Towards a Cyber-physical Systems Resilience Approach based on
Artificial Emotions and Multi-agent Systems
Eskandar Kouicem, Cl
´
ement Ra
¨
ıevsky and Michel Occello
Univ. Grenoble Alpes, Grenoble INP, LCIS, 26000 Valence, France
Keywords:
Artificial Emotions, Multi-agent Systems, Cyber-physical Systems, Resilience, Distributed Systems.
Abstract:
The concept of resilience is popular and studied in different domains like human and social sciences (psychol-
ogy, psychiatry, sociology etc.) and recently in cognitive science, biology, ecology and computer science. The
objective of this article is to present our research that aims to incorporate knowledge from human and social
sciences in computer science to solve cyber-physical systems resilience problems. For us humans, emotions
are considered as an important process in responding to unanticipated events, for that, emotions are important
for our resilience. Our work aims to inspire from emotional processes to create an agent model that increases
the resilience of cyber-physical systems. This agent model will integrate individual and collective processes.
In addition, one of our principal hypotheses in our research is that the multi-agent paradigm is suitable to
integrate emotion-like processes into cyber-physical systems.
1 INTRODUCTION
As humans, our emotions increase our ability to
adapt to unknown situations as individuals and as
groups (Frijda, 1986; Lazarus, 1991). So human emo-
tion is a relevant source of inspiration for solving dis-
tributed systems resilience problems that we consider
as the ability to recognize, adapt, and handle unantic-
ipated perturbations (Woods, 2012). For us, a cyber-
physical system (CPS) is a system in which its mecha-
nisms are controlled or monitored by computer-based
algorithms, composed of several subsystems and it
has at least a part of its subsystems in direct interac-
tion with the physical world. Depending on their char-
acteristics, which are the interaction with the physi-
cal environment and the open system, cyber-physical
systems can have some problems such as: incor-
rect collected data, communication losses, limited re-
sources, adding or removing components to/from the
CPS during the execution, and the unpredictability of
the encountered situations. We therefore aim to use
knowledge from psychology of emotion and sociol-
ogy to improve cyber-physical systems’ resilience, es-
pecially taking into consideration their distributed as-
pect. To this end, we adopt the multi-agent paradigm
which intrinsically and historically support using so-
cial and cognitive metaphors in the design and im-
plementation of distributed complex systems (Ferber
and Weiss, 1999). This approach will allow us to in-
tegrate cognitive science and psychology knowledge
about resilience (resilience to stress (Lazarus, 1993),
collective resilience etc.) and cognitive functions of
emotion in agents’ decision-making mechanisms and
in the self-organizing processes of the agents groups.
This paper starts by defining resilience in differ-
ent domains and presents a classification of exist-
ing resilience approaches and their problems. Af-
ter that, we present our analysis of the relevance of
emotion-related cognitive processes in our resilience
and how to draw from these cognitive processes to im-
prove resilience-related artificial processes in cyber-
physical systems. Relationships between resilience
properties and characteristics of emotion and multi-
agent systems are then proposed. Finally, we con-
clude this paper by the description of the perspective
of this work.
2 RESILIENCE
Resilience is studied by researchers in a variety of
disciplines including psychology, sociology, and re-
cently cognitive science, ecology and computer sci-
ence.
The term resilience is used differently by differ-
ent communities. Ruault et al. (2011) explain that, in
ecology, resilience is the ability of an ecosystem or
species to recover its normal behaviour after experi-
Kouicem, E., RaÃ
´
revsky, C. and Occello, M.
Towards a Cyber-physical Systems Resilience Approach based on Artificial Emotions and Multi-agent Systems.
DOI: 10.5220/0009176203270334
In Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) - Volume 1, pages 327-334
ISBN: 978-989-758-395-7; ISSN: 2184-433X
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
327
encing traumas. They also state that in psychology,
resilience is the ability of a person or a group to de-
velop well, to continue to project into the future, de-
spite destabilizing events, difficult living conditions
and sometimes severe traumas. At the level of the
individual, traumas destroy the psyche, at the level
of the group, traumas destroy the existing bonds be-
tween the members of the group.
Generally, in systems engineering, resilience is a
rapid recovery from a degraded system state. The net-
works community defines it as the combination of re-
liability (reliability, security, performance) and toler-
ance (survivability, disturbance tolerance and traffic
tolerance). The computer science community has de-
fined resilience as the persistence of supplying ser-
vices and the availability of features (Trivedi et al.,
2009).
