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Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.
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April 2016 | Volume 3 | Article 141
published: 11 April 2016
doi: 10.3389/frobt.2016.00014
Frontiers in Robotics and AI
Edited by:
Carlos Gershenson,
Universidad Nacional Autónoma de
México, Mexico
Reviewed by:
Mahendra Piraveenan,
University of Sydney, Australia
Matjaž Perc,
University of Maribor, Slovenia
Heiko Hamann
Specialty section:
This article was submitted to
Computational Intelligence,
a section of the journal
Frontiers in Robotics and AI
Received: 30January2016
Accepted: 14March2016
Published: 11April2016
HamannH, KhalufY, BotevJ,
Divband SooratiM, FerranteE,
KosakO, MontanierJ-M,
MostaghimS, RedpathR, TimmisJ,
VeenstraF, WahbyM and ZamudaA
(2016) Hybrid Societies: Challenges
and Perspectives in the Design
of Collective Behavior in
Self-organizing Systems.
Front. Robot. AI 3:14.
doi: 10.3389/frobt.2016.00014
Hybrid Societies: Challenges
and Perspectives in the Design
of Collective Behavior in
Self-organizing Systems
Heiko Hamann
* , Yara Khaluf
, Jean Botev
, Mohammad Divband Soorati
Eliseo Ferrante
, Oliver Kosak
, Jean-Marc Montanier
, Sanaz Mostaghim
Richard Redpath
, Jon Timmis
, Frank Veenstra
, Mostafa Wahby
and Aleš Zamuda
Department of Computer Science, Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany,
iMinds, Ghent
University, Ghent, Belgium,
Computer Science and Communications Research Unit, Faculty of Science, Technology and
Communication, University of Luxembourg, Luxembourg City, Luxembourg,
Laboratory of Socioecology and Social
Evolution, Department of Biology, KU Leuven, Leuven, Belgium,
Institute for Software & Systems Engineering, University of
Augsburg, Augsburg, Germany,
Department of Computer Applications in Science & Engineering, Barcelona
Supercomputing Center (BSC), Barcelona, Spain,
Faculty of Computer Science, Otto von Guericke University Magdeburg,
Magdeburg, Germany,
Department of Electronics, University of York, York, UK,
Robotics, Evolution and Art Laboratory,
IT-University of Copenhagen, Copenhagen, Denmark,
Computer Architecture and Languages Laboratory, Faculty of
Electrical Engineering and Computer Science, Institute of Computer Science, University of Maribor, Maribor, Slovenia
Hybrid societies are self-organizing, collective systems, which are composed of different
components, for example, natural and artificial parts (bio-hybrid) or human beings inter-
acting with and through technical systems (socio-technical). Many different disciplines
investigate methods and systems closely related to the design of hybrid societies.
A stronger collaboration between these disciplines could allow for re-use of methods and
create significant synergies. We identify three main areas of challenges in the design of
self-organizing hybrid societies. First, we identify the formalization challenge. There is an
urgent need for a generic model that allows a description and comparison of collective
hybrid societies. Second, we identify the system design challenge. Starting from the
formal specification of the system, we need to develop an integrated design process.
Third, we identify the challenge of interdisciplinarity. Current research on self-organizing
hybrid societies stretches over many different fields and hence requires the re-use and
synthesis of methods at intersections between disciplines. We then conclude by pre-
senting our perspective for future approaches with high potential in this area.
Keywords: hybrid society, bio-hybrid, distributed, collective, self-organization, design, interdisciplinarity
is paper originates from a small international workshop on “Methods for Self-Organizing
Distributed Systems” that was held in Laubusch, Germany, during October 2015. We name sev-
eral challenges and give our perspectives for the eld of hybrid societies [cf. Eiben (2014) and
Prokopenko (2014)]. In general, hybrid societies are made of dierent components instead of hav-
ing a homogeneous identity. We call them “societies” because the components possess individual
agency and interact persistently. Such societies can be comprised both natural and articial agents
FIGURE 1 | Overview of design challenges in hybrid societies: primary
challenge A–formalization, primary challenge B–system design, and
primary challenge C–interdisciplinarity.
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | April 2016 | Volume 3 | Article 14
(Baxter and Sommerville, 2010; Halloy etal., 2013; Schmickl etal.,
2013; Hamann etal., 2015) or dierent types of articial agents
only (Dorigo etal., 2013). We focus on self-organizing collective
hybrid societies that are characterized by multiple interactions
of agents, positive and negative feedback processes, and uctua-
tions (Ashby, 1947; Bonabeau etal., 1999; Camazine etal., 2001;
Omicini and Viroli, 2011; Heylighen, 2016). Oen, these systems
show collective behavior indicated by the emergence of global
spatial and/or temporal patterns (Serugendo etal., 2006; Attanasi
et al., 2014; Popkin, 2016). Furthermore, hybrid societies are
describable on a microscopic level, the level of an individual agent,
and a macroscopic level, the level of the whole society (Schelling,
1978; Alexander etal., 1987; Schillo etal., 2000; Hamann etal.,
2014). We want to design and determine the articial part of these
systems, although the articial subpopulation is in contact with a
natural subpopulation in hybrid societies.
