=Paper=
{{Paper
|id=Vol-2457/paper9
|storemode=property
|title=Artificial Emotions for Distributed Cyber-physical Systems Resilience
|pdfUrl=https://ceur-ws.org/Vol-2457/9.pdf
|volume=Vol-2457
|authors=Eskandar Kouicem,Clément Raïevsky,Michel Occello
|dblpUrl=https://dblp.org/rec/conf/cpsschool/KouicemRO19
}}
==Artificial Emotions for Distributed Cyber-physical Systems Resilience==
https://ceur-ws.org/Vol-2457/9.pdf
Artificial Emotions for Distributed
Cyber-physical Systems Resilience
Eskandar Kouicem, Clément Raı̈evsky, and Michel Occello
Univ. Grenoble Alpes, Grenoble INP, LCIS, 26000 Valence, France
firstname.lastname@lcis.grenoble-inp.fr
Abstract. The concept of system resilience is important and popular
in different domains like psychology, psychiatry, sociology, and more re-
cently in cognitive science, biological disciplines, ecology and computer
science. The main objective of this paper is to present a research avenue
exploring the applicability of knowledge from those domains to solve re-
silience problems in cyber-physical systems. Emotions have been identi-
fied as an important process to cope with unexpected events and is there-
fore crucial for resilience. Our work is thus aimed at utilizing emotion-like
processes in cyber-physical systems to improve their resilience, at indi-
vidual and collective levels. Furthermore, one of our main assumptions is
that the multi-agent paradigm is particularly well suited to embed such
emotion-like processes in this type of systems.
Keywords: Artificial Emotions · Multi-agent systems · Cyber-physical
systems · Resilience · Distributed Artificial intelligence · Distributed Sys-
tems · Human and Social Sciences
1 Introduction
Emotion has a major influence on the ability of humans to adapt to unknown or
unusual situations as individuals and as groups. It is therefore, a natural source of
inspiration when tackling the problem of distributed complex systems resilience
that we consider as the ability of such systems to identify and cope with unex-
pected situations. As emotions positively have a physiological component, the
main application domain of this PhD project1 will include cyber-physical sys-
tems, that is to say systems that are at least partially in direct interaction with
the physical world. Resilience concerns the ability to recognize, adapt, and han-
dle unanticipated perturbations that call into question the model of competence,
and demand a shift of processes, strategies and coordination [34]. In our work,
we aim at including knowledge from psychology and sociology about resilience
and emotion in cyber-physical systems utilizing the multi-agent paradigm. This
paradigm brings to the existing approaches of resilience a social metaphor for
complex systems. In addition, we aim at embedding cognitive science and psy-
chology knowledge about resilience and cognitive functions of emotion in the
1
This project is funded by the Trust research chair of the Grenoble-INP Foundation
and the Auvergne-Rhône-Alpes region.
individual systems decision-making mechanisms and in the self-organizing pro-
cesses of agents groups.
In this paper, we start by presenting a synthesis in the field of resilience
in human and social science and present the applicability of these concepts to
cyber-physical systems. After that, we will present the multi-agent paradigm our
solution will be based on. We will then present artificial emotions and expose the
way we plan to use inspiration from human emotion to solve resilience problems.
Finally, we conclude this paper by the description of the state of this project.
2 Resilience
Resilience is studied by researchers in a variety of disciplines including psy-
chology, psychiatry, sociology, and more recently cognitive science, biological
disciplines, ecology and computer science. Resilience has different definitions in
different communities. In ecology, resilience is the ability of an ecosystem or
species to recover its normal behaviour after experiencing traumas. In psychol-
ogy, “resilience is the ability of a person or a group to develop well, to continue
to project into the future, despite destabilizing events, difficult living conditions
and sometimes severe traumas” [28]. At the level of the individual, traumas de-
stroy the psyche, at the level of the group, traumas destroy the existing bonds
between the members of the group [28]. In computer science, resilience is the
persistence of supplying services and the availability of features [31].
