Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams

Conroy, Ross, Zeng, Yifeng, Cavazza, Marc and Chen, Yingke (2015) Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence. AAAI Press, Palo Alto, pp. 39-45. ISBN 9781577357384

_2015_Learning_Behaviors_in_Agents_Systems_with_Interactive_Dynamic_Influence_Diagrams.pdf - Accepted Version

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Official URL: https://www.ijcai.org/Abstract/15/013


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.

Item Type: Book Section
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 08 Jul 2020 10:35
Last Modified: 31 Jul 2021 11:46
URI: http://nrl.northumbria.ac.uk/id/eprint/43702

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