According to Woods (2012), in resilience engi-
neering, “resilience concerns the ability to recognize
and adapt to handle unanticipated perturbations that
call into question the model of competence, and de-
mand a shift of processes, strategies and coordina-
tion”.
There is often a confusion in the uses of the terms
robustness and resilience. To disambiguate the two,
we chose the definitions of (Linkov et al., 2013b):
Robustness is the extent to which a system is able
to withstand an unexpected internal or external
event or change without degrading system perfor-
mance.
Resilience refers to the system’s ability to re-
cover or regenerate its performance after an unex-
pected impact produces a degradation of its per-
formance (Linkov et al., 2013b).
2.1 Resilience in Cyber-physical
Systems
To position our approach relative to the cyber-
physical systems (CPSs) domain, we based our study
on the work of Lee et al. (2015) who defined the 5C
architecture presented in Figure 1. This architecture
specifies how a CPS is built, from the initial data ac-
quisition to the creation of final system.
The integration of all the 5 levels in a CPS is rarely
achieved. Generally, a CPS has two main functional
components:
1. advanced connectivity that provides real-time data
acquisition from the physical world and informa-
tion about cyberspace, and
2. intelligent data management, data analysis and
computing capabilities that build cyberspace.
However, this requirement is very abstract and
not specific enough for CPS implementation in gen-
eral (Lee et al., 2015).
Since our aim is to give CPS the ability to self-
configure and self-adapt in order to improve their re-
silience, it appears that our contribution will be a part
Figure 1: 5C architecture for the implementation of a cyber-physical system (Lee et al., 2015).
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
328
of the upmost layer of the 5C architecture: “V. Con-
figuration Level”.
2.2 Existing Resilience Approaches
Existing resilience approaches are divided into two
categories: qualitative and quantitative (see Fig-
ure 2) (Hosseini et al., 2016).
The qualitative category: it includes methods that
assess the resilience of a system without any
numerical descriptor, it also contains two sub-
categories:
Conceptual Frameworks: offer best practices
for analyzing system’s resilience by guiding re-
silient systems principles and characteristics.
Semi-quantitative Indices: provide expertise on
different qualitative aspects of resilience. These
approaches are developed using a set of ques-
tions designed to assess different resilience-
based characteristics (e.g., redundancy, re-
sourcefulness).
Quantitative methods with two sub-categories:
General Resilience Measures: determine re-
silience by comparing the system performance
before and after the disturbance without fo-
cusing on the specific system’s characteristics.
There are deterministic and stochastic mea-
sures, each one of them is used to describe the
static and dynamic system’s behaviour.
Structural-based Approaches: examine how the
structure of a system affects its resilience by ob-
serving the system’s behaviour and modelling
or simulating the characteristics of the system.
Another classification of existing resilience ap-
proaches into two other main categories: metric-
based and model-based approaches (see Fig-
ure 3) (Linkov and Kott, 2019).
Metric-based Approaches: use individual com-
ponent properties or system functions measures
(metrics) to assess overall the system’s perfor-
mance. Resilience metrics are defined as a mea-
surable properties of the system that quantify the
system’s objectives achievement degree.
Model-based Approaches: use system config-
uration modeling and scenario analysis to deter-
mine overall the system’s performance. These ap-
proaches use mathematical or physical concepts
to represent the real world (environment) and de-
fine the resilience. Modelling the system’s re-
silience requires the knowledge of a system’s crit-
ical functions, tasks, objectives, temporal pat-
terns, thresholds, memory, and adaptation.
Figure 3: Metric-based and model-based approaches for re-
silience assessment (Linkov and Kott, 2019).
According to Hosseini et al. (2016), the main ap-
proaches addressing the issue of resilience are either
centralized or based on redundancy. In contrast we
aim for taking the distributed nature of CPS into ac-
count at the core of the resilience-related processes
and to avoid redundancy because it is not always prac-
tically feasible in systems including physical compo-
nents.
3 PROPOSED SOLUTION
As we can see in Figure 3, agent-based approach and
multi-agent systems are considered as model-based
approaches to address resilience.
Figure 2: Classification scheme of resilience assessment methodologies (Hosseini et al., 2016).
Towards a Cyber-physical Systems Resilience Approach based on Artificial Emotions and Multi-agent Systems
329
Our work aims to increase resilience in a cyber-
physical system by providing its subsystems with a
form of autonomy of decision that allows them to de-
tect abnormal situations and adapt their behaviour to
these situations. These mechanisms are inspired by
human emotions and allow the entire cyber-physical
system to detect abnormal situations, and then, adapt
its behaviour to these situations using artificial pro-
cesses inspired by social metaphors (Kouicem et al.,
2019). To do so, our approach utilizes the multi-agent
paradigm (Ferber and Weiss, 1999).