2.1 Resilience in Human and Social Sciences
In human and social sciences and especially psychology, there are two types of
resilience, individual and collective. Individual resilience is the process of, capac-
ity for or outcome of successful adaptation despite challenging or threatening
circumstances [23]. Collective resilience is the ability of communities to with-
stand external shocks to their social infrastructure [3]. Psychological resilience
is characterized by the ability to bounce back from negative emotional experi-
ences and by adapting flexibly to the changing demands of stressful experiences,
also, the positive emotionality emerges as an important element of psychological
resilience [32,15].
The work of Norris et al. [26] (see Fig. 1) gives us insight on the applicability
of resilience in an artificial system by introducing the robustness, redundancy,
and rapidity notions. It explicitly states that resilience depends on its resources
to react well and amortize stress. According to Norris et al. [26] the keys concepts
relating resources and resilience are:
– Resources : Objects, conditions, characteristics, and energies that people
value.
– Robustness: resource strength, in combination with a low probability of re-
source deterioration.
– Redundancy: the extent to which elements are substitutable in the event of
disruption or degradation.
– Rapidity : how quickly the resource can be accessed and used (mobilized).
Fig. 1. The model of stress resistance and resilience over time [26].
On the one hand, vulnerability occurs when resources are not sufficiently
robust, redundant, or rapid to create resistance or resilience, resulting in per-
sistent dysfunction. The more severe, enduring, and surprising the stressor, the
stronger the resources must be to create resistance or resilience. On the other
hand, resilience occurs when resources are sufficiently robust, redundant, or rapid
to buffer or counteract the effects of the stressor allowing the organization to
adapt its functioning to the altered environment. For human individuals and
communities, this adaptation manifests in wellness [26].
2.2 Resilience in Cyber-Physical Systems
The 5C architecture presented by Lee et al. [18] clearly defines how to build
a CPS from the initial data acquisition to the creation of final value through
analysis. The detailed architecture of the 5C is described in Fig. 2. As we can
see, the configuration level is the one that allows machines to self-configure and
self-adapt. According to the definition of resilience we adopted, our contribution
to CPSs resilience lies in this configuration level.
Usually, a CPS consists of two main functional components: (i) advanced
connectivity that provides real-time data acquisition of the physical world and
cyberspace feedback and (ii) intelligent management of data, data analytics and
computing capabilities that build cyberspace. However, this requirement is very
Fig. 2. 5C architecture for the implementation of a cyber-physical system [18].
abstract and not specific enough for the implementation in general. The total
integration of the 5 levels within a CPS is currently rarely achieved, and it is
not always justified according to the type of application [18].
Linkov and Kott [20] define cyber-resilience as the ability of a system to re-
cover or regenerate its performance after a malfunction (or attack) has degraded
it (see Fig. 3). In this graph, we can see the four stages of the event management
cycle that a system needs to maintain to be resilient according to the National
Academy of Sciences (NAS) [1,19] which are:
1. Plan/Prepare: lay the foundation to keep services available and assets
functioning during a disruptive event (malfunction or attack).
2. Absorb: maintain most critical asset function and service availability while
repelling or isolating the disruption.
3. Recover: restore all asset function and service availability to their pre-event
functionality.
4. Adapt: using knowledge from the event, alter protocol, configuration of the
system, learning process, or other aspects to become more resilient.
2.3 Traditional resilience approaches problems
In general, resilience approaches can be divided into two broad categories: qual-
itative and quantitative.
Fig. 3. Notional resilience profile, plotting a systems critical functionality over
time [20].
– The qualitative category, which includes methods that evaluate the resilience
of a system without a numeric descriptor, contains two sub-categories:
• Conceptual frameworks that offer best practices.
• Semi-quantitative indices that provide expert assessments of different
qualitative aspects of resilience.
– Quantitative methods include two sub-categories:
• General resilience approaches that provide domain-independent mea-
sures for quantifying resilience across applications.
• Structural modelling approaches that model representations specific to
the components of the resilience components.
Linkov and Kott [20] classify existing work dealing with resilience into two
other main categories: metric-based and model-based approaches. Metric-based
approaches use measurements of the individual properties of system components
or functions to assess overall system performance. Model-based approaches use
system configuration modelling and scenario analysis to determine the overall
performance of the system (see Fig. 4). Agent based approach and multi-agent
systems are considered as model-based approaches to address resilience.