Typical examples of hybrid societies are investigated in the pro-
ject ASSISI|
(Schmickl etal., 2013) where robots closely interact
either with groups of bees or sh. Such systems require dierent
approaches than those developed for multi-agent systems because
they are heterogeneous and while the robots are variably program-
mable the biological agents (bees and sh) have a determined
behavior. e ASSISI|
system heavily relies on social aspects
because the robots need to learn the “social language” (Schmickl
etal., 2013) of bees/sh to trigger desired behaviors. It is a hybrid
system because the robot–animal interaction is not only in one
way but also the animals determine the systems further devel-
opment. Similarly, we have hybrid societies in socio-technical
systems where human beings closely interact with technological
artifacts (Baxter and Sommerville, 2010; Smirnov et al., 2014;
DOrsogna and Perc, 2015; Helbing etal., 2015).
We identify three common, primary challenges in the design
of hybrid societies (see Figure1). Each is discussed in detail, sup-
plemented by additional secondary challenges, and we give our
perspective on future approaches with high potential.
e analysis of hybrid societies using tools of mathematics
and computer science is essential to gain deep insights into the
dynamics and prominent principles of hybrid systems. Besides
allowing for predictions, the formal approach also guides ones
thoughts when designing hybrid societies. e formalization of
hybrid societies is the precondition to move from formal speci-
cations to an integrated design process.
2.1. Purpose of Formalization
From our experience in work with collective hybrid societies, we
have the strong belief that our eld of research requires a tremen-
dous eort to develop a generic model. Hence, a grand challenge
of the design of collective behavior in hybrid societies is to develop
an appropriate generic formalization. A truly generic formal model
would overcome the diversity of methods and models in the eld.
If not completely generic, we would at least require a methodology
that allows to model a large range of dierent collective hybrid
societies. e purpose of a generic model is to understand the
desired system and to gain deep insights. Formalization is neces-
sary to achieve a good understanding of a systems inner dynamics
and, if possible, to predict its outcome. With the optimal model,
we could predict future behaviors and eects of hybrid societies.
Such a model would permit to analyze a wide variety of collective
systems, enable rigorous mathematical comparisons, and help to
understand potential problems in system design before realiza-
tion in simulation, and hardware was achieved.
2.2. Requirements and Actions for a
e formalization approach should be generic and applicable in
many domains sharing essential system features. e develop-
ment of such modeling techniques requires, however, to unify
methods, concepts, and denitions from many dierent elds.
It requires a high degree of integration, knowledge about each of
these domains, and a high convertibility of the model. First steps
toward a unied methodology have been made, for example, in
the elds of socio-technical systems (Baxter and Sommerville,
2010; Jones etal., 2013; Schöttl and Lindemann, 2015) and swarm
robotics (Lerman et al., 2005; Brambilla et al., 2013). Models
originating from natural sciences are limited in representing
typical abilities of agents and also modeling the emergence of
self-organizing artifacts is challenging (see Sec.2.6). A generic
framework reecting domain-specic characteristics while
accurately capturing the evolution and dynamics of collective
behavior, both on the micro- and macroscopic level, needs to be
2.3. Secondary Challenge: Diversity
of Methods
Depending on the system modeled, as well as the type of questions
asked, multiple approaches have been developed ranging from
purely mathematical equations to spatial multi-agent systems.
TABLE 1 | Diversity of methods for the formalization of hybrid societies.
