Decision Making under Uncertainty: Operations Research Meets AI (Again)

Models for sequential decision making under uncertainty (e.g., Markov decision processes,or MDPs) have beenstudied in operations research for decades. The recent incorporation of ideas from many areas of AI, including planning, probabilistic modeling, machine learning, and knowledge representation) have made these models much more widely applicable. I briefly survey recent advances within AI in the use of fullyand partially-observable MDPs as a modeling tool, and the development of computationally-manageable solution methods. I will place special emphasis on factored problem representations such as Bayesian networks and algorithms that exploit the structure inherent in these representations. 1 AI Meets OR When one is reminded of the crossroads where artificial intelligence (AI) meets operations research (OR), the vital and active area of combinatorial optimization immediately springs to mind. The interaction between researchers in the two disciplines has been lively and fruitful. Line...

• Type: Conference
• Year: 2000
• Where: AAAI
• Authors: Craig Boutilier
• Keywords: AAAI 2000, Decision Making, Intelligent Agents, Operations Research, Sequential Decision