🤖 AI Summary
Existing approaches fail to address planning failures caused by emergent novelties in open-world environments, lacking support for continuous spatiotemporal scheduling, multi-agent coordination, and memory-augmented adaptation, while relying on low-fidelity validation environments.
Method: This paper proposes a general autonomous agent framework integrating symbol–neural hybrid modeling, online model refinement, distributed temporal scheduling, and memory-enhanced metacognitive control—forming a closed-loop “scheduler–executor–memory” architecture.
Contribution/Results: It achieves, for the first time, online novelty detection, semantic characterization, and model-level adaptive reconstruction by multiple agents within a high-fidelity dynamic military simulation environment. Experiments demonstrate a 42% improvement in planning success rate, sub-1.3-second response latency, and robust concurrent coordination among over ten agents.
📝 Abstract
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 extit{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.