Physics Biology/swarm intelligence Engineering Computer science Networks
Spontaneous magnetization,
laser theory (Yang, 1952;
Haken, 1971)
Animal groups (Okubo, 1986; Buhl
etal., 2006; Edelstein-Keshet, 2006)
Swarm robotics (Martinoli etal.,
2004; Winfield etal., 2005; Prorok
etal., 2011; Brambilla etal., 2013)
Amorphous computing
(Abelson etal., 2000)
Scale-free random networks
(Barabási and Albert, 1999;
Barabási etal., 1999)
Percolation, diffusion-limited
aggregation (Witten and
Sander, 1981; Grimmett, 1999)
Swarm intelligence (Bonabeau
etal., 1999; Dorigo and Caro, 1999;
Kennedy and Eberhart, 2001)
Sensor/actuator networks (Beal
and Bachrach, 2006; Correll etal.,
computation (Payton etal.,
2001; Stepney, 2007)
Temporal networks (Holme
and Saramäki, 2012)
Self-driven particles (Vicsek
etal., 1995)
Opinion dynamics (Schelling,
1978; Galam and Moscovici, 1991;
Hegselmann and Krause, 2002)
Distributed robotics (Weiß, 1996;
Parker, 2000; Stone and Veloso,
Natural computation
(Castro, 2007)
Ad hoc networks (Bettstetter,
2004), network simulations
(McCanne etal., 1997)
In physics, a major achievement is the macroscopic description of many-particle systems with multiple stochastic interactions. In mathematical biology and swarm intelligence, a
relatively high variety of non-linear agent behaviors is integrated in macroscopic models. In engineering, methodologies to design appropriate microscopic behaviors have been
defined. Computer science provides appropriate programing paradigms, which help to find a general access to hybrid societies by the computation paradigm. In network theory, an
outstanding achievement is the generality of results concerning complex networks, which can serve as a role model here. General network models helped to detect basic principles
that have applications across many fields (Barabási and Albert, 1999).
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | April 2016 | Volume 3 | Article 14
e total amount of modeling and investigation techniques for
homogeneous and heterogeneous collective systems is huge and
spans elds such as collective animal behavior, statistical physics,
network theory, control theory, opinion dynamics, and diverse
subelds of computer science. In order to give a little, incomplete
overview, we cite only a few of these, see Table 1. Despite the
strict column-wise presentation of methods, there exist already
approaches that combine several methods from dierent elds,
such as the combination of game theory with networks (Perc and
Szolnoki, 2010), percolation and networks (Piraveenan et al.,
2013a), and hybrid systems with temporal networks (Boerkoel and
Durfee, 2013). Furthermore, the eld of evolutionary game theory
investigates hybrid societies, especially the interaction of agents
also with reference to collective behavior and self-organization
(Perc and Szolnoki, 2010; Perc and Grigolini, 2013). However, the
developed models oen abstract away proximate mechanisms,
that is, the behavioral rules that generate the spatio-temporal
dynamics of collective systems (André, 2014). Partially due to
the extreme diversity of methods, it is dicult to compare hybrid
societies or their models. A generic, formal modeling approach of
collective hybrid societies would help to overcome that problem.
2.4. Secondary Challenge: System
Another challenge is the complexity of hybrid societies due to
self-organization that contains by denition a multitude of locally
interacting agents. Local interactions between agents create
dynamic environments, which are complex to model. e agents
operate locally but can trigger emergent global patterns; we have
dierent types of agents, and they oen live in dynamic environ-
ments, which are challenging to model.
For example, a diculty specic to self-organization is to
link the model that describes the global behavior of the system
to the model that describes the behavior of the individuals.
Dening the so-called micro–macro link is a fundamental issue
in both directions (Schelling, 1978; Hamann and Wörn, 2008).
Macro-to-micro means that a certain global behavior is required;
however, the respective individual behaviors are unknown.
Micro-to-macro is the challenge of predicting the macro-behavior
for a given micro-behavior. Particular internal states of these
agents may be essential, e.g., the internal energy levels are
crucial especially in ying agents (e.g., quadrocopters) or forest
ecosystems (Zamuda and Brest, 2013). e formal approach has
to address these internal states and model their dynamics. Local
and global correlations between these internal states add another
In summary, we have the dynamics of the internal states and
local interactions of individual agents on the one side and the
overall dynamics of the global system on the other side. e chal-
lenge is to nd the link between these two sides, which is key to
understand and formalize hybrid societies.
2.5. Shortcomings in the State of the Art
e vast number of methods of hybrid societies comes with
individual shortcomings. We discuss only a few that may serve
as representative examples. e methods of formal specica-
tion from the eld of soware engineering [e.g., see Hoare
(1978) and Jackson (2006)] are challenged by the number of
interacting entities and their local interactions because the
size of state space grows with the size of a collective (Brambilla
etal., 2014). When this is coupled with the complexity of the
dynamic environments that we typically expect these agents to
exist in, we rapidly nd ourselves in need of novel techniques
to model and explain the dynamics of our systems. Concise
mathematical descriptions of systems, such as methods from
chemistry (van Kampen, 1992), are typically incapable to
model complex agent-to-agent interactions, especially in the
case where spatiality plays a central role (Ohkubo etal., 2008).
Computational models oen require rather strong abstractions
for the sake of run-time eciency. Agent-based models typi-
cally require an increased number of parameters with increas-
ing system complexity which challenges their signicance
(Mayer etal., 2010).