3.1 Resilience Profile
Figure 4 presents our adaptation of the resilience pro-
file proposed by Linkov and Kott (2019). This profile
represents the different resilience-related behaviours
triggered by the occurrence of an advert event.
We added a detection behaviour phase which is
active before the event and crucial to CPSs since it
is responsible for the triggering of absorption and re-
covery behaviour. Once the event is detected and the
absorption is active, the system begins the recovery
process, potentially taking into account information
about the event gathered by the detection behaviour.
Figure 4: The extended resilience profile (based on (Linkov
and Kott, 2019)).
As we see, it is possible that the system will im-
prove its functionality and resilience during the adap-
tation phase. This increase is justified by the addition
of the potential gain in the detection and recovery ca-
pacity acquired during the adaptation phase to the sys-
tem’s functionality.
3.2 Multi-agent Approach for CPS
Resilience
As we have said, in our proposed solution we use a
social metaphor for cyber-physical systems. So we
have chosen the multi-agent approach because it suits
best our needs, adapted to the control of distributed
systems and addressing certain aspects of resilience.
Multi-agent systems offer us a decentralized so-
lution to solve the “single point of failure” prob-
lem inherent in centralized solutions. In this ap-
proach, each agent is autonomous and it has its own
role, there is no global control, the data and the de-
cisions are decentralized (Ferber and Weiss, 1999).
Through these characteristics a multi-agent system
can maintain its functioning in case of communica-
tion loss, decreases the volume of transmitted data,
scaling up and avoids redundancy/replication of many
software and hardware components of the cyber-
physical system using the autonomy of its agents.
Moreover, adopting the multi-agent paradigm and
self-organization (Di Marzo Serugendo et al., 2006)
breaks with traditional resilience approaches by using
a social metaphor for cyber-physical systems. Adding
to this, this paradigm allows us to use cognitive sci-
ence and psychology to reproduce the cognitive func-
tions of emotions (Ivanovi
´
c et al., 2015), both in
the individual’s decision-making process and in the
group’s self-organization process.
There is some work on resilience in cyber-
physical systems using a multi-agent ap-
proach (Janu
´
ario et al., 2018, 2019), that solves
the problem of resilience enhancement in CPSs
and WSAN (wireless sensor and actuator network)
using hierarchical multi-agent systems. They
propose frameworks based on a multi-agent ap-
proach implemented in a distributed middleware.
These frameworks aim to improve the resilience of
networked control and supervision systems. Our
proposal is largely distinct because we aim to use
artificial processes inspired by human emotions and
social metaphors to improve cyber-physical systems
resilience.
3.3 Use of Artificial Emotions for CPS
Resilience
According to Frijda (1986) emotional phenomena are:
“non-instrumental behaviours and non-instrumental
features of behaviour, physiological changes, and
evaluative subject-related experiences, as evoked by
external or mental events, and primarily by the signif-
icance of such events.”
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
330
In computer science, artificial emotions are a set
of pre-programmed or non-scheduled processes run-
ning within a machine, facilitating decision-making
and enabling the system to adapt to the environment.
The artificial emotion is the fruit of the program’s
input-output as well as its own internal activity, and
is often the object of a collaboration with a cognitive
structure, by means of which the system deals with
the problems introduced by its environment. In ad-
dition, it is part of a programming logic more or less
explicit, but still in the field of computable (Mahboub,
2011; Kouicem et al., 2019).
The main existing work uses artificial emotions
for human-machine interaction and the detection and
expression of natural emotions (Calvo et al., 2015).
Other work uses artificial emotions to make virtual
agents behaviours more realistic (Saunier and Jones,
2014; Adam et al., 2010). More specifically, in the
field of cyber-physical systems, the main work con-
cerns the emotion recognition (Calero et al., 2018)
and emotional robots (Breazeal, 2003). We noticed
that this work falls within the scope of affective com-
puting (Picard, 2000).
