Towards Formal Modeling of Affective Agents in a BDI Architecture

[EN] Affective characteristics are crucial factors that influence human behavior, and often the prevalence of either emotions or reason varies on each individual. We aim to facilitate the development of agents reasoning considering their affective characteristics. We first identify core processes in an affective BDI agent, and we integrate them into an affective agent architecture (GenIA3). These tasks include the extension of the BDI agent reasoning cycle to be compliant with the architecture, and the extension of the agent language (Jason) to support affect-based reasoning, and the adjustment of the equilibrium between the agent s affective and rational sides.This work was supported by the Generalitat Valenciana grant PROMETEOII/2013/019, and the Spanish TIN2014-55206-R project of the Ministerio de Economa y Competitividad.Alfonso Espinosa, B.; Vivancos, E.; Botti, V. (2017). Towards Formal Modeling of Affective Agents in a BDI Architecture. ACM Transactions on Internet Technology. 17(1):5:1-5:23. https://doi.org/10.1145/3001584S5:15:23171Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2014. An open architecture for affective traits in a BDI agent. In Proceedings of the 6th ECTA 2014. Part of the 6th IJCCI 2014. 320--325.Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2016a. Design of an Affective Intelligent Agent on GenIA. Technical Report. DSIC, UPV, Spain.Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2016b. Toward a Systematic Development of Affective Intelligent Agents. Technical Report. DSIC, UPV, Spain.Gordon Willard Allport. 1937. Personality: A Psychological Interpretation. Henry Holt, New York.Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 2 (1977), 191.Cristina Battaglino, Rossana Damiano, and Leonardo Lesmo. Emotional range in value-sensitive deliberation. In Proceedings of AAMAS’13. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 769--776.Antoine Bechara, Hanna Damasio, and Antonio R Damasio. 2000. Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex 10, 3 (2000), 295--307.Rafael H. Bordini and Jomi Fred Hübner. 2010. Semantics for the Jason variant of AgentSpeak (plan failure and some internal actions). In Proceedings of ECAI’10. IOS Press, Amsterdam, The Netherlands, 635--640.Rafael H. Bordini, Jomi Fred Hübner, and Michael Wooldridge. 2007. Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley.Tibor Bosse, Joost Broekens, João Dias, and Janneke van der Zwaan. 2014. Emotion Modeling. Springer.Scott Brave, Clifford Nass, and Kevin Hutchinson. 2005. Computers that care: Investigating the effects of orientation of emotion exhibited by an embodied computer agent. International Journal of Human-computer Studies 62, 2 (2005), 161--178.Jerome R. Busemeyer, Eric Dimperio, and Ryan K. Jessup. 2007. Integrating Emotional Processes Into Decision-Making Models. Oxford University Press, 29--44.Colin F Camerer, George Loewenstein, and Matthew Rabin. 2011. Advances in Behavioral Economics. Princeton University Press.Martin A Conway. 1990. Autobiographical Memory: An Introduction. Open University Press.Ronald De Sousa. 1990. The Rationality of Emotion. MIT Press.João Dias, Samuel Mascarenhas, and Ana Paiva. 2014. FAtiMA Modular: Towards an Agent Architecture with a Generic Appraisal Framework. Springer International Publishing, 44--56. DOI:http://dx.doi.org/10.1007/978-3-319-12973-0_3Magy Seif El-Nasr, John Yen, and Thomas R Ioerger. 2000. Flame—fuzzy logic adaptive model of emotions. Autonomous Agents and Multi-agent systems 3, 3 (2000), 219--257.Hans Jürgen Eysenck. 1982. Personality, Genetics, and Behavior: Selected Papers. Praeger, Chapter Development of a Theory, 1--48.Shane Frederick. 2005. Cognitive reflection and decision making. The Journal of Economic Perspectives 19, 4 (2005), 25--42.N. H. Frijda, A. S. R. Manstead, and S. Bem. 2000. Emotions and Beliefs: How Feelings Influence Thoughts. Cambridge University Press.Nico H. Frijda. 2007. The Laws of Emotion. Lawrence Erlbaum Associates, Incorporated.Patrick Gebhard. 2005. ALMA: A layered model of affect. In Proceedings of the 4th AAMAS. ACM, New York, NY, 29--36. DOI:http://dx.doi.org/10.1145/1082473.1082478Lewis R. Goldberg and others. 1990. An alternative “description of personality”: The big-five factor structure. Journal of Personality and Social Psychology 59, 6 (1990), 1216--1229.James J. Gross and Ross A. Thompson. 