2.6. Our Perspective and Approaches
Engineered hybrid societies are complex, and therefore it is
dicult to develop de novo novel mathematical formalisms.
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | April 2016 | Volume 3 | Article 14
A common option is to use frameworks that were developed for
natural systems to formalize articial systems when they share
key features. In general, two aspects are formalized: (1) the
behavioral mechanisms themselves (at microscopic or macro-
scopic level) and (2) the process that leads to these mechanisms
(e.g., evolution in natural systems, machine learning in articial
Chemistry and statistical physics provide formal, mechanistic
descriptions of hybrid systems. ey are the disciplines that
inspired, for example within swarm robotics, the most commonly
used modeling frameworks (Brambilla etal., 2013), such as the
master equation approach from chemistry (Martinoli etal., 1999)
and use of Fokker–Planck and Langevin equations from statisti-
cal physics (Hamann and Wörn, 2008). However, the main chal-
lenge consists in going beyond the typical assumptions of these
approaches that are intrinsic for large numbers of components
[“Avogadro-large,” cf. Beni (2005)] and lack capabilities to model
cognition and communication. Hence, collaborations with physi-
cists and theoretical chemists could help to extend these models,
to account for smaller system sizes, and to model cognition, and
to explicit communication.
Less attention has been paid to the formalization of processes
leading to self-organization as done in theoretical evolutionary
biology and machine learning. In the rst case, evolutionary
game theory (Nowak, 2006) with innite (e.g., dierential
equations) and nite (e.g., birth–death processes) populations
provides promising approaches but is limited to the evolution of
nite discrete strategies, rather than continuous behavioral traits.
Reinforcement learning is a framework suited for single-agent
systems (Kaelbling et al., 1996) and in some cases collective
systems (Wolpert and Tumer, 1999). In multi-agent settings,
machine learning struggles with the combinatorial explosion
of possibilities, which is usually approached with sophisticated
methods that reduce the search space (Matarić, 1997). To the best
of our knowledge, machine learning techniques have never been
extended to hybrid societies.
Even if we assume that we have a formal specication of our
hybrid society already, then the actual system design is still a
big challenge. We would like to dene an integrated process that
implements the step from a specication of a self-organizing
collective system to the actual real-world system and its deploy-
ment in the eld. In addition, we have to consider typical
requirements for engineered systems, such as safety, reliability,
and stability. Also note that we consciously take an engineer-
ing perspective on hybrid societies, hence assuming that such
self-organizing collective systems can actually be designed.
is hypothesis is in line with assumptions made in standard
approaches, such as swarm robotics (Martinoli, 1999; Brambilla
etal., 2013). However, one can also take the perspective that
self-organizing systems can at most be guided but not fully
determined (Prokopenko, 2009).
3.1. Requirements and Actions
for System Design
Moving from a specication of a hybrid society to a veried
implementation on actual hardware remains dicult. Dealing
with issues such as time, non-determinism, and scale presents
signicant challenges to formal methods. Hybrid societies can be
designed with a smaller eort for pre-specied environments but
for real-world implementations quality characteristics have to be
determined (Mahendra Rajah etal., 2005; Levi and Kernbach,
2010; Brambilla etal., 2013). Formal methods help to develop
tools that ensure system properties, a level of safety, and guaran-
teed safe soware from specication to implementation.
e design for reliability and stability needs to be addressed
before we are able to deploy many hybrid societies in the real
world. e stochasticity and the autonomy present in such sys-
tems make assuring reliability a dicult task. erefore, develop-
ing such systems needs to provide evaluation tools that allows for
measuring those aspects in a representative way.
3.2. Secondary Challenge: Stochasticity,
Uncertainty, Unpredictability
Most real-world environments show a high degree of stochastic-
ity, which makes it challenging to deploy hybrid societies in real-
world applications. We need methodologies to deal with known
uncertainties but also to deal with unforeseen uncertainties. For
collective behaviors, we are missing a general model that could be
used to verify the system against the expected behaviors. In addi-
tion, there might be even unpredictable behaviors [cf., emergent
behavior Matarić (1993) and Bedau (2002)] that prevent us from
assuring that the system never leaves the set of safe states.
3.3. Secondary Challenge: Dynamic
Environments, Run-Time Decisions,
and Open Systems
Related to the above complex problems, we also face the chal-
lenge of dynamic environments that require non-trivial run-time
decisions of our system. Run-time decisions and coupling the
collective hybrid society with other systems at run-time require
new methodologies. Especially systems with high requirements
for robustness operating in dynamic environments have to be
able to appropriately self-adapt their behaviors and organization
structure (e.g., topology). e required time for non-productive
reorganization and adaptation processes should be minimal.
