A Value Equivalence Approach for Solving Interactive Dynamic Influence Diagrams

Conroy, Ross, Zeng, Yifeng, Cavazza, Marc, Tang, Jing and Pan, Yinghui (2016) A Value Equivalence Approach for Solving Interactive Dynamic Influence Diagrams. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems, New York, NY, pp. 1162-1170. ISBN 9781450342391

[img]
Preview
Text
2016_A_Value_Equivalence_Approach_for_Solving_Interactive_Dynamic_Influence_Diagrams.pdf - Accepted Version

Download (1MB) | Preview

Abstract

Interactive dynamic influence diagrams (I-DIDs) are recognized graphical models for sequential multiagent decision making under uncertainty. They represent the problem of how a subject agent acts in a common setting shared with other agents who may act in sophisticated ways. The difficulty in solving I-DIDs is mainly due to an exponentially growing space of candidate models ascribed to other agents over time. in order to minimize the model space, the previous I-DID techniques prune behaviorally equivalent models. In this paper, we challenge the minimal set of models and propose a value equivalence approach to further compress the model space. The new method reduces the space by additionally pruning behaviorally distinct models that result in the same expected value of the subject agent's optimal policy. To achieve this, we propose to learn the value from available data particularly in practical applications of real-time strategy games. We demonstrate the performance of the new technique in two problem domains.

Item Type: Book Section
Subjects: G400 Computer Science
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 26 Oct 2020 12:38
Last Modified: 31 Jul 2021 13:15
URI: http://nrl.northumbria.ac.uk/id/eprint/44589

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics