Abstract
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, 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.
- Bexy 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. Google Scholar
Digital Library
- Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2016a. Design of an Affective Intelligent Agent on GenIA. Technical Report. DSIC, UPV, Spain.Google Scholar
- Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2016b. Toward a Systematic Development of Affective Intelligent Agents. Technical Report. DSIC, UPV, Spain.Google Scholar
- Gordon Willard Allport. 1937. Personality: A Psychological Interpretation. Henry Holt, New York.Google Scholar
- Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 2 (1977), 191.Google Scholar
Digital Library
- 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.Google Scholar
- Antoine Bechara, Hanna Damasio, and Antonio R Damasio. 2000. Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex 10, 3 (2000), 295--307. Google Scholar
Cross Ref
- 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.Google Scholar
- Rafael H. Bordini, Jomi Fred Hübner, and Michael Wooldridge. 2007. Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley.Google Scholar
- Tibor Bosse, Joost Broekens, João Dias, and Janneke van der Zwaan. 2014. Emotion Modeling. Springer. Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- Jerome R. Busemeyer, Eric Dimperio, and Ryan K. Jessup. 2007. Integrating Emotional Processes Into Decision-Making Models. Oxford University Press, 29--44. Google Scholar
Cross Ref
- Colin F Camerer, George Loewenstein, and Matthew Rabin. 2011. Advances in Behavioral Economics. Princeton University Press.Google Scholar
- Martin A Conway. 1990. Autobiographical Memory: An Introduction. Open University Press.Google Scholar
- Ronald De Sousa. 1990. The Rationality of Emotion. MIT Press.Google Scholar
- 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_3 Google Scholar
Cross Ref
- Magy 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. Google Scholar
Digital Library
- Hans Jürgen Eysenck. 1982. Personality, Genetics, and Behavior: Selected Papers. Praeger, Chapter Development of a Theory, 1--48.Google Scholar
- Shane Frederick. 2005. Cognitive reflection and decision making. The Journal of Economic Perspectives 19, 4 (2005), 25--42. Google Scholar
Cross Ref
- N. H. Frijda, A. S. R. Manstead, and S. Bem. 2000. Emotions and Beliefs: How Feelings Influence Thoughts. Cambridge University Press. Google Scholar
Cross Ref
- Nico H. Frijda. 2007. The Laws of Emotion. Lawrence Erlbaum Associates, Incorporated.Google Scholar
- 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/10.1145/1082473.1082478 Google Scholar
Digital Library
- Lewis 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.Google Scholar
- James J. Gross and Ross A. Thompson. 2011. Emotion regulation: Conceptual fundations. In Handbook of Emotion Regulation. Guilford Publications.Google Scholar
- 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/s10458-009-9096-7 Google Scholar
Digital Library
- William G. Kennedy. 2012. Modelling human behaviour in agent-based models. In Agent-based Models of Geographical Systems. Springer, 167--179. Google Scholar
Cross Ref
- 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. Google Scholar
Cross Ref
- Richard S. Lazarus and Susan Folkman. 1984. Stress, Appraisal, and Coping. Springer.Google Scholar
- 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/10.1145/860575.860626 Google Scholar
Digital Library
- Stacy C. Marsella and Jonathan Gratch. 2009. EMA: A process model of appraisal dynamics. Cognitive Systems Research 10, 1 (2009), 70--90. Google Scholar
Digital Library
- 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.Google Scholar
- Robert R. McCrae and Oliver P. John. 1992. An introduction to the five-factor model and its applications. Journal of Personality 60, 2 (1992), 175--215. Google Scholar
Cross Ref
- Albert Mehrabian. 1996a. Analysis of the big-five personality factors in terms of the PAD temperament model. Australian Journal of Psychology 48, 2 (1996), 86--92. DOI:http://dx.doi.org/10.1080/00049539608259510 Google Scholar
Cross Ref
- Albert Mehrabian. 1996b. Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology 14, 4 (1996), 261--292. DOI:http://dx.doi.org/10.1007/BF02686918 Google Scholar
Cross Ref
- Albert Mehrabian and James A. Russell. 1974. An Approach to Environmental Psychology. MIT Press.Google Scholar
- John-Jules Ch. Meyer. 2006. Reasoning about emotional agents. International Journal of Intelligent Systems 21, 6 (June 2006), 601--619. DOI:http://dx.doi.org/10.1002/int.v21:6 Google Scholar
Digital Library
- Katherine Nelson. 1993. The psychological and social origins of autobiographical memory. Psychological Science 4, 1 (1993), 7--14. Google Scholar
Cross Ref
- Magalie Ochs, David Sadek, and Catherine Pelachaud. 2012. 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-z Google Scholar
Digital Library
- Andrew 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.Google Scholar
- Andrew Ortony, Gerald L. Clore, and Allan Collins. 1988. The Cognitive Structure of Emotions. Cambridge University Press. Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- Gordon D. Plotkin. 1981. A Structural Approach to Operational Semantics. Technical Report DAIMI FN-19. Aarhus University.Google Scholar
- 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. Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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-x Google Scholar
Cross Ref
- Ira J. Roseman. 2001. A Model of Appraisal in the Emotion System: Integrating Theory, Research, and Applications. Oxford University Press, 68--91.Google Scholar
- James A. Russell. 2003. Core affect and the psychological construction of emotion. Psychological Review 110, 1 (2003), 145--172. Google Scholar
Cross Ref
- Klaus R. Scherer. 2001. Appraisal considered as a process of multilevel sequential checking. Appraisal Processes in Emotion: Theory, Methods, Research 92 (2001), 120.Google Scholar
- Norbert Schwarz. 2000. Emotion, cognition, and decision making. Cognition 8 Emotion 14, 4 (2000), 433--440.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Craig A. Smith and Richard S. Lazarus. 1990. Emotion and adaptation. In Handbook of Personality: Theory and Research, Lawrence A. Pervin (Ed.). 609--637.Google Scholar
- Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer. 2009. A formal model of emotion-based action tendency for intelligent agents. In Proceedings of EPIA’09. Springer-Verlag, Berlin, 174--186. DOI:http://dx.doi.org/10.1007/978-3-642-04686-5_15 Google Scholar
Digital Library
- Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer. 2012. A formal model of emotion triggers: An approach for BDI agents. Synthese 185 (2012), 83--129. DOI:http://dx.doi.org/10.1007/s11229-011-0004-8 Google Scholar
Cross Ref
- AW Tucker. 1983. The mathematics of tucker: A sampler. The Two-Year College Mathematics Journal 14, 3 (1983), 228--232. Google Scholar
Cross Ref
- Renata Vieira, Álvaro F. Moreira, Michael Wooldridge, and Rafael H. Bordini. 2007. On the formal semantics of speech-act based communication in an agent-oriented programming language. J. Artif. Intell. Res. (JAIR) 29 (2007), 221--267.Google Scholar
Cross Ref
- G. Weiss. 2013. Multiagent Systems. MIT Press.Google Scholar
Index Terms
Toward Formal Modeling of Affective Agents in a BDI Architecture
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