Bayesian Agent

Bayesian agents are computational models that make decisions by updating their beliefs based on probabilistic reasoning, aiming to optimize actions in uncertain environments. Current research focuses on developing efficient algorithms for Bayesian inference in complex scenarios, including the use of neural networks to approximate intractable Bayesian computations and the exploration of model architectures like GFlowNets for sampling complex structures. This work is significant for advancing our understanding of decision-making under uncertainty, with implications for fields ranging from artificial intelligence and robotics to cognitive science and economics, particularly in designing robust and adaptable agents for real-world applications.