2011. Emotion regulation: Conceptual fundations. In Handbook of Emotion Regulation. Guilford Publications.JonathanY. Ito, DavidV. Pynadath, and StacyC. Marsella. 2010. Modeling self-deception within a decision-theoretic framework. AAMAS 20, 1 (2010), 3--13. DOI:http://dx.doi.org/10.1007/s10458-009-9096-7William G. Kennedy. 2012. Modelling human behaviour in agent-based models. In Agent-based Models of Geographical Systems. Springer, 167--179.Jonathan Klein, Youngme Moon, and Rosalind W. Picard. 2002. This computer responds to user frustration: Theory, design, and results. Interacting with Computers 14, 2 (2002), 119--140.Richard S. Lazarus and Susan Folkman. 1984. Stress, Appraisal, and Coping. Springer.Stacy Marsella and Jonathan Gratch. 2003. Modeling coping behavior in virtual humans: Don’t worry, be happy. In Proceedings of AAMAS’03. ACM, 313--320. DOI:http://dx.doi.org/10.1145/860575.860626Stacy C. Marsella and Jonathan Gratch. 2009. EMA: A process model of appraisal dynamics. Cognitive Systems Research 10, 1 (2009), 70--90.Stacy C. Marsella, Jonathan Gratch, and Paolo Petta. 2010. Computational models of emotion. In A Blueprint for Affective Computing: A Sourcebook and Manual. OUP Oxford, 21--46.Robert R. McCrae and Oliver P. John. 1992. An introduction to the five-factor model and its applications. 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A formal model of emotions for an empathic rational dialog agent. AAMAS 24, 3 (2012), 410--440. DOI:http://dx.doi.org/10.1007/s10458-010-9156-zAndrew Ortony. 2003. On making believable emotional agents believable. In Emotions in Humans and Artifacts, R. P. Trapple, P. Petta, and S. Payer (Eds.). MIT Press, Chapter 6, 189--212.Andrew Ortony, Gerald L. Clore, and Allan Collins. 1988. The Cognitive Structure of Emotions. Cambridge University Press.Rosalind W. Picard and Karen K. Liu. 2007. Relative subjective count and assessment of interruptive technologies applied to mobile monitoring of stress. International Journal of Human-Computer Studies 65, 4 (2007), 361--375.César F. Pimentel and Maria R. Cravo. 2005. Affective revision. In Progress in Artificial Intelligence, Carlos Bento, Amílcar Cardoso, and Gaël Dias (Eds.). LNCS, Vol. 3808. Springer Berlin, 115--126.Gordon D. Plotkin. 1981. A Structural Approach to Operational Semantics. Technical Report DAIMI FN-19. Aarhus University.Anand S. Rao. 1996. Agentspeak(L): BDI agents speak out in a logical computable language. In Proceedings of the 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Rudy Van Hoe (Ed.). Eindhoven, The Netherlands.Rainer Reisenzein, Eva Hudlicka, Mehdi Dastani, Jonathan Gratch, Koen Hindriks, Emiliano Lorini, and J-JC Meyer. 2013. Computational modeling of emotion: Toward improving the inter- and intradisciplinary exchange. IEEE Transactions on Affective Computing 4, 3 (2013), 246--266.Luis-Felipe Rodríguez and Félix Ramos. 2014. Development of computational models of emotions for autonomous agents: A review. Cognitive Computation 6, 3 (2014), 351--375. DOI:http://dx.doi.org/10.1007/s12559-013-9244-xIra J. Roseman. 2001. A Model of Appraisal in the Emotion System: Integrating Theory, Research, and Applications. Oxford University Press, 68--91.James A. Russell. 2003. Core affect and the psychological construction of emotion. Psychological Review 110, 1 (2003), 145--172.Klaus R. Scherer. 2001. Appraisal considered as a process of multilevel sequential checking. Appraisal Processes in Emotion: Theory, Methods, Research 92 (2001), 120.Norbert Schwarz. 2000. Emotion, cognition, and decision making. Cognition 8 Emotion 14, 4 (2000), 433--440.Leila Selimbegović, Isabelle Régner, Pascal Huguet, and Armand Chatard. 2015. On the power of autobiographical memories: From threat and challenge appraisals to actual behaviour. Memory (2015), 1--8.Martin Sewell. 2010. Emotions help solve the prisoner’s dilemma. In Proceedings of the Behavioural Finance Working Group Conference: Fairness, Trust and Emotions in Finance, London. 1--2.Craig A. Smith and Richard S. Lazarus. 1990. Emotion and adaptation. In Handbook of Personality: Theory and Research, Lawrence A. Pervin (Ed.). 609--637.Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer. 2009. A formal model of emotion-based action tendency for intelligent agents. 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Affect and believability in game characters:a review of the use of affective computing in games

Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions

Computational Modeling of Emotion: Towards Improving the Inter- and Intradisciplinary Exchange

International audienceThe past years have seen increasing cooperation between psychology and computer science in the field of computational modeling of emotion. However, to realize its potential, the exchange between the two disciplines, as well as the intradisciplinary coordination, should be further improved. We make three proposals for how this could be achieved. The proposals refer to: 1) systematizing and classifying the assumptions of psychological emotion theories; 2) formalizing emotion theories in implementation-independent formal languages (set theory, agent logics); and 3) modeling emotions using general cognitive architectures (such as Soar and ACT-R), general agent architectures (such as the BDI architecture) or general-purpose affective agent architectures. These proposals share two overarching themes. The first is a proposal for modularization: deconstruct emotion theories into basic assumptions; modularize architectures. The second is a proposal for unification and standardization: Translate different emotion theories into a common informal conceptual system or a formal language, or implement them in a common architecture

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

[EN] The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be "adapted " when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA(3) architecture.This work is partially supported by the Spanish Government projects PID2020-113416RB-I00, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Alfonso, B.; Taverner-Aparicio, JJ.; Vivancos, E.; Botti, V. (2021). From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents. Applied Sciences. 11(22):1-29. https://doi.org/10.3390/app112210874S129112

Normative Emotional Agents: a viewpoint paper

[EN] Human social relationships imply conforming to the norms, behaviors and cultural values of the society, but also socialization of emotions, to learn how to interpret and show them. In multiagent systems, much progress has been made in the analysis and interpretation of both emotions and norms. Nonetheless, the relationship between emotions and norms has hardly been considered and most normative agents do not consider emotions, or vice-versa. In this article, we provide an overview of relevant aspects within the area of normative agents and emotional agents. First we focus on the concept of norm, the different types of norms, its life cycle and a review of multiagent normative systems. Secondly, we present the most relevant theories of emotions, the life cycle of an agent¿s emotions, and how emotions have been included through computational models in multiagent systems. Next, we present an analysis of proposals that integrate emotions and norms in multiagent systems. From this analysis, four relationships are detected between norms and emotions, which we analyze in detail and discuss how these relationships have been tackled in the reviewed proposals. Finally, we present a proposal for an abstract architecture of a Normative Emotional Agent that covers these four norm-emotion relationships.This work was supported by the Spanish Government project TIN2017-89156- R, the Generalitat Valenciana project PROMETEO/2018/002 and the Spanish Goverment PhD Grant PRE2018-084940.Argente, E.; Del Val, E.; Pérez-García, D.; Botti Navarro, VJ. (2022). Normative Emotional Agents: a viewpoint paper. IEEE Transactions on Affective Computing. 13(3):1254-1273. https://doi.org/10.1109/TAFFC.2020.3028512S1254127313

Agents with Affective Traits for Decision-Making in Complex Environments

Recent events have probably lead us to wonder why people make decisions that seem to be irrational, and that go against any easily understandable logic. The fact that these decisions are emotionally driven often explains what, at first glance, does not have a plausible explanation. Evidence has been found that proves that emotions and other affective characteristics guide decisions beyond a purely rational deliberation. Understanding the way emotions take place, the way emotions change, and/or the way emotions influence behavior, has traditionally been a concern of several fields including psychology and neurology. Moreover, other sciences such as behavioral economics, artificial intelligence, and in general, all sciences that aim to understand, explain, or simulate human behavior, acknowledge the important role of affective characteristics in this task. Specifically, artificial intelligence uses psychological findings in order to create agents that simulate human behavior. Nevertheless, individual research efforts in modeling affective characteristics are often overlapped, short of integration, and they lack of a common conceptual system. This deprives individual researches of the exchange and cooperation's inherent benefits, and makes the task of computationally simulating affective characteristics more difficult. Although much individual effort has been put in classifying, formalizing and modeling emotions and emotion theories on some fields, recognized researchers of emotions' and affective processes' modeling report that a common formal language, an informal conceptual system, and a general purpose affective agent architecture will greatly improve the interdisciplinary exchange and the intradisciplinary coordination. The research literature proposes a wide amount of affective models that deal with some of: relationship between emotions and cognition, relationship between emotions and behavior, emotions and their evolutionary account, emotions for appraising situations, emotion regulation, etc. These models are useful tools for addressing particular emotion-related issues. Furthermore, computational approaches that are based on particular psychological theories have also been proposed. They often address domain specific issues starting from a specific psychological theory. In such solutions, the absence of a common conceptual system and/or platform, makes difficult the feedback between psychological theories and computational approaches. This thesis systematizes and formalizes affect-related theories, what can benefit the interdisciplinary exchange, the intradisciplinary coordination, and hence, allows the improvement of involved disciplines. Specifically this thesis makes the following contributions: (1) a theoretical framework that includes the main processes and concepts that a model of an affective agent with practical reasoning should have; (2) a general-purpose affective agent architecture that shares the concepts of the proposed theoretical framework; (3) an implementation-independent formal language for designing affective agents that have the proposed architecture; and (4) a specific agent language for implementing affective agents which is an extension of a BDI language. Some studies with human participants have helped to validate the contributions of this thesis. They include classical games of game theory, and an study with 300 participants, which have provided the necessary information to evaluate the contributions. The validation has been performed in three directions: determine whether the proposed computational approach represents better the human behavior than traditional computational approaches; determine whether this approach allows to improve psychological theories used by default; and determine whether the proposed affective agents' behavior is closer to human behavior than the behavior of a purely rational agent.Probablemente algunos eventos recientes nos han conducido a preguntarnos por qué las personas toman decisiones aparentemente irracionales y en contra de alguna lógica fácilmente comprensible. El hecho de que estas decisiones estén bajo la influencia de las emociones a menudo explica lo que, a primera vista, parece no tener una explicación aceptable. En este sentido, se han encontrado evidencias que prueban que las emociones y otras características afectivas condicionan las decisiones más allá de una deliberación meramente racional. Entender cómo las emociones tienen lugar, cómo cambian y cómo influyen en el comportamiento, ha sido tradicionalmente de interés para muchos campos de investigación, incluyendo la psicología y la neurología. Además, otras ciencias como la economía conductual o la inteligencia artificial reconocen el importante papel de las características afectivas en esta tarea. Específicamente, la inteligencia artificial utiliza los resultados obtenidos en psicología para crear agentes que simulan el comportamiento humano. Sin embargo, a menudo los esfuerzos individuales de investigación en el modelado del afecto se solapan, carecen de la suficiente integración y de un sistema conceptual común. Esto limita a las investigaciones individuales para disponer de los beneficios que ofrecen el intercambio y la cooperación, y hace más compleja la tarea de simular los procesos afectivos. Las emociones y teorías relacionadas han sido clasificadas, formalizadas y modeladas. No obstante, reconocidos investigadores argumentan que un lenguaje formal común, un sistema conceptual informal y una arquitectura de agentes de propósito general, mejorarán significativamente el intercambio interdisciplinar y la coordinación intradisciplinar. En la literatura se propone una amplia cantidad de modelos afectivos que modelan: la relación entre las emociones y la cognición, la relación entre las emociones y el comportamiento, las emociones para evaluar las situaciones, la regulación de