000			01397nam a22002177a 4500
999			_c492 _d492
005			20191111142651.0
008			191111b ||||| |||| 00| 0 eng d
020			_a9781609601652
082			_a006.3 _bSUC
100			_aSucar, L. Enrique _91503
245			_aDecision theory models for applications in artificial intelligence: concepts and solutions
260			_bIGI Global _aHershey, USA _c2012
300			_axiv, 428 p.
365			_aUSD _b180.00
520			_aDescription 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.
650			_aArtificial intelligence--Statistical methods _91504
650			_aBayesian statistical decision theory _91505
700			_aHoey, Jesse _91506
700			_aMorales, Eduardo F _91507
942			_2ddc _cBK