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Adaptable and stable decentralized task allocation for hierarchical domains

Part of: Adaptive Learning Agents 2019

Published online by Cambridge University Press:  04 June 2020

Vera A. Kazakova Open the ORCID record for Vera A. Kazakova [Opens in a new window]  and
Gita R. Sukthankar
Vera A. Kazakova
Affiliation:
Intelligent Agents Laboratory, University of Central Florida, Orlando, FL, USA e-mails: kazakova.cs@ucf.edu, gitars@eecs.ucf.edu
Gita R. Sukthankar
Affiliation:
Intelligent Agents Laboratory, University of Central Florida, Orlando, FL, USA e-mails: kazakova.cs@ucf.edu, gitars@eecs.ucf.edu
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Abstract

Many real-world domains can benefit from adaptable decentralized task allocation through emergent specialization, especially in large teams of non-communicating agents. We begin with an existing bio-inspired response threshold reinforcement approach for decentralized task allocation and extend it to handle hierarchical task domains. We test the extension on self-deployment of a large team of non-communicating agents to patrolling a hierarchically defined set of areas. Results show near-ideal performance across all areas, while minimizing wasteful task switching through the development of specializations and subsequent respecializations when area demands change. A genetic algorithm is then used to evolve even more adaptable and stable task allocation behavior, by incorporating weight and power coefficients into agents’ response threshold reinforcement action probability calculations.

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Type
Research Article
Information
The Knowledge Engineering Review , Volume 35 , 2020 , e26
Copyright
© Cambridge University Press, 2020

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