We find that the main approaches addressing the issue of resilience are cen-
tralized or based on redundancy [11]. This project aims to provide the subsystems
constituting a CPS with a form of decision-making autonomy, allowing the CPS
to detect abnormal situations and then adapt its behaviour to these situations.
To do so, our approach utilize the multi-agent paradigm.
Fig. 4. Metric-based and model-based approaches for resilience assessment [20].
3 Multi-agent systems and resilience
In this section, we are going to present multi-agent systems, self-organization
and the advantages of using a multi-agent approach for cyber-physical systems
resilience.
3.1 Multi-agent system (MAS)
An agent is a real (physical) or virtual entity, located in an environment, able
to perceive and act upon it, which can communicate with other agents, an agent
is responsive, proactive, social, autonomous and can also have the ability to
learn [8,33]. According to Demazeau, a multi-agent system is a system composed
of: a set of agents, an environment, a set of relationships and interactions between
agents and a set of organizations of agents [6].
According to Ferber [8] MAS main characteristics are:
– Each agent has its own role.
– No global control.
– The data and the decisions are decentralized.
– The operations are asynchronous.
The notion of organization plays a fundamental role within MASs and can
be defined as:
– a mean of logically organizing agents,
– a default communication network, and
– an agent, role, or competence search media.
In addition, its reification provides an entry point into the system for visualizing
and improving agent interactions through its dynamic evolution. Organization is
needed to structure the interactions that occur between different system entities
(agents) [24]. MASs can be provided with a self-organization process making
them able to adapt to changes in the environment. Self-organization is an en-
dogenous, bottom-up, process concerning systems in which only information is
manipulated by agents which may be unaware of the state of the organization in
its entirety [7,13]. Self-organization is achieved by modifying the organization;
either by directly changing the configuration of the system (topology, neigh-
borhoods, and influences) or through the system’s environment by using local
interactions and influences, avoiding using predefined models.
3.2 Multi-agent approach for resilience
Multi-agent systems offer us a decentralized solution to solve the “single point
of failure” problem which is intrinsic in centralized solutions. Moreover, with
a multi-agent approach we can avoid the redundancy/replication of a lot of
software and hardware components of the cyber-physical system [8]. Multi-agent
system can maintain its functioning in case communication loss, decrease of data
volumes to be transmitted and scaling using the autonomy of its agents. In
addition, adopting the multi-agent paradigm and self-organization is breaking
with traditional approaches of resilience by the fact that it makes a use of a
social metaphor for complex systems. Adding to that, this paradigm allows us
to use cognitive science and psychology to reproduce the cognitive functions of
emotions, both in the decision-making mechanism of the individual and in the
process of the self-organization of the group.
4 Artificial emotions and CPSs
In this section, we are going to present artificial emotions, emotions theories
(computational models) and the use of artificial emotions in our project.
4.1 Artificial emotions
According to Frijda [9], emotional phenomena are: non-operationalized behaviours,
non-instrumental behavioural traits, physiological changes, and evaluative expe-
riences, related to the subject, all caused by external or mental events, and
primarily by the meaning of such events. In computer science, artificial emo-
tions are a set of pre-programmed or non-scheduled processes running within a
machine, facilitating decision-making and enabling the system to adapt to the
environment. This 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 addition, it is part of a programming logic
more or less explicit, but still in the field of computable [21]. In the literature,
a panoply of emotion models and computational models are offered but for an
artificial system, certain constraints are always present (performance, reliability,
durability, integration...) for this reason the choice of an emotion model and
a computational model requires a deep analysis to have the best choice for a
cyber-physical system (see Fig. 5).
Fig. 5. A history of computational models of emotion [22].
Appraisal theory is one of the main psychological perspectives on emotion
and arguably the most fruitful source for the design of symbolic AI systems. In
this theory, emotion is argued to arise from patterns of individual judgment con-
cerning the relationship between events and an individuals beliefs, desires and
intentions, sometimes referred to as the person-environment relationship [16].
