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Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions

Release Date: October, 2011. Copyright © 2012. 444 pages.
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DOI: 10.4018/978-1-60960-165-2, ISBN13: 9781609601652, ISBN10: 1609601653, EISBN13: 9781609601676
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MLA

Sucar, L. Enrique, Eduardo F. Morales and Jesse Hoey. "Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions." IGI Global, 2012. 1-444. Web. 19 Jun. 2013. doi:10.4018/978-1-60960-165-2

APA

Sucar, L. E., Morales, E. F., & Hoey, J. (2012). Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions (pp. 1-444). doi:10.4018/978-1-60960-165-2

Chicago

Sucar, L. Enrique, Eduardo F. Morales and Jesse Hoey. "Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions." 1-444 (2012), accessed June 19, 2013. doi:10.4018/978-1-60960-165-2

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Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions
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Description

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.

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Table of Contents and List of Contributors

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1.
Introduction (pages 1-8)
L. Enrique Sucar (National Institute for Astrophysics, Optics and Electronics, Mexico), Eduardo Morales (National Institute for Astrophysics, Optics and Electronics, Mexico), Jesse Hoey (University of Waterloo, Canada)
This chapter gives a general introduction to decision-theoretic models in artificial intelligence and an overview of the rest of the book. It starts by motivating th... Sample PDF | More details...
$37.50
2.
Luis Enrique Sucar (National Institute for Astrophysics, Optics and Electronics, Mexico)
In this chapter we will cover the fundamentals of probabilistic graphical models, in particular Bayesian networks and influence diagrams, which are the basis for som... Sample PDF | More details...
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3.
Pascal Poupart (University of Waterloo, Canada)
The goal of this chapter is to provide an introduction to Markov decision processes as a framework for sequential decision making under uncertainty. The aim of this... Sample PDF | More details...
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4.
Eduardo F. Morales (National Institute of Astrophysics, Optics and Electronics, México), Julio H. Zaragoza (National Institute of Astrophysics, Optics and Electronics, México)
This chapter provides a concise introduction to Reinforcement Learning (RL) from a machine learning perspective. It provides the required background to understand th... Sample PDF | More details...
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5.
Matthew Hoffman (University of British Columbia, Canada), Nando de Freitas (University of British Columbia, Canada)
Semi-Markov decision processes are used to formulate many control problems and also play a key role in hierarchical reinforcement learning. In this chapter we show h... Sample PDF | More details...
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6.
Boris Defourny (University of Liège, Belgium), Damien Ernst (University of Liège, Belgium), Louis Wehenkel (University of Liège, Belgium)
In this chapter, we present the multistage stochastic programming framework for sequential decision making under uncertainty. We discuss its differences with Markov... Sample PDF | More details...
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7.
Omar Zia Khan (University of Waterloo, Canada), Pascal Poupart (University of Waterloo, Canada), James P. Black (University of Waterloo, Canada)
Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies f... Sample PDF | More details...
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8.
Dynamic LIMIDS (pages 164-189)
Francisco J. Díez (UNED, Spain), Marcel A. J. van Gerven (Radboud University Nijmegen, The Netherlands)
One of the objectives of artificial intelligence is to build decision-support models for systems that evolve over time and include several types of uncertainty. Dyna... Sample PDF | More details...
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9.
Eduardo F. Morales (National Institute of Astrophysics, Optics and Electronics, México), Julio H. Zaragoza (National Institute of Astrophysics, Optics and Electronics, México)
This chapter introduces an approach for reinforcement learning based on a relational representation that: (i) can be applied over large search spaces, (ii) can incor... Sample PDF | More details...
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10.
Kasia Muldner (Arizona State University, USA), Cristina Conati (University of British Columbia, Canada)
We describe a decision-theoretic tutor that helps students learn from Analogical Problem Solving (APS), i.e., from problem-solving activities that involve worked-out... Sample PDF | More details...
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11.
Julieta Noguez (Tecnológico de Monterrey, Mexico), Karla Muñoz (University of Ulster, Northern Ireland), Luis Neri (Tecnológico de Monterrey, Mexico), Víctor Robledo-Rella (Tecnológico de Monterrey, Mexico), Gerardo Aguilar (Tecnológico de Monterrey, Mexico)
Active learning simulators (ALSs) allow students to practice and carry out experiments in a safe environment – anytime, anywhere. Well-designed simulations may enhan... Sample PDF | More details...
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12.
Alberto Reyes (Instituto de Investigaciones Eléctricas, México), Francisco Elizalde (Instituto de Investigaciones Eléctricas, México)
In this chapter we present AsistO, a simulation-based intelligent assistant for power plant operators that provides on-line guidance in the form of ordered recommend... Sample PDF | More details...
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13.
Jesse Hoey (University of Waterloo, Canada), Pascal Poupart (University of Waterloo, Canada), Craig Boutilier (University of Toronto, Canada), Alex Mihailidis (University of Toronto, Canada)
This chapter presents a general decision theoretic model of interactions between users and cognitive assistive technologies for various tasks of importance to the el... Sample PDF | More details...
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14.
Jason D. Williams (AT&T Labs, USA)
Spoken dialog systems present a classic example of planning under uncertainty. Speech recognition errors are ubiquitous and impossible to detect reliably, so the sta... Sample PDF | More details...
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15.
Elva Corona (National Institute of Astrophysics, Optics and Electronics, Mexico), L. Enrique Sucar (National Institute of Astrophysics, Optics and Electronics, Mexico)
Markov Decision Processes (MDPs) provide a principled framework for planing under uncertainty. However, in general they assume a single action per decision epoch. In... Sample PDF | More details...
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16.
Aurélie Beynier (University Pierre and Marie Curie, France), Abdel-Illah Mouaddib (University of Caen, France)
In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We first describe some applicative domains and review the mai... Sample PDF | More details...
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Reviews and Testimonials

