Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2020

Abstract

In the ad hoc teamwork setting, a team of agents needs to perform a task without prior coordination. The most advanced approach learns policies based on previous experiences and reuses one of the policies to interact with new teammates. However, the selected policy in many cases is sub-optimal. Switching between policies to adapt to new teammates' behaviour takes time, which threatens the successful performance of a task. In this paper, we propose AATEAM – a method that uses the attention-based neural networks to cope with new teammates' behaviour in real-time. We train one attention network per teammate type. The attention networks learn both to extract the temporal correlations from the sequence of states (i.e. contexts) and the mapping from contexts to actions. Each attention network also learns to predict a future state given the current context and its output action. The prediction accuracies help to determine which actions the ad hoc agent should take. We perform extensive experiments to show the effectiveness of our method.

Keywords

Engineering, Electrical and electronic engineering

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the AAAI Conference on Artificial Intelligence, 34

First Page

7095

Last Page

7102

ISBN

9781577358350

Identifier

10.1609/aaai.v34i05.6196

Publisher

AAAI press

City or Country

Washington

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1609/aaai.v34i05.6196

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