Adaptable and stable decentralized task allocation for hierarchical domains

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.

Special issue on adaptive and learning agents 2019

“…The first paper Adaptable and stable decentralized task allocation for hierarchical domains by Kazakova and Sukthankar (2020) extends a previous model of observed insect behavior (StimHab), to allow agents to self-allocate to hierarchical sets of tasks. The proposed method is highly scalable as individual agents do not communicate with each other and are not aware of the capabilities, preferences, or circumstances of the other agents in the system.…”