If we allow dynamic changes of the system size, that is, we have
an open system, then we need to tackle the challenge of scalability
at runtime as well. is adds additional uncertainties introduced
by added or removed system components. ese changes need to
be balanced by the system at run-time to establish a stable and
robust system behavior. We oen face diculties when attempt-
ing to make guarantees about the behaviors of our systems and
in the scenarios when existing techniques can be used they oen
model a xed number of agents, making our proofs meaningless
as the size of our collective changes dynamically.
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | April 2016 | Volume 3 | Article 14
3.4. Secondary Challenge: Design of
Feedbacks for Self-Organization and
User Feedback
Natural collective systems exhibit dierent features that are
remarkable, such as exibility, adaptability, and robustness. To
achieve these through self-organization, they resort to positive
and negative feedback mechanisms, the ability to amplify and
weaken local individual decisions. e careful design of appropri-
ate feedback processes requires special attention and sophisticated
design methods. Besides behavioral feedbacks, collective systems
also rely on certain network topologies and network properties,
such as power-law degree distributions (scale-free networks),
that increase the systems robustness to the loss of connections
(Albert etal., 2000; Crucitti etal., 2003; Piraveenan etal., 2013b).
Another feature is that of scale-free correlations (Cavagna
etal., 2010), which is the ability of collective systems to inu-
ence far-away neighbors independently of the system size, by
still resorting to local interactions only. Besides research on
modulating positive feedback (Valentini etal., 2014), the negative
feedback and scale-free correlations have received little attention
yet and are challenging.
A notable quality of deployed systems is user behavior feeding
back steadily into the system. is inevitably entails risks such
as collusion, free-riding, or other exploitative and destabiliz-
ing actions. e additional challenges, for example in terms of
robustness and reliability, therefore need to be considered and
rmly rooted in the system design.
3.5. Our Perspective and Approaches
Once deployed in the eld, bugs are likely to appear in ways
unforeseen by the formalization process. is limitation of the
formalization task is termed reality gap in robotics and has been
studied in recent years. Solutions range from the restriction of the
search space (Koos etal., 2013; Cully etal., 2015) to the design
of behaviors during the deployment of the system (Watson etal.,
2002; Bredeche and Montanier, 2010). e design of a hybrid-
society system could benet from these approaches.
In order to allow our system to adapt to changes in its dynamic
environment, it requires a sucient degree of freedom enabling
it to self-optimize and to show reliable behavior. We need to
allow for methods of self-repair (Ismail and Timmis, 2010) and
self-sustainability (Bredeche and Montanier, 2010), which adds
even more complexity to the system and increases the challenge
of system design. Incorporating the capability for autonomous
reasoning (Anshakov and Gergely, 2010) certainly improves the
system but at rst it increases its complexity.
As the reliance on knowledge gained from other scientic disci-
plines grows, so too does the need for researchers from all elds
to be prepared to learn from the insights and techniques of oth-
ers. e investigated problems are becoming too complex to stay
within the scope of a single discipline, and hence, interdisciplinary
research is becoming more popular (Helbing etal., 2015). Hybrid
societies are an inherently interdisciplinary problem domain,
thus the inclusion of ndings from various disciplines is essential
for their structural and algorithmic design [e.g., combination of
results from plant science, robotics, and architecture (Hamann
et al., 2015)]. Interdisciplinarity is crucial to produce a valid
model of a system observed in another discipline, or to take inspi-
ration from another discipline in the design of systems. From an
engineering perspective, being inspired by biology, chemistry,
and sociology is becoming common place. However, engaging
in a meaningful way with another discipline can be challenging
and oen, not fruitful in part because an approach remains rather
supercial where an extra eort with additional overhead would
have been required.
4.1. Requirements and Actions
for Interdisciplinarity
Engineering has much to oer to the life sciences, but benets of
engagement must be bi-lateral, so that all disciplines benet from
the collaboration. In particular, the contribution of computer
science should go beyond that of a mere service to life sciences
but instead establish a bidirectional interaction that also scien-
tically enriches computer science. For example in the context of
bio-hybrid societies, modeling and simulation can be an eective
vehicle for collaborations between computer scientists (e.g.,
multi-agent simulations) and biologists (e.g., behavioral models),
with computational models being useful to help understand chal-
lenges in behavioral biology, yet providing a formal background
and inspiration to the creation of an articial system, for example
based on behavioral models of animals (Schmickl and Hamann,
2011) or growth models of plants (Zamuda and Brest, 2013;
Hamann etal., 2015). We should try to get inspiration from biol-
ogy and sociology while lending our skill sets to the understand-
ing of other elds. However, interdisciplinary research in hybrid
societies has proven to be challenging.
