Academic Papers

Providing Predictions of Adversary Movements in a Gridworld Environment to a Human-Machine Team

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.

• Year: 2023
• Category: Artificial Intelligence
• Tag: human-machine teaming, Theory of Mind Modeling; cognitive modeling
• Author: Jeffry A. Coady, Paul Dysart, Aidan Schumann, Stephan A. Koehler, Michael J. Munje, William Casebeer, David M. Huberdeau
• Released: SPIE Defense + Commercial Sensing 2023 - Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V