Agent State

Agent state, in the context of artificial intelligence, refers to the internal representation an agent maintains to guide its actions within a partially observable environment. Current research focuses on developing effective methods for constructing and updating this agent state, employing techniques like recurrent neural networks, Petri nets for constraint enforcement, and various policy search algorithms within the framework of partially observable Markov decision processes (POMDPs). This research is crucial for improving the robustness, reliability, and explainability of AI agents in complex real-world applications, such as autonomous driving and multi-agent robotics, where complete environmental knowledge is unavailable.