Emergent behaviors in multi-agent target acquisition

Piyush K. Sharma; Erin Zaroukian; Derrik E. Asher; Bryson Howell

doi:10.1117/12.2618646

6 June 2022 Emergent behaviors in multi-agent target acquisition

Piyush K. Sharma, Erin Zaroukian, Derrik E. Asher, Bryson Howell

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 1211314 (2022) https://doi.org/10.1117/12.2618646
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Only limited studies and superficial evaluations are available on agents' behaviors and roles within a Multi-Agent System (MAS). We simulate a MAS using Reinforcement Learning (RL) in a pursuit-evasion (a.k.a. predator- prey pursuit) game, which shares task goals with target acquisition, and we create different adversarial scenarios by replacing RL-trained pursuers' policies with two distinct (non-RL) analytical strategies. Using heatmaps of agents' positions (state-space variable) over time, we are able to categorize an RL-trained evader's behaviors. The novelty of our approach entails the creation of an influential feature set that reveals underlying data regularities, which allow us to classify an agent's behavior. This classification may aid in catching the (enemy) targets by enabling us to identify and predict their behaviors, and when extended to pursuers, this approach towards identifying teammates' behavior may allow agents to coordinate more effectively.

Conference Presentation

Citation Download Citation

Piyush K. Sharma, Erin Zaroukian, Derrik E. Asher, and Bryson Howell "Emergent behaviors in multi-agent target acquisition", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 1211314 (6 June 2022); https://doi.org/10.1117/12.2618646

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