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

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

L. Enrique Sucar, Eduardo F. Morales, Jesse Hoey

Copyright: © 2012 |Pages: 444

ISBN13: 9781609601652|ISBN10: 1609601653|EISBN13: 9781609601676

DOI: 10.4018/978-1-60960-165-2

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MLA

Sucar, L. Enrique, et al., editors. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions. IGI Global, 2012. https://doi.org/10.4018/978-1-60960-165-2

APA

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

Chicago

Sucar, L. Enrique, Eduardo F. Morales, and Jesse Hoey, eds. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-165-2

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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.

Table of Contents

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Front Materials

Title Page

Copyright Page

List of Reviewers

Foreword

Preface

Acknowledgment

Chapters

Fundamentals

Chapter 1

Introduction (pages 1-8)

L. Enrique Sucar, Eduardo Morales, Jesse Hoey

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 the use of decision-theoretic models in artificial intelligence and...

Chapter 2

Introduction to Bayesian Networks and Influence Diagrams (pages 9-32)

Luis Enrique Sucar

In this chapter we will cover the fundamentals of probabilistic graphical models, in particular Bayesian networks and influence diagrams, which are the basis for some of the techniques and applications that are described in the rest...

Chapter 3

An Introduction to Fully and Partially Observable Markov Decision Processes (pages 33-62)

Pascal Poupart

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 introduction is to provide practitioners with a basic understanding...

Chapter 4

An Introduction to Reinforcement Learning (pages 63-80)

Eduardo F. Morales, Julio H. Zaragoza

This chapter provides a concise introduction to Reinforcement Learning (RL) from a machine learning perspective. It provides the required background to understand the chapters related to RL in this book. It makes no assumption on...

Concepts

Chapter 5

Inference Strategies for Solving Semi-Markov Decision Processes (pages 82-96)

Matthew Hoffman, Nando de Freitas

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 how to translate the decision making problem into a form that can...

Chapter 6

Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty (pages 97-143)

Boris Defourny, Damien Ernst, Louis Wehenkel

In this chapter, we present the multistage stochastic programming framework for sequential decision making under uncertainty. We discuss its differences with Markov Decision Processes, from the point of view of decision models and...

Chapter 7

Automatically Generated Explanations for Markov Decision Processes (pages 144-163)

Omar Zia Khan, Pascal Poupart, James P. Black

Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDPs by populating a set of domain-independent...

Chapter 8

Dynamic LIMIDS (pages 164-189)

Francisco J. Díez, Marcel A. J. van Gerven

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. Dynamic limited-memory influence diagrams (DLIMIDs) are a new type of...

Chapter 9

Relational Representations and Traces for Efficient Reinforcement Learning (pages 190-217)

Eduardo F. Morales, Julio H. Zaragoza

This chapter introduces an approach for reinforcement learning based on a relational representation that: (i) can be applied over large search spaces, (ii) can incorporate domain knowledge, and (iii) can use previously learned...

Solutions

Chapter 10

A Decision-Theoretic Tutor for Analogical Problem Solving (pages 219-247)

Kasia Muldner, Cristina Conati

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 examples. This tutor incorporates an innovative example-selection...

Chapter 11

Dynamic Decision Networks Applications in Active Learning Simulators (pages 248-270)

Julieta Noguez, Karla Muñoz, Luis Neri, Víctor Robledo-Rella, Gerardo Aguilar

Active learning simulators (ALSs) allow students to practice and carry out experiments in a safe environment – anytime, anywhere. Well-designed simulations may enhance learning, and provide the bridge from concept to practical...

Chapter 12

An Intelligent Assistant for Power Plant Operation and Training Based on Decision-Theoretic Planning (pages 271-293)

Alberto Reyes, Francisco Elizalde

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 recommendations. These recommendations are generated using the formalism of...

Chapter 13

POMDP Models for Assistive Technology (pages 294-314)

Jesse Hoey, Pascal Poupart, Craig Boutilier, Alex Mihailidis

This chapter presents a general decision theoretic model of interactions between users and cognitive assistive technologies for various tasks of importance to the elderly population. The model is a partially observable Markov...

Chapter 14

A Case Study of Applying Decision Theory in the Real World: POMDPs and Spoken Dialog Systems (pages 315-342)

Jason D. Williams

Spoken dialog systems present a classic example of planning under uncertainty. Speech recognition errors are ubiquitous and impossible to detect reliably, so the state of the conversation can never be known with certainty. Despite...

Chapter 15

Task Coordination for Service Robots Based on Multiple Markov Decision Processes (pages 343-360)

Elva Corona, L. Enrique Sucar

Markov Decision Processes (MDPs) provide a principled framework for planing under uncertainty. However, in general they assume a single action per decision epoch. In service robot applications, multiple tasks are required...

Chapter 16

Applications of DEC-MDPs in Multi-Robot Systems (pages 361-384)

Aurélie Beynier, Abdel-Illah Mouaddib

In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We first describe some applicative domains and review the main characteristics of the decision problems the robots must deal...

Back Materials

Compilation of References

About the Contributors

Index

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