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Information Science Reference Decision Theory Models for AI - Concepts and Solutions

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Description

Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions by Information Science Reference introduces essential decision theory techniques for autonomous agents navigating uncertain and incomplete information. This comprehensive resource covers Markov Decision Processes (MDPs), Partially Observable Markov Decision Processes (POMDPs), Influence Diagrams, and Reinforcement Learning, with practical applications throughout artificial intelligence systems. Designed for professionals and researchers, the book bridges theoretical foundations and real-world implementation, enabling readers to design rational agents that make optimal decisions based on available information and defined goals. Whether you're developing intelligent systems or advancing your expertise in decision-making frameworks, this Premier Reference Source provides the conceptual depth and practical guidance needed to apply sophisticated decision theory techniques across diverse AI applications and problem domains.

Key Features

Covers multiple decision theory techniques including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning methods.

Provides practical applications and solutions for autonomous agent design in artificial intelligence systems.

Introduces concepts for creating rational agents that decide optimally given incomplete and uncertain information.

Offers insights and guidance from Information Science Reference's established Premier Reference Source collection.

Addresses the fundamental challenge of enabling AI systems to make effective decisions with limited data availability.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
01 October 2011
Listed Since
17 May 2010

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