Probabilistic graphical models in artificial intelligence
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We start by giving an account of the early years when there was important controversy about the suitability of probability for intelligent systems. We then discuss the main milestones for the foundations of graphical models starting with Pearls pioneering work. Some of the main techniques for problem solving (abduction, classification, and decision-making) are briefly explained. Finally, we propose some important challenges for future research and highlight relevant applications (forensic reasoning, genomics and the use of graphical models as a general optimization tool).
probability classification forensic genomic optimization uncertain reasoning bayesian networks gaussian networks credal networks factor graphs kikuchi approximations decision-making metaheuristics