Learning Social Interactions

One important feature of an intelligent agent is its ability to make rational decisions based on its current knowledge of the environment. If the environment of the agent is not static, i.e., there are other active entities, e.g., other agents or humans in the environment, it is crucial to model these entities for making rational decisions.

Our earlier research in this areas has been directed at learning social interactions between agents based on, for example, Markov games. The reinforcement learning approach based on Markov games provides a complete model of interactions between learning agents.

Publications

• Könönen, V.J., 2004. Asymmetric multiagent reinforcement learning. Web Intelligence and Agent Systems: An International Journal (WIAS) 2, number 2, pages 105-121.
• Könönen, V.J., 2004. Hybrid model for multiagent reinforcement learning. Proceedings of the International Joint Conference on Neural Networks (IJCNN-2004). Budapest, Hungary, 25-29 July 2004, pages 1793-1798.
• Könönen, V.J., 2004. Policy gradient method for team Markov games. Proceedings of the Fifth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL-2004). Exeter, UK, 25-27 August 2004. Heidelberg, Springer-Verlag. Lecture Notes in Computer Science 3177, pages 733-739.
• Könönen, V.J. and Oja, E., 2004. Asymmetric multiagent reinforcement learning in pricing applications. Proceedings of the International Joint Conference on Neural Networks (IJCNN-2004). Budapest, Hungary, 25-29 July 2004, pages 1097-1102.
• Skripal, P. and Honkela, T. Framework for Modeling Emotions in Communities of Agents. In: H. Hyötyniemi, P. Ala-Siuru and J. Seppänen (eds.), Life, Cognition and Systems Sciences, Symposium Proceedings of the 11th Finnish Artificial Intelligence Conference, Finnish Science Center Heureka Vantaa, 1-3 September 2004, pp. 163-172.
• Honkela, T., Hynna, K. I. and Knuuttila, T. Framework for Modeling Partial Conceptual Autonomy of Adaptive and Communicating Agents. Proceedings of CogSci2003, 25th Annual Meeting of Cognitive Science Society, Boston, Massachusetts, July 31-August 2, 2003.
• Honkela, T. and Winter, J. Simulating Language Learning in Community of Agents Using Self-Organizing Maps, Report A71, Helsinki University of Technology, Laboratory of Computer and Information Science, December, 2003.

Links

• Computational Cognitive Systems research group

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