Goal Directed Behavior

Goal-directed behavior research aims to understand how agents, both biological and artificial, select and execute actions to achieve desired outcomes. Current research focuses on developing computational models, such as those based on reinforcement learning, active inference, and large language models, to explain and replicate this behavior, often incorporating hierarchical planning and adaptive skill acquisition. These advancements are improving the efficiency and flexibility of artificial agents in complex environments and offer insights into the cognitive mechanisms underlying human decision-making and action selection, with implications for robotics, AI, and cognitive science.