These judgments, formalized through reference to devices such as situational
meaning structures or appraisal variables [10], characterize aspects of the per-
sonal significance of events. Patterns of appraisal are associated with specific
physiological and behavioral reactions. Computational appraisal models have
been applied to a variety of uses including contributions to psychology and AI.
For example, several authors have argued that appraisal processes would be re-
quired by any intelligent agent that must operate in real-time, ill-structured,
multi-agent environments (e.g., Staller and Petta [30]).
Dimensional theories of emotion argue that emotion and other affective
phenomena should be conceptualized, not as discrete entities but as points in a
continuous (typically two or three) dimensional space [25]. It is not surprising
that these theories relegate the term emotion to a cognitive label attributed, ret-
rospectively, to some perceived body state. Computational dimensional models
are most often used for animated character behavior generation, perhaps because
it translates emotion into a small number of continuous dimensions that can be
readily mapped to continuous features of behavior such as the spatial extent of
a gesture. For example, PAD model of Mehrabian and Russell [25] where these
dimensions correspond to pleasure (a measure of valence), arousal (indicating
the level of affective activation) and dominance (a measure of power or control).
Anatomic theories stem from an attempt to reconstruct the neural links
and processes that underlie organisms emotional reactions [17]. Unlike appraisal
theories, such models tend to emphasize sub-symbolic processes. Unlike dimen-
sional theories, anatomic approaches tend to view emotions as different, discrete
neural circuits and emphasize processes or systems associated with these cir-
cuits. Computational models inspired by the anatomic tradition often focus on
low-level perceptual-motor tasks and encode a two-process view of emotion that
support for a fast, automatic, undifferentiated emotional response and a slower,
more differentiated response that relies on higher-level reasoning processes (e.g.,
Armony et al. [4]).
Rational approaches typically reside in the field of artificial intelligence
and view emotion as window through which one can gain insight into adaptive
behavior. Within this rational approaches, cognition is conceived as a collection
of symbolic processes that serve specific cognitive functions and are subject
to certain architectural constraints on how they interoperate. Emotion, within
this view, is simply another, although often overlooked, set of processes and
constraints that have adaptive value. Models of this sort are most naturally
directed towards the goal of improving theories of machine intelligence [22].
4.2 Artificial emotions for CPSs
Emotions have inspired many studies on human-machine interaction and espe-
cially on the detection and expression of natural emotions [5]. Other work on
artificial emotions aim to reproduce in artificial systems the stereotyped behav-
iors that are associated to make virtual agent behaviors more realistic [29,2].
It should be noted that our work is clearly distinct from these themes be-
cause it does not deal with the interaction with humans or simulation of natural
emotions but to reproduce their functions [27]. In existing approaches of arti-
ficial emotions, emotions are elicited by an analysis of symbolic events related
to the system’s goals or include a lot of knowledge from the system’s designer
about situations the system might encounter. These initial assumptions of exist-
ing work limits intrinsically the resilience of the resulting systems. Furthermore,
these characteristics are hardly applicable to CPSs since 1) they are embedded
in the physical world and can’t trigger emotions from symbolic data about the
situation and 2) the distributed and open nature of CPSs make it impossible for
their designers to foresee all possible situations.
Our aim is to design processes that replicate some functions of emotions
while avoiding to depend upon such assumptions. Therefore, the functions of
emotion we chose to replicate in artificial systems to improve their resilience are:
detecting abnormal situations and updating social organizations in response to
these abnormal situations. These functions are directly related to the ”Absorb”
and ”Adapt” phases of the resilience profile depicted in Fig. 3.
5 State of the project
In this project, we adopted an experimental approach made of different hypothe-
ses, and currently we are working on an emotional agent model for testing their
validity.
