Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World

Autonomous agents operating within real-world environments often rely on automated planners to ascertain optimal actions towards desired goals or the optimization of a specified objective function. Integral to these agents are common architectural components such as schedulers, tasked with determining the timing for executing planned actions, and execution engines, responsible for carrying out these scheduled actions while monitoring their outcomes. We address the significant challenge that arises when unexpected phenomena, termed \textit{novelties}, emerge within the environment, altering its fundamental characteristics, composition, and dynamics. This challenge is inherent in all deployed real-world applications and may manifest suddenly and without prior notice or explanation. The introduction of novelties into the environment can lead to inaccuracies within the planner's internal model, rendering previously generated plans obsolete. Recent research introduced agent design aimed at detecting and adapting to such novelties. However, these designs lack consideration for action scheduling in continuous time-space, coordination of concurrent actions by multiple agents, or memory-based novelty accommodation. Additionally, the application has been primarily demonstrated in lower fidelity environments. In our study, we propose a general purpose AI agent framework designed to detect, characterize, and adapt to novelties in highly noisy, complex, and stochastic environments that support concurrent actions and external scheduling. We showcase the efficacy of our agent through experimentation within a high-fidelity simulator for realistic military scenarios.

@article{chao2025_2306.12654,
  title={ Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World },
  author={ James Chao and Wiktor Piotrowski and Roni Stern and Héctor Ortiz-Peña and Mitch Manzanares and Shiwali Mohan and Douglas S. Lange },
  journal={arXiv preprint arXiv:2306.12654},
  year={ 2025 }
}