Several excellent textbooks cover Bayesian networks. Focus in these books is belief updating in Bayesian networks and learning of models. However, I have for many years been missing a graduate textbook, which systematically introduces the concepts and techniques of graphical models for sequential decision making. This book serves this purpose. Not only does it introduce the basic theory and concepts, but it also contains sections indicating new research directions as well as examples of real world decision models. [...] For the domain expert wanting to exploit graphical decision models for constructing a specific decision support system, this book is a useful hand book of the theory as well as of ideas, which may help establishing appropriate models. To the young researcher: this book will give you a firm ground for working with graphical decision models. Read the book, and you will realize that the story is not over. There are lots of challenges waiting for you, and the book provides you an excellent starting point for an exciting journey into the science of graphical decision models.

– Professor Finn V. Jensen, Aalborg University, Denmark
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Topics Covered

  • Active learning simulators
  • Bayesian networks and influence diagrams
  • Decision theoretic models for health in the home
  • Dynamic decision networks applications
  • Fully and partially observable Markov decision processes
  • Intelligent assistants for power plant operations and training
  • Multistage stochastic programming
  • Reinforcement Learning
  • Strategies for solving semi-Markov decision processes
  • Task coordination for service robots
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Preface

Under the rational agent approach, the goal of artificial intelligence is to design rational agents that must take the best actions given the information available, their prior knowledge, and their goals. In many cases, the information and knowledge is incomplete or unreliable, and the results of their decisions are not certain, that is, they have to make decisions under uncertainty. An intelligent agent should try to make the best decisions based on limited information and limited resources. Decision Theory provides a normative framework for decision making under uncertainty. It is based on the concept of rationality, that is that an agent should try to maximize its utility or minimize its costs. Decision theory was initially developed in economics and operations research, but in recent years has attracted the attention of artificial intelligence researchers interested in understanding and building intelligent agents.

Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, 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. It includes a general and comprehensive overview of decision theoretic models in artificial intelligence, with a review of the basic solution techniques, a sample of more advanced approaches, and examples of some recent real world applications.

The book is divided into three parts: Fundamentals, Concepts, and Solutions. Section 1 provides a general introduction to the main decision-theoretic techniques used in artificial intelligence: Influence Diagrams, Markov Decision Processes and Reinforcement Learning. It also reviews the bases of probability and decision theory, and provides a general overview of probabilistic graphical models. Section 2 presents recent theoretical developments that extend some of the techniques in Section 1 to deal with computational and representational issues that arise in artificial intelligence. Section 3 describes a wide sample of applications of decision-theoretic models in different areas of artificial intelligence, including: intelligent tutors and intelligent assistants, power plant control, medical assistive technologies, spoken dialog systems, service robots, and multi-robot systems.

This book is intended for students, researchers and practitioners of artificial intelligence that are interested in decision-theoretic approaches for building intelligent systems. Besides providing a comprehensive introduction to the theoretical bases and computational techniques, it includes recent developments in representation, inference and learning. It is also useful for engineers and other professionals in different fields interested in understanding and applying decision-theoretic techniques to solve practical problems in education, health, robotics, industry and communications, among other application areas. The book includes a wide sample of applications that illustrate the advantages as well as the challenges involved in applying these techniques in different domains.
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Author(s)/Editor(s) Biography

Enrique Sucar has a Ph.D in computing from Imperial College, London; a M.Sc. in electrical engineering from Stanford University; and a B.Sc. in electronics and communications engineering from ITESM, Monterrey, Mexico. He has been a Researcher at the Electrical Research Institute and Professor at ITESM Cuernavaca, and is currently a Senior Researcher at INAOE, Puebla, Mexico. He has more than 100 publications in journals and conference proceedings, and has directed 16 Ph.D. thesis. Dr. Sucar is Member of the National Research System, the Mexican Science Academy, and Senior Member of the IEEE. He has served as president of the Mexican AI Society, has been member of the Advisory Board of IJCAI, and is Associate Editor of the journals Computación y Sistemas and Revista Iberoamericana de Inteligencia Artificial. His main research interest are in graphical models and probabilistic reasoning, and their applications in computer vision, robotics and biomedicine.
Eduardo Morales, Ph.D., is a research scientist since 2006 of the National Institute of Astrophysics, Optics and Electronics (INAOE) in Mexico where he conducts research in Machine Learning and Robotics. He has a B.Sc. degree (1974) in Physics Engineering from Universidad Autonoma Metropolitana (Mexico), an M.Sc. degree (1985) in Information Technology: Knowledge-Based Systems from the University of Edinburgh (U.K.), and a PhD degree (1992) in Computer Science from the Turing Institute - University of Strathclyde (U.K.). He has been responsible for 20 research projects sponsored by different funding agencies and private companies and has more than 100 articles in journals and conference proceedings.
Jesse Hoey is an assistant professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Hoey is also an adjunct scientist at the Toronto Rehabilitation Institute in Toronto, Canada. His research focuses on planning and acting in large scale real-world uncertain domains. He has worked extensively on systems to assist persons with cognitive and physical disabilities. He won the Best Paper award at the International Conference on Vision Systems (ICVS) in 2007 for his paper describing an assistive system for persons with dementia during hand washing. Hoey won a Microsoft/AAAI Distinguished Contribution Award at the 2009 IJCAI Workshop on Intelligent Systems for Assisted Cognition, for his paper on technology to facilitate creative expression in persons with dementia. He also works on devices for ambient assistance in the kitchen, on stroke rehabilitation devices, and on spoken dialogue assistance systems. Hoey was co-Chair of the 2008 Medical Image Understanding and Analysis (MIUA) conference and he is Program Chair for the British Machine Vision Conference (BMVC) in 2011.