Providing predictions of adversary movements in a gridworld environment to a human-machine team improves teaming performance

Jeffry A. Coady; Paul Dysart; Aidan Schumann; Stephan A. Koehler; Michael J. Munje; William D. Casebeer; David M. Huberdeau

doi:10.1117/12.2663881

12 June 2023 Providing predictions of adversary movements in a gridworld environment to a human-machine team improves teaming performance

Jeffry A. Coady, Paul Dysart, Aidan Schumann, Stephan A. Koehler, Michael J. Munje, William D. Casebeer, David M. Huberdeau

Author Affiliations +

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125380N (2023) https://doi.org/10.1117/12.2663881
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Knowing the future states of an adversary in an adversary-avoidance game can impart a survival advantage. To assess how predictive modeling helps agents achieve goals and avoid adversaries, we tested the efficacy of three predictive algorithms within a gridworld-based game. For one predictive algorithm, model predictions of adversary moves furnished to an agent helped the agent avoid capture compared to a case without predictions. A human-machine team scenario also benefited from model predictions, while humans alone experienced a ceiling effect. We investigated the efficacy of two additional predictive algorithms and present a maritime vessel pursuit scenario.

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Jeffry A. Coady, Paul Dysart, Aidan Schumann, Stephan A. Koehler, Michael J. Munje, William D. Casebeer, and David M. Huberdeau "Providing predictions of adversary movements in a gridworld environment to a human-machine team improves teaming performance", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380N (12 June 2023); https://doi.org/10.1117/12.2663881

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