Smyrnakis, M. and Veres, S.M. (2016) Fictitious play for cooperative action selection in robot teams. Engineering Applications of Artificial Intelligence, 56. pp. 14-29. ISSN 0952-1976
Abstract
A game-theoretic distributed decision making approach is presented for the problem of control effort allocation in a robotic team based on a novel variant of fictitious play. The proposed learning process allows the robots to accomplish their objectives by coordinating their actions in order to efficiently complete their tasks. In particular, each robot of the team predicts the other robots' planned actions, while making decisions to maximise their own expected reward that depends on the reward for joint successful completion of the task. Action selection is interpreted as an n-player cooperative game. The approach presented can be seen as part of the Belief Desire Intention (BDI) framework, also can address the problem of cooperative, legal, safe, considerate and emphatic decisions by robots if their individual and group rewards are suitably defined. After theoretical analysis the performance of the proposed algorithm is tested on four simulation scenarios. The first one is a coordination game between two material handling robots, the second one is a warehouse patrolling task by a team of robots, the third one presents a coordination mechanism between two robots that carry a heavy object on a corridor and the fourth one is an example of coordination on a sensors network.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier. This is an author produced version of a paper subsequently published in Engineering Applications of Artificial Intelligence. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Robot team coordination; Fictitious play; Extended Kalman filters; Game theory; Distributed optimisation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Nov 2016 16:13 |
Last Modified: | 29 Aug 2017 16:58 |
Published Version: | http://doi.org/10.1016/j.engappai.2016.08.008 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.engappai.2016.08.008 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:107701 |