15 May 2017 Application of free energy minimization to the design of adaptive multi-agent teams
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Abstract
Many novel DoD missions, from disaster relief to cyber reconnaissance, require teams of humans and machines with diverse capabilities. Current solutions do not account for heterogeneity of agent capabilities, uncertainty of team knowledge, and dynamics of and dependencies between tasks and agent roles, resulting in brittle teams. Most importantly, the state-of-the-art team design solutions are either centralized, imposing role and relation assignment onto agents, or completely distributed, suitable for only homogeneous organizations such as swarms. Centralized design models can’t provide insights for team’s self-organization, i.e. adapting team structure over time in distributed collaborative manner by team members with diverse expertise and responsibilities. In this paper we present an information-theoretic formalization of team composition and structure adaptation using a minimization of variational free energy. The structure adaptation is obtained in an iterative distributed and collaborative manner without the need for centralized control. We show that our model is lightweight, predictive, and produces team structures that theoretically approximate an optimal policy for team adaptation. Our model also provides a unique coupling between the structure and action policy, and captures three essential processes of learning, perception, and control.
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Georgiy Levchuk, Krishna Pattipati, Adam Fouse, Daniel Serfaty, "Application of free energy minimization to the design of adaptive multi-agent teams", Proc. SPIE 10206, Disruptive Technologies in Sensors and Sensor Systems, 102060E (15 May 2017); doi: 10.1117/12.2263542; https://doi.org/10.1117/12.2263542
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