From the state of the art of artificial emotions, resilience and multi-agent
systems, we are designing an individual, decentralized mechanism for detecting
abnormal situations using a process inspired by the triggering of an emotional
episode. The mechanism will be used in the ”Absorb” phase of the resilience
profile and integrated in our agent model. Another mechanism will be designed,
a process of adapting the individual behaviour of subsystems to improve the
resilience of the CPS (the ”Adapt” phase of the resilience profile). Using the
multi-agent paradigm, we design a collective process for detecting abnormal sit-
uations using the information built by the individual detection mechanism. This
collective process is initiated by the behaviour adopted when an abnormal sit-
uation is detected individually. The adaptation of individual behaviour impacts
on the social organization of the group of agents carrying out the control of the
CPS, which will trigger a self-organization in order to collectively adapt to the
abnormal situation.
The mechanisms proposed in this project are generic, not specific to the
tasks of the system. For validating our hypotheses, we have chosen a case of
application, a building’s thermal regulation system (CP Strb ), consisting of a set
of heaters, air conditioners, thermal sensors, thermostats, light sensors, fans and
automatic rolling shutters. The resilience in this system is that CP Strb continues
to ensure the temperatures chosen by users in case of problems. Before defining
the resilience for our CP Strb , we have described the main features, sensors,
actuators of this system as well as the potential problems it may encounter.
In this context, we are interested in problems like: connectivity breakdown, a
node failure, increasing workload issue and adding or removing a node to/from
CP Strb . This cyber-physical system is agentified using DIAMOND method [12].
In this method, there are four phases to move from global characterization to
the specification of local behaviours:
– Situation phase : define the general context of the multi-agent system, i.e.
the environment, the agents with their main capacities and their contexts.
– Individual phase : define agents from an internal point of view (indepen-
dent of social relationships).
– Social phase : describe the interaction and organization from an external
point of view.
– Integration phase : to integrate social influences into agents behaviours.
The proposed processes and multi-agent system will be simulated using MASH2 [14],
a hybrid hardware software simulation platform, in order to quantify experimen-
tally the resilience improvements they bring.
2
MASH (MultiAgent Software/Hardware simulator)
References
1. Disaster resilience: A national imperative. Washington, DC: The National
Academies Press (2012)
2. Adam, C., Canal, R., Gaudou, B., Vinh, H.T., Taillandier, P., et al.: Simulation of
the emotion dynamics in a group of agents in an evacuation situation. In: Interna-
tional Conference on Principles and Practice of Multi-Agent Systems. pp. 604–619.
Springer (2010)
3. Adger, W.N.: Social and ecological resilience: are they related? Progress in human
geography 24(3), 347–364 (2000)
4. Armony, J.L., Servan-Schreiber, D., Cohen, J.D., LeDoux, J.E.: Computational
modeling of emotion: Explorations through the anatomy and physiology of fear
conditioning. Trends in cognitive sciences 1(1), 28–34 (1997)
5. Calvo, R.A., D’Mello, S., Gratch, J.M., Kappas, A.: The Oxford handbook of
affective computing. Oxford University Press, USA (2015)
6. Demazeau, Y.: From interactions to collective behaviour in agent-based systems.
In: In: Proceedings of the 1st. European Conference on Cognitive Science. Saint-
Malo. Citeseer (1995)
7. Di Marzo Serugendo, G., Gleizes, M.P., Karageorgos, A.: Self-organisation and
emergence in multi-agent systems: An overview. Informatica 30(1), 45–54 (2006)
8. Ferber, J., Weiss, G.: Multi-agent systems: an introduction to distributed artificial
intelligence, vol. 1. Addison-Wesley Reading (1999)
9. Frijda, N.H.: The emotions. Cambridge University Press (1986)
10. Frijda, N.H.: Emotion, cognitive structure, and action tendency. Cognition and
emotion 1(2), 115–143 (1987)
11. Hosseini, S., Barker, K., Ramirez-Marquez, J.E.: A review of definitions and mea-
sures of system resilience. Reliability Engineering & System Safety 145, 47–61
(2016)
12. Jamont, J.P., Occello, M.: Designing embedded collective systems: The diamond
multiagent method. In: IEEE International Conference on Tools with Artificial
Intelligence-ICTAI 07. pp. 91–94. IEEE Computer Society (2007)
13. Jamont, J.P., Occello, M.: A self-organization process for communication manage-
ment in embedded multiagent system. In: IEEE/WIC/ACM International Confer-
ence on Intelligent Agent Technology. pp. 51–55. IEEE Computer Society (2007)
14. Jamont, J.P., Occello, M.: Using mash in the context of the design of embed-
ded multiagent system. In: International Conference on Practical Applications of
Agents and Multi-Agent Systems. pp. 283–286. Springer (2013)
15. Lazarus, R.S.: From psychological stress to the emotions: A history of changing
outlooks. Annual review of psychology 44(1), 1–22 (1993)
16. Lazarus, R.S., Lazarus, R.S.: Emotion and adaptation. Oxford University Press on
Demand (1991)
