Goal Inference

Goal inference, the process of determining an agent's objectives from observations of their actions and communications, is a crucial area of research in artificial intelligence and cognitive science. Current research focuses on developing computational models that combine top-down reasoning (e.g., Bayesian inverse planning) with bottom-up processing of sensory data, often employing neural networks and probabilistic methods like particle filtering to handle uncertainty and large goal spaces. These models are evaluated against human performance in various tasks, including collaborative problem-solving and trajectory prediction, demonstrating progress in understanding both human and artificial goal inference. This work has implications for improving human-robot interaction, building more socially intelligent AI systems, and furthering our understanding of social cognition.