh, and Dublin
Philosophical Magazine and Journal of Science, 41(314), 256–275.
Shute, V. J. (2011). Stealth assessment in computer-based games to support learning. Computer games and
instruction, 55(2), 503–524.
Shute, V. J., Ventura, M., & Kim, Y. J. (2013). Assessment and learning of qualitative physics in newton's
playground. Journal of Educational Research, 106(6), 423–430.
Shute, V. J., Wang, L., Greiff, S., Zhao, W., & Moore, G. (2016). Measuring problem solving skills via stealth
assessment in an engaging video game. Computers in Human Behavior, 63, 106–117.
Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y. & Potts, C. (2013). Recursive Deep
Models for Semantic Compositionality Over a Sentiment Treebank. Proceedings of the 2013 Conference
on Empirical Methods in Natural Language Processing (pp. 1631–1642). Stroudsburg, PA: ACL.
Retrieved from http://www.aclweb.org/anthology/D13-1170.Accessed12July2019.
Steiner, C., Gaisbachgrabner, K., Nussbaumer, A., Mertens, J., Hemmje, M., Nadolski, R. J., Westera, W.,
Bazzanella, B., Casagrande, M., Moinari, A., Humphreys, S., Mascarenhas, S., Guimarães, M., Prada, R.
& Santos, P. A. (2018). D8.4 –Second RAGE Evaluation Report. RAGE project. https://research.ou.
nl/en/publications/d84-second-rage-evaluation-report. Last accessed June 24, 2019.
Stewart, J., Bleumers, L., Van Looy, J., Mariën, I., All, A., Schurmans, D., Willaert, K., De Grove, F., Jacobs,
A. & Misuraca, G. (2013). The Potential of Digital Games for Empowerment and Social Inclusion of
Groups at Risk of Social and Economic Exclusion: Evidence and Opportunity for Policy. C. Centeno
(Ed.), Joint Research Centre, European Commission. Retrieved from http://ipts.jrc.ec.europa.
eu/publications/pub.cfm?id=6579. Accessed 12 July 2019.
Tai, K. S., Socher, R. & Manning, C. D. (2015). Improved semantic representations from tree-structured Long
short-term memory networks. Proceedings of ACL (pp. 1556–1566). Stroudsburg, PA: ACL. https://doi.
Taylor, S., Kim, T., Yue, Y., Mahler, M., Krahe, J., Rodriguez, A. G., Hodgins, J. & Matthews, I. (2017). A
deep learning approach for generalized speech animation. ACM Trans. Graph. 36(4), article 93.
Thiebaux, M., Marsella, S., Marshall, A. N. & Kallmann, M. (2008). Smartbody: Behavior realization for
embodied conversational agents. In: Proceedings of the 7th International Joint Conference on
Autonomous Agents and Multiagent Systems - Volume 1, ser. AAMAS '08, Estoril, Portugal.Richland,
SC: International Foundation for Autonomous Agents and Multiagent Systems (pp. 151-158). Retrieved
from https://pdfs.semanticscholar.org/2ba6/7705aa5d45ea3cff74104d0cb9082dd7add7.pdf. Accessed 12
Van den Berg, J., Guy, S. J., Lin, M. & Manocha, D. (2011). Reciprocal n-body collision avoidance. In: C.
Pradalier, R. Siegwart & G. Hirzinger (Eds.), Robotics Research. Proceedings of the 14th International
Symposium of Robotic Research, ISRR 2009 - Lucerne, Switzerland. Springer tracts in advanced robotics,
Vol. 70 (pp. 3-19). https://doi.org/10.1007/978-3-642-19457-3_1. Retrieved from http://gamma.cs.unc.
Van der Maas, H. L. J., & Nyamsuren, E. (2017). Cognitive analysis of educational games: The number game.
Topics in Cognitive Science, 9, 395–412. https://doi.org/10.1111/tops.12231.
Van der Vegt, W., Nyamsuren, E., & Westera, W. (2016a, 6-7 June). RAGE reusable game software
components and their integration into serious game engines. In: G. M. Kapitsaki & E. Santana de
Almeida (Eds.), Bridging with Social-Awareness, 15th International Conference, ICSR 2016, Limassol,
Cyprus. Proceedings, lecture notes in computer science, Vol. 9679 (pp. 165-180). Cham: Springer.
van der Vegt, W., Westera, W., Nyamsuren, E., Georgiev, A., & Martínez Ortiz, I. (2016b). RAGE architecture
for reusable serious gaming technology components. International Journal of Computer Games
Technology, Article ID, 5680526,1–11. https://doi.org/10.1155/2016/5680526. Accessed 12 July 2019.
Ventura, M., Shute, V., & Small, M. (2014). Assessing persistence in educational games. Design recommen-
dations for adaptive intelligent tutoring systems. Learner Modeling, 2,93–101.
378 Education and Information Technologies (2020) 25:351–380
Vygotsky, L. S. (1978). Interaction between learning and development. In L. S. Vygotsky (Ed.), Mind and
society (pp. 79–91). Cambridge MA: Harvard University Press.
Westera, W., Nadolski, R., Hummel, H., & Wopereis, I. (2008). Serious games for higher education: A
framework for reducing design complexity. Journal of Computer Assisted Learning, 24(5), 420–432.
Westera, W., Nadolski, N., & Hummel, H. (2014). Serious gaming analytics: What students´ log files tell us
about gaming and learning. International Journal of Serious Games, 1(2), 35–50.
Westera, W., Dascalu, M., Kurvers, H., Ruseti, S., & Trausan-Matu, S. (2018). Automated essay scoring in
applied games: Reducing the teacher bandwidth problem in online training. Computers & Education, 123,
Yannakakis, G. N. (2012). Game AI revisited. Proceedings of the 9th Conference on Computing Frontiers,
Cagliari (pp. 285–292). New York: ACM. https://doi.org/10.1145/2212908.2212954.
Yannakakis, G. N., & Togelius, J. (2015). A panorama of artificial and computational intelligence in games.
IEEE Transactions on Computational Intelligence and AI in Games, 7(4), 317–335.
Yannakakis, G. N. & Togelius, J. (2018). Artificial intelligence and games.Berlin: Springer. Retrieved from
http://gameaibook.org/. Accessed 12 July 2019.
Yumak, Z., & Magnenat-Thalmann, N. (2015). Multi-modal and multi-party social interactions. In N.
Magnenat-Thalmann, J. Yuan, D. Thalman, & B. You (Eds.), Context aware human-robot and human-
agent interaction (pp. 275–298). Cham: Springer International Publishing.
Yumak, Z., van den Brink, B., & Egges, A. (2017). Autonomous social gaze model for an interactive virtual
character in real-life settings. Computer Animation and Virtual Worlds, 3(4), e1757. https://doi.
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