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Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams

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)

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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 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)
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: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: Marc Cavazza
Date Deposited: 19 May 2016 13:45 UTC
Last Modified: 01 Aug 2019 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55618 (The current URI for this page, for reference purposes)
Cavazza, Marc: https://orcid.org/0000-0001-6113-9696
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