Self-adaptive Mission Planning using High-Fidelity Open World Simulation

Wiktor Mateusz Piotrowski, James Chao, Sachin Grover, Roni Stern, Shiwali Mohan, Douglas S. Lange

Keywords: Open World learning, mission planning, self-adaptive agents

Abstract: AI and ML agents are developed with closed world assumptions, that can change during execution. This demo paper presents HYDRA, a framework for developing self-adaptive autonomous agents capable of handling unexpected domain shifts (also called \textit{novelty}) during execution, applied to a high fidelity simulator for military mission planning. The framework is divided into a base agent, responsible for basic predict-decide-act cycle, and novelty monitoring to detect, characterize and adapt to the novelty. AFSIM is a high-fidelity mission simulator that incorporates many real-world military models; and has been used for mission planning in several scenarios. This paper shows successful integration of HYDRA with AFSIM, and demonstrates HYDRA agents efficiently adapting to novelty in realistic simulated military scenarios. Demonstration of our system is available at https://tinyurl.com/wb3z2edv.

Submission Number: 6