We note that our work differs from these research
themes, it does not deal with the interaction with
humans or the simulation of natural emotions, but
rather by inspiring from their functions (Ra
¨
ıevsky and
Michaud, 2009). In existing work on artificial emo-
tions, emotions are elicited by an analysis of sym-
bolic events related to the objectives of the system
or require a large amount of knowledge of the sys-
tem designer about the situations that the system may
encounter. These initial hypotheses of existing work
limit the resilience of the resulting systems. Further-
more, these characteristics are difficult to apply to
CPSs because:
1. They are embedded in the physical world and can
not trigger emotions from symbolic data about the
situation and,
2. the distributed and opened nature of CPSs pre-
vents their designers from anticipating all possible
situations.
In order for cyber-physical systems to benefit from
the capacities provided by emotions, without compro-
mising their resilience, we propose to identify generic
mechanisms, not specific to the task of the system,
to initiate the artificial equivalent of an emotional
episode. Otherwise, artificial emotions will mainly
be used to detect abnormal situations and update so-
cial organizations in response to these abnormal sit-
uations. These functions are directly related to the
“Plan/Prepare”,“Absorb” and Adapt” phases of the
resilience profile depicted in Figure 4.
The existing work that links resilience with emo-
tions is mainly in psychology and sociology (Mas-
ten et al., 1990; Lazarus, 1991, 1993; Tugade and
Fredrickson, 2004; Norris et al., 2008). We note
that our idea of creating an emotion-based approach
to increase cyber-physical systems resilience remains
original.
3.4 Resilience Matrix
In resilience profile (see Figure 4), we can see the
four stages of the event management cycle that a sys-
tem needs to maintain to be resilient according to
the National Academy of Sciences (NAS) (National
Academy of Sciences, 2012; Linkov et al., 2013b,a)
which are:
1. Plan/Prepare: lay the foundation to ensure that
services and assets remain available in case of ab-
normal situations.
2. Absorb: maintain the availability of the most crit-
ical functions and services while delaying or iso-
lating the disruption.
3. Recover: restore all asset functions and service
availability to their pre-event (before the abnormal
situation) functionality.
4. Adapt: use knowledge of critical events to mod-
ify communication protocols, system configura-
tion, learning process or other aspects to increase
resilience.
Linkov et al. (Linkov et al., 2013a,b) combined those
four phases and four domains (physical, information,
cognitive and social) to create a generic matrix of
resilience metrics. In our case, we combined the
four phases of resilience with artificial emotions (cog-
nitive) and multi-agent systems (social) for obtain-
ing our resilience matrix (see Table 1). This map-
ping helped us to identify the aspects of artificial
emotions and multi-agent systems (cells of the ma-
trix) that we will integrate in our agent model to in-
crease cyber-physical systems resilience. In the ma-
trix, we used the detection phase as a combination of
“Plan/Prepare” and Absorb” phases because it fully
covers the planning phase and trigger the absorption
phase (see Figure 4).
3.5 Scenario (An Example)
We consider a cyber-physical system composed of
several sensors and actuators as a multi-agent sys-
tem, each subsystem (sensor or actuator) of the CPS
is an agent. A sensor agent perceives data from the
environment with its sensor module. An actuator
Towards a Cyber-physical Systems Resilience Approach based on Artificial Emotions and Multi-agent Systems
331
agent acts on the environment with its actuator mod-
ule. Agents who share the same goals belong to the
same organization, whether they are sensors or actua-
tors. The agents of an organization communicate with
each other, so the actuator agents share with the other
agents their operating status and the sensor agents
provide information about the environment. So, if
an actuator agent needs some information, it asks the
sensor agents of its organization to perceive data for
it, if these ones can’t get it, they can ask the sen-
sor agents of the other organizations. Each sensor
agent stores the recently perceived data in a time se-
ries database in its own memory, then the old time
series will be shared in the memories of the actuators
of its same organization. Basically, the agents have a
predefined tolerance thresholds to the perceived data
stocked in both sensor and actuator agents.
Here is a reaction scenario of the agents based on
emotional processes in case of an anomaly in the data
that a sensor agent perceives from the environment:
Detection: the detection is always active, before
any critical event until the beginning of the ab-
sorption phase. Once a sensor agent has a doubt
about the data that it perceives by getting out-of-
domain values unusual sequences or long repeti-
tion of similar data, the planning phase begins.
Plan/Prepare: the sensor agent increases the
sampling frequency and compares the data with a
larger time series and communicates the situation
to the other agents of his organization to confirm
whether it is a true abnormal or just a false posi-
tive.
Absorb: if the situation is confirmed as abnor-
mal, the agents reduce the tolerance thresholds
and communicate this situation to other organiza-
tions of the system.
