Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams

Ross Conroy, Yifeng Zeng, Marc Cavazza, Yingke Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)
2 Downloads (Pure)

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 behavior 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
Title of host publicationProceedings of the twenty-fourth international joint conference on artificial intelligence
EditorsQiang Yang, Michael J. Wooldridge
Place of PublicationPalo Alto
PublisherAssociation for the Advancement of Artificial Intelligence Press
Pages39-45
Number of pages7
ISBN (Print)9781577357384
Publication statusPublished - 1 Nov 2015
Externally publishedYes
EventThe Twenty-Fourth International Joint Conference on Artificial Intelligence -
Duration: 25 Jul 201531 Jul 2015

Conference

ConferenceThe Twenty-Fourth International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
Period25/07/1531/07/15

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