A Probabilistic Implementation of Emotional BDI Agents

João Gluz, Patricia Jaques


A very well known reasoning model in Artificial Intelligence is the BDI (Belief-Desire-Intention). A BDI agent should be able to choose the more rational action to be done with bounded resources and incomplete knowledge in an acceptable time. Although humans need emotions in order to make immediate decisions with incomplete information, traditional BDI models do not take into account affective states of the agent. In this paper we present an implementation of the appraisal process of emotions in BDI agents using a BDI language that integrates logic and probabilistic reasoning. Specifically, we implement the event-generated emotions with consequences for self based on the OCC cognitive psychological theory of emotions. We also present an illustrative scenario and its implementation. One original aspect of this work is that we implement the emotions intensity using a probabilistic extension of a BDI language. This intensity is defined by the desirability central value, as pointed by the OCC model. In this way, our implementation of an emotional BDI allows to differentiate between emotions and affective reactions. This is an important aspect because emotions tend to generate stronger response. Besides, the intensity of the emotion also determines the intensity of an individual reaction.


  1. Adam, C., Herzig, A., and Longin, D. (2009). A logical formalization of the OCC theory of emotions. Synthese, 168(2):201-248.
  2. Bagozzi, R. P., Dholakia, U. M., and Basuroy, S. (2003). How effortful decisions get enacted: the motivating role of decision processes, desires, and anticipated emotions. Journal of Behavioral Decision Making, 16(4):273-295.
  3. Baral, C. and Hunsaker, M. (2007). Using the probabilistic logic programming language p-log for causal and counterfactual reasoning and non-naive conditioning. In Proceedings of the 20th international joint conference on Artifical intelligence, IJCAI'07, pages 243- 249, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
  4. Bordini, R. H., Dastani, M., Dix, J., and Seghrouchni, A. E. F. (2005). Multi-agent programming : languages, platforms and applications., volume 15 of Multiagent systems, artificial societies, and simulated organizations. Springer, New York.
  5. Bordini, R. H., Hübner, J. F., and Wooldridge, M. (2007). Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology). John Wiley & Sons.
  6. Bratman, M. (1990). What is intention? In Cohen, P. R., Morgan, J. L., and Pollack, M. E., editors, Intentions in Communications, Bradford books, pages 15-31. MIT Press, Cambridge.
  7. Damasio, A. R. (1994). Descartes' error : emotion, reason, and the human brain. G.P. Putnam New York.
  8. Dias, J. a. and Paiva, A. (2013). I want to be your friend: establishing relations with emotionally intelligent agents. In Proceedings of the 2013 international conference on Autonomous agents and multiagent systems, AAMAS 7813, pages 777-784, Richland, SC. IFMAS.
  9. Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4):169-200.
  10. Gebhard, P. (2005). Alma: a layered model of affect. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, AAMAS 7805, pages 29-36, New York, NY, USA. ACM.
  11. Isen, A. M. and Patrick, R. (1983). The effect of positive feelings on risk taking: When the chips are down. Organizational Behavior and Human Performance, 31(2):194-202.
  12. Jaques, P. A., Vicari, R., Pesty, S., and Martin, J.-C. (2011). Evaluating a Cognitive-Based Affective Student Model. In D'Mello, S. K., Graesser, A. C., Schuller, B., and Martin, J.-C., editors, International Conference on Affective Computing and Intelligent Interaction (ACII), volume 6974 of Lecture Notes in Computer Science, pages 599-608. Springer.
  13. Jiang, H., Vidal, J., and Huhns, M. N. (2007). International Conference On Autonomous Agents. ACM, New York.
  14. Korb, K. and Nicholson, A. (2003). Bayesian Artificial Intelligence. Chapman & Hall/CRC Computer Science & Data Analysis. Taylor & Francis.
  15. Milch, B. and Koller, D. (2000). Probabilistic models for agents' beliefs and decisions. In Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence, UAI'00, pages 389-396, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
  16. Moors, A., Ellsworth, P. C., Scherer, K. R., and Frijda, N. H. (2013). Appraisal Theories of Emotion: State of the Art and Future Development. Emotion Review, 5(2):119-124.
  17. Ortony, A., Clore, G. L., and Collins, A. (1990). The Cognitive Structure of Emotions. Cambridge University Press.
  18. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  19. Picard, R. W. (2000). Affective Computing. University Press Group Limited.
  20. Plutchik, R. (1980). A general psychoevolutionary theory of emotion. Emotion: Theory, research, and experience, 1(3):3-33.
  21. Raghunathan, R. and Pham, M. T. (1999). All negative moods are not equal: Motivational influences of anxiety and sadness on decision making. Organizational Behavior and Human Decision Processes, 79(1):56- 77.
  22. Rao, A. S. and Georgeff, M. (1995). BDI Agents: from Theory to Practice. Technical Report Technical Note 56, Melbourne, Australia.
  23. Russell, S. J. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence. Pearson Education/Prentice Hall.
  24. Scherer, K. R. (1999). Appraisal theory. In Dalgleish, T. and Power, M., editors, Handbook of Cognition and Emotion, volume 19, chapter 30, pages 637-663. John Wiley & Sons Ltd.
  25. Scherer, K. R. (2000). Psychological models of emotion. In Borod, J., editor, The neuropsychology of emotion, volume 137 of The neuropsychology of emotion, chapter 6, pages 137-162. Oxford University Press.
  26. Signoretti, A., Feitosa, A., Campos, A. M., Canuto, A. M., Xavier-Junior, J. C., and Fialho, S. V. (2011). Using an affective attention focus for improving the reasoning process and behavior of intelligent agents. In Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02, WI-IAT 7811, pages 97-100, Washington, DC, USA. IEEE Computer Society.
  27. Silva, D. and Gluz, J. (2011). AgentSpeak(PL): A New Programming Language for BDI Agents with Integrated Bayesian Network Model. In 2011 International Conference on Information Science and Applications. IEEE.
  28. Steunebrink, B., Dastani, M., and Meyer, J.-J. (2012). A formal model of emotion triggers: an approach for bdi agents. Synthese, 185(1):83-129.
  29. Steunebrink, B. R., Meyer, J.-J. C., and Dastani, M. (2008). A Formal Model of Emotions: Integrating Qualitative and Quantitative Aspects. In Bordini, R., Dastani, M., Dix, J., and Fallah-Seghrouchni, A. E., editors, ECAI 2008. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany.
  30. Van Dyke Parunak, H., Bisson, R., Brueckner, S., Matthews, R., and Sauter, J. (2006). A model of emotions for situated agents. Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems AAMAS 06, 2006:993.
  31. Wooldridge, M. (1999). Intelligent Agents. In Weiss, G., editor, Multiagent systems, pages 27-77. MIT Press, Cambridge, MA, USA.
  32. Wooldridge, M. (2009). An Introduction to MultiAgent Systems. John Wiley & Sons.

Paper Citation

in Harvard Style

Gluz J. and Jaques P. (2014). A Probabilistic Implementation of Emotional BDI Agents . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 121-129. DOI: 10.5220/0004815501210129

in Bibtex Style

author={João Gluz and Patricia Jaques},
title={A Probabilistic Implementation of Emotional BDI Agents},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A Probabilistic Implementation of Emotional BDI Agents
SN - 978-989-758-015-4
AU - Gluz J.
AU - Jaques P.
PY - 2014
SP - 121
EP - 129
DO - 10.5220/0004815501210129