Modelling other agents through evolutionary behaviours

Zeng, Yifeng, Ran, Qiang, Ma, Biyang and Pan, Yinghui (2022) Modelling other agents through evolutionary behaviours. Memetic Computing, 14 (1). pp. 19-30. ISSN 1865-9284

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Modelling other agents is a challenging topic in artificial intelligence research particularly when a subject agent needs to optimise its own decisions by predicting their behaviours under uncertainty. Existing research often leads to a monotonic set of behaviours for other agents so that a subject agent can not cope with unexpected decisions from the other agents. It requires creative ideas about developing diversity of behaviours so as to improve the subject agent’s decision quality. In this paper, we resort to evolutionary computation approaches to generate a new set of behaviours for other agents and solve the complicated agents’ behaviour search and evaluation issues. The new approach starts with the initial behaviours that are ascribed to the other agents and expands the behaviours by using a number of genetic operators in the behaviour evolution. This is the first time that evolutionary techniques are used to modelling other agents in a general multiagent decision framework. We examine the new methods in two well-studied problem domains and provide experimental results in support.

Item Type: Article
Additional Information: Funding information: Dr. Yinghui Pan and Qiang Ran are supported in part by the National Natural Science Foundation of China (Grants No.61806089, 61772442 and 61836005). Both Dr. Biyang Ma and Professor Yifeng Zeng are partially supported by the EPSRC project (Grant No. EP/S011609/1). Dr. Yinghui Pan and Dr. Biyang Ma are the corresponding authors for this work.
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 13 Aug 2021 13:32
Last Modified: 29 Mar 2022 10:00

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