emociones, etc. Estos modelos son herramientas útiles para abordar aspectos particulares relacionados con las emociones. Además, se han realizado propuestas computacionales que abordan aspectos específicos sobre la base de teorías psicológicas específicas. En éstas soluciones, la ausencia de una plataforma y/o sistema conceptual dificulta la retroalimentación entre las teorías psicológicas y las propuestas computacionales. Esta tesis sistematiza y formaliza teorías relacionadas con el afecto, lo cual beneficia el intercambio interdisciplinar y la coordinación intradisciplinar, y por tanto, permite el desarrollo de las disciplinas correspondientes. Específicamente esta tesis realiza las siguientes contribuciones: (1) una plataforma teórica que incluye los conceptos y procesos principales que debería poseer un modelo de agentes afectivos con razonamiento práctico; (2) una arquitectura de agentes de propósito general que comparte los conceptos de la plataforma teórica propuesta; (3) un lenguaje formal independiente de la implementación, para diseñar agentes afectivos que poseen la arquitectura propuesta; y (4) un lenguaje de agentes específico para implementar agentes afectivos el cual es un extensión de un lenguaje BDI. Algunos estudios con participantes humanos han ayudado a validar las contribuciones de esta tesis. Estos incluyen juegos clásicos de teoría de juegos y un estudio con 300 participantes, los cuales han proporcionado la información necesaria para evaluar las contribuciones. La validación se ha realizado en tres direcciones: determinar si la propuesta computacional que se ha realizado representa mejor el comportamiento humano que propuestas computacionales tradicionales; determinar si esta propuesta permite mejorar las teorías psicológicas empleadas por defecto; y determinar si el comportamiento de los agentes afectivos propuestos se acerca más al comportamiento humano que el comporProbablement alguns esdeveniments recents ens han conduït a preguntar-nos per què les persones prenen decisions que aparentment són irracionals i que van en contra d'algun tipus de lògica fàcilment comprensible. El fet que aquestes decisions estiguin sota la influència de les emocions sovint explica el que, a primera vista, sembla no tenir una explicació acceptable. En aquest sentit, s'han trobat evidències que proven que les emocions i altres característiques afectives condicionen les decisions més enllà d'una deliberació merament racional. Entendre com les emocions tenen lloc, com canvien i com influeixen en el comportament, ha estat tradicionalment d'interès per a molts camps d'investigació, incloent la psicologia i la neurologia. A més, altres ciències com l'economia conductual, la intel·ligència artificial i, en general, totes les ciències que intenten entendre, explicar o simular el comportament humà, reconeixen l'important paper de les característiques afectives en aquesta tasca. Específicament, la intel·ligència artificial utilitza els resultats obtinguts en psicologia per crear agents que simulen el comportament humà. No obstant això, sovint els esforços individuals d'investigació en el modelatge de l'afecte es solapen, no tenen la suficient integració ni compten amb un sistema conceptual comú. Això limita a les investigacions individuals, que no poden disposar dels beneficis que ofereixen l'intercanvi i la cooperació, i fa més complexa la tasca de simular els processos afectius. Les emocions i teories relacionades han estat classificades, formalitzades i modelades. No obstant això reconeguts investigadors argumenten que un llenguatge formal comú, un sistema conceptual informal i una arquitectura d'agents de propòsit general, milloraran significativament l'intercanvi interdisciplinar i la coordinació intradisciplinar. En la literatura es proposa una àmplia quantitat de models afectius que modelen: la relació entre les emocions i la cognició, la relació entre les emocions i el comportament, les emocions per avaluar les situacions, la regulació d'emocions, etc. Aquests models són eines útils per abordar aspectes particulars relacionats amb les emocions. A més, s'han realitzat propostes computacionals que aborden aspectes específics sobre la base de teories psicològiques específiques. En aquestes solucions, l'absència d'una plataforma i/o sistema conceptual dificulta la retroalimentació entre les teories psicològiques i les propostes computacionals. Aquesta tesi sistematitza i formalitza teories relacionades amb l'afecte, la qual cosa beneficia l'intercanvi interdisciplinar i la coordinació intradisciplinar, i per tant, permet el desenvolupament de les disciplines corresponents. Específicament aquesta tesi realitza les següents contribucions: (1) una plataforma teòrica que inclou els conceptes i processos principals que hauria de posseir un model d'agents afectius amb raonament pràctic; (2) una arquitectura d'agents de propòsit general que comparteix els conceptes de la plataforma teòrica proposta; (3) un llenguatge formal independent de la implementació, per dissenyar agents afectius que posseeixen l'arquitectura proposada; i (4) un llenguatge d'agents específic per implementar agents afectius el qual és un extensió d'un llenguatge BDI. Alguns estudis amb participants humans han ajudat a validar les contribucions d'aquesta tesi. Aquests inclouen jocs clàssics de teoria de jocs i un estudi amb 300 participants, els quals han proporcionat la informació necessària per avaluar les contribucions. La validació s'ha realitzat en tres direccions: determinar si la proposta computacional que s'ha realitzat representa millor el comportament humà que propostes computacionals tradicionals; determinar si aquesta proposta permet millorar les teories psicològiques emprades per defecte; i determinar si el comportament dels agents afectius proposats s'acosta més alAlfonso Espinosa, B. (2017). Agents with Affective Traits for Decision-Making in Complex Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90497TESI

