4 May 2007 Classifying and evolving multi-agent behaviors from animal packs in search and tracking problems
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Abstract
This work investigates the efforts behind defining a classification system for multi-agent search and tracking problems, specifically those based on relatively small numbers of agents. The pack behavior search and tracking classification (PBSTC) we define as mappings to animal pack behaviors that regularly perform activities similar to search and tracking problems, categorizing small multi-agent problems based on these activities. From this, we use evolutionary computation to evolve goal priorities for a team of cooperating agents. Our goal priorities are trained to generate candidate parameter solutions for a search and tracking problem in an emitter/sensor scenario. We identify and isolate several classifiers from the evolved solutions and how they reflect on the agent control systems's ability in the simulation to solve a task subset of the search and tracking problem. We also isolate the types of goal vector parameters that contribute to these classified behaviors, and categorize the limitations from those parameters in these scenarios.
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George A. Vilches, Annie S. Wu, John Sciortino, Daniel Pack, Jeffrey P. Ridder, "Classifying and evolving multi-agent behaviors from animal packs in search and tracking problems", Proc. SPIE 6563, Evolutionary and Bio-inspired Computation: Theory and Applications, 656303 (4 May 2007); doi: 10.1117/12.719281; https://doi.org/10.1117/12.719281
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