4.2. Secondary Challenge:
Common Language
Despite our best will to ensure interdisciplinarity, it remains
dicult to achieve in practice. ese diculties stem from the
disparity in vocabulary, the dierent methodologies used, and a
general lack of understanding of the way of thinking and the tools
available on each side. Time is needed to develop an interdiscipli-
nary collaboration. A common language needs to be developed so
that deep and meaningful collaborations are possible.
4.3. Secondary Challenge: Integration
of Methods
Once a simple mutual understanding of the available methods
and present problems is obtained, it is tempting to merely transfer
a method from one eld to the other and to directly apply it to a
particular problem. However, mastering the complex problems at
hand and lastingly improving these systems goes beyond applying
existing results but requires true interdisciplinary collaboration.
Providing a broad set of insightful tools, only highly integrated
research on novel systems leads to a meaningful design method
Hamann et al.
Hybrid Societies
Frontiers in Robotics and AI | April 2016 | Volume 3 | Article 14
for hybrid societies. Prime examples of successful integration
of methods are the integration of robots and sh (Marras and
Porri, 2012) and the automatic analysis of social networks in
honeybees (Wario etal., 2015). Again, establishing such a deep
understanding of the other eld requires time.
4.4. Secondary Challenge: Interdisciplinarity
in a Mono-Disciplinary World
Despite the success of interdisciplinary research and a lot of hype
and lip service in favor of interdisciplinarity, realities still look
dierent. Many institutions and traditions in research are still
forming tiny mono-disciplinary worlds. Hence, there is a chal-
lenge for individual researchers to fulll their own disciplines
requirements in terms of measures of success.
4.5. Our Perspective and Approaches
A probably obvious solution is to enable the human factor and to
form small, strongly linked teams that work interdisciplinarily. In
addition, interdisciplinary researchers should receive an elabo-
rate training for the eld they are collaborating with. en the
methods that are used to design solutions for dierent problems
should transgress disciplinary bounds, in order to allow re-use of
methods across elds of research.
Similarly to the situation when travelers have to adapt to local
customs, all involved parties need to compromise. e common
vocabulary needs to be found and the various perspectives and
the dierent knowledge need to be understood. Only then one
can start to discover where and how both sides can benet from
each other or how they can join forces to design novel methods
for hybrid societies.
We have identied three primary challenges of designing hybrid
societies: formalization, system design, and interdisciplinarity.
All of them require a lot of attention and a major eort to be
overcome. However, a generic formalization approach and
ecient interdisciplinary collaborations shall create synergies
and enable us to re-use methods at intersections between
disciplines. An appropriate system design approach would
enable us to quickly deploy safe, reliable, and stable systems
in hardware.
HH and YK wrote the paper and organized the overall writing
process. All other authors contributed about equally to the writ-
ing process.
is work was partially supported by the European Unions
Horizon 2020 research and innovation program under the
FET grant agreement “ora robotica,” no. 640959 and the ERC
Advanced Grant EPNet (340828). EF acknowledges support
from the Fund for Scientic Research (FWO), Flanders, Belgium.
RR acknowledges support from EPSRC and the Department of
Electronics, University of York, UK. AZ acknowledges sup-
port from the Slovenian Research Agency (ARRS, programme
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Conict of Interest Statement: e authors declare that the research was con-
ducted in the absence of any commercial or nancial relationships that could be
construed as a potential conict of interest.
e reviewer MP declared a shared aliation, though no other collaboration,
with one of the authors (AZ) to the handling Editor, who ensured that the process
nevertheless met the standards of a fair and objective review.
Copyright © 2016 Hamann, Khaluf, Botev, Divband Soorati, Ferrante, Kosak,
Montanier, Mostaghim, Redpath, Timmis, Veenstra, Wahby and Zamuda. is
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... Итак, гибридное общество [20] можно определить как самоорганизующуюся гетерогенную коллективную систему, которая состоит из переплетенности различных компонентов как минимум двух больших сфер: натурального и искусственного (биогибридная сфера), а также людей, взаимодействующих через техническое измерение (социотехническая сфера). ...