ABC-EBDI: A cognitive-affective framework to support the modeling of believable intelligent agents.

El Grupo de Investigación de Interfaces Avanzadas (AffectiveLab), es un grupo reconocido por el Gobierno de Aragón (T60-20R) cuya actividad se enmarca en el área de la Interacción Humano-Computadora (IHC). Su actividad investigadora se ha centrado, en los últimos años, en cuatro temas principales: interacción natural, informática afectiva, accesibilidad e interfaces basadas en agentes inteligentes, siendo esta última en la que se enmarca esta tesis doctoral. Más concretamente, la realización de esta tesis doctoral se enmarca dentro de los proyectos de investigación nacionales JUGUEMOS (TIN2015-67149-C3-1R) y PERGAMEX (RTI2018-096986-B-C31). Una de sus líneas de investigación se centra en el desarrollo de arquitecturas cognitivo-afectivas para apoyar el modelado afectivo de los agentes inteligentes. El AffectiveLab tiene una sólida experiencia en el uso de agentes de interfaz incorporados que exhiben expresiones afectivas corporales y faciales (Baldassarri et al., 2008). En los últimos años, se han centrado en el modelado del comportamiento de los agentes inteligentes (Pérez et al., 2017).La definición de agente inteligente es un tema controvertido, pero se puede decir que es una entidad autónoma que recibe información dinámica del entorno a través de sensores y actúa sobre el medio ambiente a través de actuadores, mostrando un comportamiento dirigido a un objetivo (Russell et al., 2003). El modelado de los procesos cognitivos en los agentes inteligentes se basa en diferentes teorías (Moore, 1980; Newell, 1994; Bratman, 1987) que explican, desde diferentes puntos de vista, el funcionamiento de la mente humana. Los agentes inteligentes implementados sobre la base de una teoría cognitiva se conocen como agentes cognitivos. Los más desarrollados son los que se basan en arquitecturas cognitivas, como Soar (Laird et al., 1987), ACT-R (Anderson, 1993) y BDI (Rao and Georgeff, 1995). Comparado con Soar y otras arquitecturas complejas, BDI se destaca por su simplicidad y versatilidad. BDI ofrece varias características que la hacen popular, como su capacidad para explicar el comportamiento del agente en cada momento, haciendo posible una interacción dinámica con el entorno. Debido a la creciente popularidad del marco BDI se ha utilizado para apoyar el modelado de agentes inteligentes (Larsen, 2019; (Cranefield and Dignum, 2019). En los últimos años, también han aparecido propuestas de BDI que integran aspectos afectivos. Los agentes inteligentes construidos en base a la arquitectura BDI que también incorporan capacidades afectivas, se conocen como agentes EBDI (Emotional BDI) y son el foco de esta tesis. El objetivo principal de esta tesis ha sido proponer un marco cognitivo-afectivo basado en el BDI que sustente el modelado cognitivo-afectivo de los agentes inteligentes. La finalidad es ser capaz de reproducir un comportamiento humano creíble en situaciones complejas donde el comportamiento humano es variado y bastante impredecible. El objetivo propuesto se ha logrado con éxito en los términos descritos a continuación:• Se ha elaborado un exhaustivo estado del arte relacionado con los modelos afectivos más utilizados para modelar los aspectos afectivos en los agentes inteligentes.