## More Aware AI Agents Through Introspection A recent study focuses on the development of artificial intelligence agents capable of modeling their own internal mental states, a crucial step in advancing "Theory of Mind" in AI. The research explores self-awareness by equipping reinforcement learning agents with the ability to infer their own internal states in simulated environments. ## Biological Pain as a Learning Signal Researchers have introduced an introspective exploration component, inspired by biological pain as a learning signal. This component uses a hidden Markov model to infer a "pain-belief" from online observations. This signal is then integrated into a subjective reward function to study how self-awareness affects the agent's learning abilities. ## Pain Perception Models The study compares the performance between normal and chronic pain perception models. The results show that introspective agents significantly outperform standard baseline agents and are able to replicate complex human-like behaviors. This innovative approach could lead to significant advances in the development of more sophisticated and adaptable artificial intelligence systems.