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Probabilistic graphical models in artificial intelligence
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DOI：10.1016/j.asoc.2008.01.003.png)
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Abstract
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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 Pearl's 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). (C) 2008 Elsevier B. V. All rights reserved.
Keywords:
Probability
Uncertain reasoning
Bayesian networks
Gaussian networks
Credal networks
Factor graphs
Kikuchi approximations
Decision-making
Classification
Optimization
Metaheuristics
Genomic
Forensic
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