Recover: the system’s agents continue to oper-
ate ignoring the data of this sensor agent and take
into consideration the system’s data history in the
same conditions.
Adapt: the agents use the knowledge of this sit-
uation by modifying their communication pro-
tocol intra-organization and/or extra-organization
or updating the organizations architectures to im-
prove its resilience.
4 CONCLUSIONS AND FUTURE
WORK
In this paper, we presented our work about utilizing
the multi-agent paradigm to integrate knowledge from
psychology of emotion to improve cyber-physical
systems resilience. We presented our study of the def-
inition of resilience in relevant research domains and
presented our proposition and our choices regarding
inspiration from human emotion as well as the choice
of the multi-agent paradigm.
Our goal is to create an agent model based on
the resilience matrix that has been defined. This
agent model integrates individual and decentralized
processes dedicated to the detection of abnormal sit-
uations inspired by the cognitive processes trigger-
ing emotional episodes in humans. The agent model
will also include mechanisms for adapting individual
behaviour in relation with the detected situations in
order to increase the resilience of the cyber-physical
system. Individual detection and adaptation trigger
collective processes in the system, these processes
use the information built by the individual mecha-
nisms. The individual behaviour adaptation impacts
on the organization of the agents group that controls
Table 1: The resilience matrix.
Detection phase: Plan/Prepare and
Absorb
Recover Adapt
Artificial
emotions
-Using an incremental process for
anomaly detection.
-Anticipating existing scenarios.
-Increase the sampling frequency.
-Increasing in the readiness for action.
-Decrease of tolerance thresholds.
-Rapid recovery of nominal behaviour.
-Review the response and decision-making
processes.
-Adaptation of the response behaviour of
the situation.
Multi-agent
systems
-Collective detection process.
-Propagation of detected situations.
-Cooperation for negotiate a detected
situation to decide if it is a real or a
fake abnormal situation.
-Establish a collective decision making
techniques or aids to select recovery
options.
-Sharing the responses (adaptations).
-A self-organization of the group of agents
carrying out the system’s control.
-Improve decision-making and allow
the system to adapt to the environment.
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
332
the cyber-physical system, which will trigger self-
organization for adapting the collective behaviour to
the abnormal situation.
The proposed agent model and multi-agent sys-
tem will be tested by simulation (Jamont and Occello,
2013), and also in real cyber-physical systems in or-
der to quantify experimentally the resilience improve-
ment they bring.
ACKNOWLEDGEMENTS
This Ph.D project has received funding from the Trust
research chair of the Grenoble-INP Foundation and
the Auvergne-Rh
ˆ
one-Alpes region.
REFERENCES
Adam, C., Canal, R., Gaudou, B., Vinh, H. T., Taillandier,
P., et al. (2010). Simulation of the emotion dynamics
in a group of agents in an evacuation situation. In
International Conference on Principles and Practice
of Multi-Agent Systems, pages 604–619. Springer.
Breazeal, C. (2003). Emotion and sociable humanoid
robots. International journal of human-computer
studies, 59(1-2):119–155.
Calero, J. A. M., Marino, R., Lanza-Gutierrez, J. M.,
Riesgo, T., Garcia-Valderas, M., and Lopez-Ongil, C.
(2018). Embedded emotion recognition within cyber-
physical systems using physiological signals. In 2018
Conference on Design of Circuits and Integrated Sys-
tems (DCIS), pages 1–6. IEEE.
Calvo, R. A., D’Mello, S., Gratch, J. M., and Kappas, A.
(2015). The Oxford handbook of affective computing.
Oxford University Press, USA.
Di Marzo Serugendo, G., Gleizes, M.-P., and Karageorgos,
A. (2006). Self-organisation and emergence in multi-
agent systems: An overview. Informatica, 30(1):45–
54.
Ferber, J. and Weiss, G. (1999). Multi-agent systems: an
introduction to distributed artificial intelligence, vol-
ume 1. Addison-Wesley Reading.
Frijda, N. H. (1986). The emotions. Cambridge University
Press.
Hosseini, S., Barker, K., and Ramirez-Marquez, J. E.
(2016). A review of definitions and measures of
system resilience. Reliability Engineering & System
Safety, 145:47–61.
Ivanovi
´
c, M., Budimac, Z., Radovanovi
´
c, M., Kurbalija, V.,
Dai, W., B
˘
adic
˘
a, C., Colhon, M., Ninkovi
´
c, S., and
Mitrovi
´
c, D. (2015). Emotional agents–state of the art
and applications. Computer Science and Information
Systems, 12(4):1121–1148.