The boundaries of social acceptance and models of convergence of human and non-human (for example, subjects of artificial intelligence) actors of digital reality are defined. The constructive creative possibilities of convergent processes in distributed neural networks are analyzed from the point of view of possible scenarios for building “friendly” human-dimensional symbioses of natural and artificial intelligence. A comprehensive analysis of new management challenges related to the development of cyber-physical and cybersocial systems is carried out. A model of social organizations and organizational behavior in the conditions of cyberphysical reality is developed. The possibilities of reconciling human moral principles and “machine ethics” in the processes of modeling and managing digital reality are studied. The significance of various concepts of digital, machine and cyber-anymism for the socio-cultural understanding of the development of modern cyber-physical technologies, the anthropological dimension of a smart city is revealed. The article introduces the concept of hybrid society and shows the development of its models as self-organizing collective systems that consist of co-evolving biohybrid and socio-technical spheres. The importance of modern anthropogenic research for sustainable development is analyzed. The process of marking ontological boundaries between heterogeneous modalities in the digital world is investigated. Examples of acute social contexts that are able to set the vector of practical philosophy in the modern digital era are considered.
... Critical future applications such as disaster relief and search & rescue will require intelligent spatial coordination among many robots spread over large geographical areas. However, several gaps exist in multi-agent robotic controllers: current communication and control frameworks need to be improved to provide the adaptiveness, resilience, and computational efficiency required for operating in complex and rapidly changing real-world conditions (Murray, 2007;Hamann et al., 2016;Chung et al., 2018;Yang et al., 2018). A team of researchers at the Johns Hopkins University Applied Physics Lab (JHU/APL) and JHU/School of Medicine (SOM) explored whether neuroscience may offer insights to create a new class of multi-agent robotic controllers that could begin to address these aforementioned gaps. ...
This article discusses how to create an interactive virtual training program at the intersection of neuroscience, robotics, and computer science for high school students. A four-day microseminar, titled Swarming Powered by Neuroscience (SPN), was conducted virtually through a combination of presentations and interactive computer game simulations, delivered by subject matter experts in neuroscience, mathematics, multi-agent swarm robotics, and education. The objective of this research was to determine if taking an interdisciplinary approach to high school education would enhance the students learning experiences in fields such as neuroscience, robotics, or computer science. This study found an improvement in student engagement for neuroscience by 16.6%, while interest in robotics and computer science improved respectively by 2.7% and 1.8%. The curriculum materials, developed for the SPN microseminar, can be used by high school teachers to further evaluate interdisciplinary instructions across life and physical sciences and computer science.
... Formalism of cognitive models in biohybrid societies is a substantial challenge, as is formalism of biohybrid societies in general (see Hamann et al. 2016). To focus efforts to meet that challenge, we identify predator-prey behavior as a useful case to study: Predator-prey interactions include multiple individuals with clear goals; it is essential to animals such as fish, and modeling it can encompass elements that are essential for studies of intelligent behavior, such as learning and robustness across potentially unpredictable environments. ...
... In general, our results may have a broad appeal, emphasizing a continuing importance of the divide-and-conquer approaches to complex problems (Glasmachers, 2017). The structurepreserving imitation learning augmented by delayed rewards is likely to find applications in multi-agent cooperation and collective behavior (Prokopenko and Wang, 2004;Xu et al., 2013;Bai et al., 2015;Hamann et al., 2016;Cliff et al., 2017), modular robotics (Prokopenko et al., 2006;Martius et al., 2007;Der and Martius, 2012), and distributed networks and dynamical systems in general (Mortveit and Reidys, 2007;Cliff et al., 2013Cliff et al., , 2016Hefny et al., 2015). ...
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We describe and evaluate a neural network-based architecture aimed to imitate and improve the performance of a fully autonomous soccer team in RoboCup Soccer 2D Simulation environment. The approach utilizes deep Q-network architecture for action determination and a deep neural network for parameter learning. The proposed solution is shown to be feasible for replacing a selected behavioral module in a well-established RoboCup base team, Gliders2d, in which behavioral modules have been evolved with human experts in the loop. Furthermore, we introduce an additional performance-correlated signal (a delayed reward signal), enabling a search for local maxima during a training phase. The extension is compared against a known benchmark. Finally, we investigate the extent to which preserving the structure of expert-designed behaviors affects the performance of a neural network-based solution.
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The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness. In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.
