Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction

Hou, Yaqing, Ong, Yew-Soon, Tang, Jing and Zeng, Yifeng (2021) Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51 (10). pp. 5962-5976. ISSN 2168-2216

_2019_IEEE_SMC_Evolutionary_Multi_Agent_Transfer_Learning_with_Model_based_Opponent_Behavior_Prediction.pdf - Accepted Version

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Official URL: https://doi.org/10.1109/TSMC.2019.2958846


This article embarks a study on multiagent transfer learning (TL) for addressing the specific challenges that arise in complex multiagent systems where agents have different or even competing objectives. Specifically, beyond the essential backbone of a state-of-the-art evolutionary TL framework (eTL), this article presents the novel TL framework with prediction (eTL-P) as an upgrade over existing eTL to endow agents with abilities to interact with their opponents effectively by building candidate models and accordingly predicting their behavioral strategies. To reduce the complexity of candidate models, eTL-P constructs a monotone submodular function, which facilitates to select Top-K models from all available candidate models based on their representativeness in terms of behavioral coverage as well as reward diversity. eTL-P also integrates social selection mechanisms for agents to identify their better-performing partners, thus improving their learning performance and reducing the complexity of behavior prediction by reusing useful knowledge with respect to their partners' mind universes. Experiments based on a partner-opponent minefield navigation task (PO-MNT) have shown that eTL-P exhibits the superiority in achieving higher learning capability and efficiency of multiple agents when compared to the state-of-the-art multiagent TL approaches.

Item Type: Article
Uncontrolled Keywords: Behavior prediction, evolutionary transfer learning (eTL), monotone submodular model selection, multiagent system (MAS)
Subjects: G400 Computer Science
G600 Software Engineering
Department: Faculties > Business and Law > Newcastle Business School
Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 07 Jul 2020 09:21
Last Modified: 22 Oct 2021 09:15
URI: http://nrl.northumbria.ac.uk/id/eprint/43674

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