Conroy, Ross, Zeng, Yifeng, Cavazza, Marc, Chen, Yingke (2015) Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams. In: Proceedings of the 24th International Conference on Artificial Intelligence, 25-31 July 2015, Buenos Aires, Argentina. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:55618)
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Language: English Restricted to Repository staff only |
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Official URL: http://dl.acm.org/citation.cfm?id=2832249.2832255 |
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.
Item Type: | Conference or workshop item (Paper) |
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Uncontrolled keywords: | Dynamic Influence Diagrams, Agents Behaviour |
Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Marc Cavazza |
Date Deposited: | 19 May 2016 13:45 UTC |
Last Modified: | 10 Oct 2023 12:59 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/55618 (The current URI for this page, for reference purposes) |
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