17. LeDoux, J.: The emotional brain: The mysterious underpinnings of emotional life.
Simon and Schuster (1998)
18. Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for industry
4.0-based manufacturing systems. Manufacturing Letters 3, 18–23 (2015)
19. Linkov, I., Eisenberg, D.A., Plourde, K., Seager, T.P., Allen, J., Kott, A.: Resilience
metrics for cyber systems. Environment Systems and Decisions 33(4), 471–476
(2013). https://doi.org/10.1007/s10669-013-9485-y
20. Linkov, I., Kott, A.: Fundamental concepts of cyber resilience: Introduction and
overview. In: Cyber resilience of systems and networks, pp. 1–25. Springer (2019)
21. Mahboub, K.: Modélisation des processus émotionnel dans la prise de
décision.(Emotional processes modelling in decision making). Ph.D. thesis, Uni-
versity of Le Havre, France (2011)
22. Marsella, S., Gratch, J., Petta, P., et al.: Computational models of emotion. A
Blueprint for Affective Computing-A sourcebook and manual 11(1), 21–46 (2010)
23. Masten, A.S., Best, K.M., Garmezy, N.: Resilience and development: Contributions
from the study of children who overcome adversity. Development and psychopathol-
ogy 2(4), 425–444 (1990)
24. Mathieu, P., Routier, J.C., Secq, Y.: Rio: Roles, interactions and organizations. In:
International Central and Eastern European Conference on Multi-Agent Systems.
pp. 147–157. Springer (2003)
25. Mehrabian, A., Russell, J.A.: An approach to environmental psychology. the MIT
Press (1974)
26. Norris, F.H., Stevens, S.P., Pfefferbaum, B., Wyche, K.F., Pfefferbaum, R.L.: Com-
munity resilience as a metaphor, theory, set of capacities, and strategy for disaster
readiness. American journal of community psychology 41(1-2), 127–150 (2008)
27. Raı̈evsky, C., Michaud, F.: Emotion generation based on a mismatch theory of
emotions for situated agents. In: Handbook of Research on Synthetic Emotions
and Sociable Robotics: New Applications in Affective Computing and Artificial
Intelligence, pp. 247–266. IGI Global (2009)
28. Ruault, J.R., Luzeaux, D., Colas, C., Sarron, J.C.: Résilience des systèmes so-
ciotechniques application à l’ingénierie système. Génie logiciel 96, 40–52 (2011)
29. Saunier, J., Jones, H.: Mixed agent/social dynamics for emotion computation. In:
Proceedings of the 2014 international conference on Autonomous agents and multi-
agent systems. pp. 645–652. International Foundation for Autonomous Agents and
Multiagent Systems (2014)
30. Staller, A., Petta, P., et al.: Introducing emotions into the computational study of
social norms: A first evaluation. Journal of artificial societies and social simulation
4(1), U27–U60 (2001)
31. Trivedi, K.S., Kim, D.S., Ghosh, R.: Resilience in computer systems and networks.
In: Proceedings of the 2009 International Conference on Computer-Aided Design.
pp. 74–77. ACM (2009)
32. Tugade, M.M., Fredrickson, B.L.: Resilient individuals use positive emotions to
bounce back from negative emotional experiences. Journal of personality and social
psychology 86(2), 320 (2004)
33. Weiss, G.: Multiagent systems: a modern approach to distributed artificial intelli-
gence. MIT press (1999)
34. Woods, D.D.: Essential characteristics of resilience. In: Resilience engineering, pp.
33–46. CRC Press (2012)