Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams

Ross Conroy, Yifeng Zeng, Marc Cavazza, Yingke Chen

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Abstract

Interactive dynamic influence diagrams (I-DIDs) are a well recognized decision model that explicitly considers how multiagent interaction affects individual decision making. To predict behavior of other agents, I-DIDs require models of the other agents to be known ahead of time and manually encoded. This becomes a barrier to I-DID applications in a human-agent interaction setting, such as development of intelligent non-player characters (NPCs) in real-time strategy (RTS) games, where models of other agents or human players are often inaccessible to domain experts. In this paper, we use automatic techniques for learning behaviour of other agents from replay data in RTS games. We propose a learning algorithm with improvement over existing work by building a full profile of agent behavior. This is the first time that data-driven learning techniques are embedded into the I-DID decision making framework. We evaluate the performance of our approach on two test cases.
Original languageEnglish
Pages39-45
Publication statusPublished - 22 Jun 2015
Event24th International Joint Conference on Artificial Intelligence - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015

Conference

Conference24th International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2015
CountryArgentina
CityBuenos Aires
Period25/07/1531/07/15

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    Conroy, R., Zeng, Y., Cavazza, M., & Chen, Y. (2015). Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams. 39-45. Paper presented at 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina.