Structured Probabilistic Models: Bayesian Networks and Beyond
For many years, probabilistic models were largely neglected within the AI community. Now they play a fundamental role in many areas in AI, including diagnosis, planning, and learning. One of the crucial reasons for this transition is the use of structured model-based representations such as Bayesian networks. Building on this idea, we can extend the success of probabilistic modeling to much more complex domains, ones involving many components that interact and evolve ove time. These domains are significantly beyond the scope of traditional Bayesian networks. I describe a broad class of structured probabilistic representations that extend Bayesian networks to deal with these new challenges. I argue that these representations can form the basis for agents that reason and act in complex uncertain environments.