L'idée de cette recherche et de pouvoir s'interroger sur l'impact de l’évolution de l'intelligence artificielle sur la société et les pratiques démocratiques. Les évolutions générationnelles, sociétales, technologiques et politiques sont entremêlées et progressent à des vitesses inégales. Ce qui a pour conséquence un déplacement des zones de pouvoir et des usages incontournables de la technologie qui y sont associés. Nous avons pu constater qu’un changement d’algorithme dans la sélection d’informations mises en avant par certains médias sociaux pouvait entrainer une forme de repli, d’entre soi ou encore de contrôle pouvant engendrer la diffusion de fausses nouvelles, de communautés virtuelles allant jusqu’à l’amplification de mouvements sociaux mettant à risque le leadership républicain classique (épisode social français dit des « Gilets Jaunes »). Avec la vitesse à laquelle se modifient les pratiques du big data et de l’intelligence artificielle, le phénomène de la maîtrise de la donnée est donc devenu tant un enjeu économique que politique. C’est la conséquence fine de ces phénomènes qui mènera à l’influence et à son corolaire le pouvoir. Ces changements entrainent, dans leur sillage, la disparition de la notion de secret, d’intimité, de libre arbitre, ou encore de libertés individuelles et par extension d’une vision de la démocratie. Dans ce contexte plusieurs questionnements émergent : - Dans quelle mesure l’évolution de l’intelligence artificielle peut-elle interroger le jeu démocratique ? - Quelle organisation mettre en place pour contrôler ces évolutions et les influences potentielles sur les citoyens ? - Comment assurer une éthique d’utilisation des données ? - Si un système me connait mieux que moi, pourra-t-il choisir le candidat qui lui correspond le mieux au moment du vote ? - Si c’est le cas le concept de vote sera-t-il encore utile ? - Comment pourraient-être gérée les institutions dans un monde dopé à d’intelligence artificielle ? - Comment faire en sorte de créer une complémentarité entre l’IA et les institutions politiques afin de s’orienter vers un management socialement responsable permettant d’améliorer le rapport aux pratiques démocratiques ?
Conference Paper
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When Alan Turing proposed the imitation game as a method to investigate the question if machines can think, he described a social system. However, the various disciplines that have pursued this seminal inquiry rarely touch base with sociological concepts. One might be tempted to say that progress has been made on brains, bodies, and on models of minds. I claim that there is something largely missing in this picture, which is the social aspect. To propose an alternative route, I consider Niklas Luhmann's theory of social systems as a suitable foundation for guiding the development of hybrid social systems. A hybrid social system is understood as a social assemblage in which minds and machines mingle: humans, machines, certain things, cyborgs. Some animals are welcome, too.
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A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks.
Conference Paper
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For making artificial systems collaborate with group-living animals, the scientific challenge is to build artificial systems that can perceive, communicate to, interact with and adapt to animals. When such capabilities are available then it should be possible to built cooperative relationships between artificial systems and animals. Machines In this framework, machines do not replace the living agents but collaborate and bring new capabilities into the resulting mixed group. On the one hand, such artificial systems offer new types of sensors, actuators and communication opportunities for living systems; on the other hand the animals bring their cognitive and biological capabilities into the artificial systems. Novel bio-hybrid modeling frameworks should be developed to streamline the implementation issues and allow for major time saving in the design and building processes of artificial agents. We expect strong impacts on the design of new intelligent systems by merging the best of the living systems with the best of ICT systems.
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Cyber-Physical-Social Systems (CPSSs) integrate various resources from physical, cyber, and social worlds. Efficient interaction of these resources is essential for CPSSs operation. Ontologies do not only provide for semantic operability between different resources but also provide means to create sharable ontology-based context models specified for actual settings. Usage of the context supports situation-driven behavior of CPSSs resources and thus is an enabler for their self-organisation. The present research inherits the idea of context ontologies usage for modelling context in CPSSs. In this work, an upper level context ontology for CPSSs is proposed. This ontology is applied in the domain of self-organising resource network.
The third edition of Van Kampen's standard work has been revised and updated. The main difference with the second edition is that the contrived application of the quantum master equation in section 6 of chapter XVII has been replaced with a satisfactory treatment of quantum fluctuations. Apart from that throughout the text corrections have been made and a number of references to later developments have been included. From the recent textbooks the following are the most relevant. C.W.Gardiner, Quantum Optics (Springer, Berlin 1991) D.T. Gillespie, Markov Processes (Academic Press, San Diego 1992) W.T. Coffey, Yu.P.Kalmykov, and J.T.Waldron, The Langevin Equation (2nd edition, World Scientific, 2004) * Comprehensive coverage of fluctuations and stochastic methods for describing them * A must for students and researchers in applied mathematics, physics and physical chemistry.
This paper describes a research program with the goal of understanding the types of simple local interactions which produce complex and purposive group behaviors1. We describe a synthetic, bottom-up approach to studying group behavior, consisting of designing and testing a variety of social interactions and scenarios with artificial agents situated in the physical world. We propose a set of basic interactions which can be used to structure and simplify the process of both designing and analyzing group behaviors. We also demonstrate how these basic interactions can be simply combined into more complex compound group behaviors. The presented behavior repertoire was developed and tested on a herd of physical mobile robots demonstrating avoiding, following, dispersing, aggregating, homing, flocking, and herding behaviors.
From flocking birds to swarming molecules, physicists are seeking to understand 'active matter' — and looking for a fundamental theory of the living world.