One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.Elinas, P. (2005, Jun). Monte-Carlo localization for mobile robots with stereo vision. In Proc. Robotics: Science and Systems. Cambridge ... J., St-Aubin, R., Hu, A., aamp; Boutilier, C. (1999). SPUDD: Stochastic planning using decision diagrams.
|Title||:||Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions|
|Author||:||Sucar, L. Enrique|
|Publisher||:||IGI Global - 2011-10-31|