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Intelligent Planning

A Decomposition and Abstraction Based Approach

  • Textbook
  • © 1997

Overview

Authors:
  1. Qiang Yang

    1. School of Computing Science Ebco/Epic NSERC Industrial Chair, Simon Fraser University, Burnaby, Canada

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Part of the book series: Artificial Intelligence (AI)

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Table of contents (13 chapters)

  1. Front Matter

    Pages I-XXII
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  2. Introduction

    1. Introduction

      • Qiang Yang
      Pages 1-9

  3. Representation, Basic Algorithms, and Analytical Techniques

    1. Front Matter

      Pages 11-13
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    2. Representation and Basic Algorithms

      • Qiang Yang
      Pages 15-38

    3. Analytical Techniques

      • Qiang Yang
      Pages 39-48

    4. Useful Supporting Algorithms

      • Qiang Yang
      Pages 49-66

    5. Case Study: Collective Resource Reasoning

      • Qiang Yang
      Pages 67-79

  4. Problem Decomposition and Solution Combination

    1. Front Matter

      Pages 81-83
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    2. Planning by Decomposition

      • Qiang Yang
      Pages 85-100

    3. Global Conflict Resolution

      • Qiang Yang
      Pages 101-120

    4. Plan Merging

      • Qiang Yang
      Pages 121-139

    5. Multiple-Goal Plan Selection

      • Qiang Yang
      Pages 141-157

  5. Hierarchical Abstraction

    1. Front Matter

      Pages 159-161
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    2. Hierarchical Planning

      • Qiang Yang
      Pages 163-188

    3. Generating Abstraction Hierarchies

      • Qiang Yang
      Pages 189-206

    4. Properties of Task Reduction Hierarchies

      • Qiang Yang
      Pages 207-223

    5. Effect Abstraction

      • Qiang Yang
      Pages 225-237

  6. Back Matter

    Pages 239-252
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Keywords

About this book

"The central fact is that we are planning agents." (M. Bratman, Intentions, Plans, and Practical Reasoning, 1987, p. 2) Recent arguments to the contrary notwithstanding, it seems to be the case that people-the best exemplars of general intelligence that we have to date­ do a lot of planning. It is therefore not surprising that modeling the planning process has always been a central part of the Artificial Intelligence enterprise. Reasonable behavior in complex environments requires the ability to consider what actions one should take, in order to achieve (some of) what one wants­ and that, in a nutshell, is what AI planning systems attempt to do. Indeed, the basic description of a plan generation algorithm has remained constant for nearly three decades: given a desciption of an initial state I, a goal state G, and a set of action types, find a sequence S of instantiated actions such that when S is executed instate I, G is guaranteed as a result. Working out the details of this class of algorithms, and making the elabora­ tions necessary for them to be effective in real environments, have proven to be bigger tasks than one might have imagined.

Authors and Affiliations

  • School of Computing Science Ebco/Epic NSERC Industrial Chair, Simon Fraser University, Burnaby, Canada

    Qiang Yang

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