• Se han estudiado las arquitecturas de BDI y las propuestas previas de EBDI. El estudio, que dio lugar a una publicación (Sánchez-López and Cerezo, 2019), permitió detectar las cuestiones abiertas en el área, y la necesidad de considerar todos los aspectos de la afectividad (emociones, estado de ánimo, personalidad) y su influencia en todas las etapas cognitivas. El marco resultante de este trabajo doctoral incluye también el modelado de la conducta y el comportamiento comunicativo, que no habían sido considerados hasta ahora en el modelado de los agentes inteligentes. Estos aspectos colocan al marco resultante entre EBDI los más avanzados de la literatura. • Se ha diseñado e implementado un marco basado en el BDI para soportar el modelado cognitivo, afectivo y conductual de los agentes inteligentes, denominado ABC-EBDI (Sanchez et al., 2020) (Sánchez et al., 2019). Se trata de la primera aplicación de un modelo psicológico muy conocido, el modelo ABC de Ellis, a la simulación de agentes inteligentes humanos realistas. Esta aplicación implica:o La ampliación del concepto de creencias. En el marco se consideran tres tipos de creencias: creencias básicas, creencias de contexto y comportamientos operantes. Las creencias básicas representan la información general que el agente tiene sobre sí mismo y el entorno. Las conductas operantes permiten modelar la conducta reactiva del agente a través de las conductas aprendidas. Las creencias de contexto, que se representan en forma de cogniciones frías y calientes, se procesan para clasificarlas en creencias irracionales y racionales siguiendo las ideas de Ellis. Es la consideración de creencias irracionales/racionales porque abre la puerta a la simulación de reacciones humanas realistas.o La posibilidad de gestionar de forma unificada las consecuencias de los acontecimientos en términos de consecuencias afectivas y de comportamiento (conducta). Las creencias de contexto racionales conducen a emociones funcionales y a una conducta adaptativa, mientras que las creencias de contexto irracionales conducen a emociones disfuncionales y a una conducta maladaptativa. Este carácter funcional/disfuncional de las emociones no se había utilizado nunca antes en el contexto del BDI. Además, el modelado conductual se ha ampliado con el modelado de estilos comunicativos, basado en el modelo Satir, tampoco aplicado previamente al modelado de agentes inteligentes. El modelo de Satir considera gestos corporales, expresiones faciales, voz, entonación y estructuras lingüísticas.• Se ha elegido un caso de uso, "I wish a had better news" para la aplicación del marco propuesto y se han realizado dos tipos de evaluaciones, por parte de expertos y de usuarios. La evaluación ha confirmado el gran potencial del marco propuesto para reproducir un comportamiento humano realista y creíble en situaciones complejas.<br /

Towards a Model of Open and Reliable Cognitive Multiagent Systems: Dealing with Trust and Emotions

 Open multiagent systems are those in which the agents can enter or leave the system freely. In these systems any entity with unknown intention can occupy the environment. For this scenario trust and reputation mechanisms should be used to choose partners in order to request services or delegate tasks. Trust and reputation models have been proposed in the Multiagent Systems area as a way to assist agents to select good partners in order to improve interactions between them. Most of the trust and reputation models proposed in the literature take into account their functional aspects, but not how they affect the reasoning cycle of the agent. That is, under the perspective of the agent, a trust model is usually just a “black box” and the agents usually does not take into account their emotional state to make decisions as well as humans often do. As well as trust, agent’s emotions also have been studied with the aim of making the actions and reactions of the agents more like those of humans being in order to imitate their reasoning and decision making mechanisms. In this paper we analyse some proposed models found in the literature and propose a BDI and multi-context based agent model which includes emotional reasoning to lead trust and reputation in open multiagent systems