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 language | English |
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Pages | 39-45 |
Publication status | Published - 22 Jun 2015 |
Event | 24th International Joint Conference on Artificial Intelligence - Buenos Aires, Argentina Duration: 25 Jul 2015 → 31 Jul 2015 |
Conference
Conference | 24th International Joint Conference on Artificial Intelligence |
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Abbreviated title | IJCAI 2015 |
Country/Territory | Argentina |
City | Buenos Aires |
Period | 25/07/15 → 31/07/15 |
Bibliographical note
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Yingke Chen
- Centre for Digital Innovation
- Department of Computing & Games - Associate Professor (Research) in Computer Science
Person: Academic