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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.

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

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