Jamont, J.-P. and Occello, M. (2013). Using mash in the
context of the design of embedded multiagent system.
In International Conference on Practical Applications
of Agents and Multi-Agent Systems, pages 283–286.
Springer.
Janu
´
ario, F., Cardoso, A., and Gil, P. (2018). Multi-agent
framework for resilience enhancement over a wsan.
In 2018 15th International Conference on Electrical
Engineering/Electronics, Computer, Telecommunica-
tions and Information Technology (ECTI-CON), pages
110–113. IEEE.
Janu
´
ario, F., Cardoso, A., and Gil, P. (2019). A distributed
multi-agent framework for resilience enhancement
in cyber-physical systems. IEEE Access, 7:31342–
31357.
Kouicem, E., Ra
¨
ıevsky, C., and Occello, M. (2019). Artifi-
cial Emotions for Distributed Cyber-physical Systems
Resilience. In Proceedings of the Cyber-Physical Sys-
tems PhD Workshop 2019, pages 84–95.
Lazarus, R. S. (1991). Emotion and adaptation. Oxford
University Press on Demand.
Lazarus, R. S. (1993). From psychological stress to the
emotions: A history of changing outlooks. Annual
review of psychology, 44(1):1–22.
Lee, J., Bagheri, B., and Kao, H.-A. (2015). A cyber-
physical systems architecture for industry 4.0-based
manufacturing systems. Manufacturing Letters, 3:18–
23.
Linkov, I., Eisenberg, D. A., Bates, M. E., Chang, D., Con-
vertino, M., Allen, J. H., Flynn, S. E., and Seager, T. P.
(2013a). Measurable resilience for actionable policy.
Environmental Science & Technology, 47(18):10108–
10110.
Linkov, I., Eisenberg, D. A., Plourde, K., Seager, T. P.,
Allen, J., and Kott, A. (2013b). Resilience metrics for
cyber systems. Environment Systems and Decisions,
33(4):471–476.
Linkov, I. and Kott, A. (2019). Fundamental concepts of
cyber resilience: Introduction and overview. In Cy-
ber resilience of systems and networks, pages 1–25.
Springer.
Mahboub, K. (2011). Mod
´
elisation des processus
´
emotionnel dans la prise de d
´
ecision.(Emotional pro-
cesses modelling in decision making). PhD thesis,
University of Le Havre, France.
Masten, A. S., Best, K. M., and Garmezy, N. (1990).
Resilience and development: Contributions from the
study of children who overcome adversity. Develop-
ment and psychopathology, 2(4):425–444.
National Academy of Sciences, N. (2012). Disaster re-
silience: A national imperative. Washington, DC: The
National Academies Press.
Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F.,
and Pfefferbaum, R. L. (2008). Community resilience
as a metaphor, theory, set of capacities, and strategy
for disaster readiness. American journal of community
psychology, 41(1-2):127–150.
Picard, R. W. (2000). Affective computing. MIT press.
Ra
¨
ıevsky, C. and Michaud, F. (2009). Emotion generation
based on a mismatch theory of emotions for situated
agents. In Handbook of Research on Synthetic Emo-
tions and Sociable Robotics: New Applications in Af-
Towards a Cyber-physical Systems Resilience Approach based on Artificial Emotions and Multi-agent Systems
333
fective Computing and Artificial Intelligence, pages
247–266. IGI Global.
Ruault, J.-R., Luzeaux, D., Colas, C., and Sarron, J.-
C. (2011). R
´
esilience des syst
`
emes sociotechniques
application
`
a l’ing
´
enierie syst
`
eme. G
´
enie logiciel,
96:40–52.
Saunier, J. and Jones, H. (2014). Mixed agent/social dy-
namics for emotion computation. In Proceedings
of the 2014 international conference on Autonomous
agents and multi-agent systems, pages 645–652. In-
ternational Foundation for Autonomous Agents and
Multiagent Systems.
Trivedi, K. S., Kim, D. S., and Ghosh, R. (2009). Resilience
in computer systems and networks. In Proceedings
of the 2009 International Conference on Computer-
Aided Design, pages 74–77. ACM.
Tugade, M. M. and Fredrickson, B. L. (2004). Resilient in-
dividuals use positive emotions to bounce back from
negative emotional experiences. Journal of personal-
ity and social psychology, 86(2):320.
Woods, D. D. (2012). Essential characteristics of resilience.
In Resilience engineering, pages 33–46. CRC Press.
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
334