Agent Model

Agent modeling focuses on creating computational representations of agents—be they humans, robots, or AI systems—to understand their behavior, predict their actions, and facilitate interaction. Current research emphasizes developing robust agent models capable of handling uncertainty, imperfect information, and dynamic environments, often employing techniques like reinforcement learning, belief-desire-intention architectures, and contrastive learning. These advancements are crucial for improving AI safety, enabling effective human-AI collaboration, and building more realistic simulations for diverse applications such as autonomous systems and social sciences.