Intelligent Planning: A Decomposition and Abstraction Based Approach
"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 datedo 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 wantsand 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 elaborations necessary for them to be effective in real environments, have proven to be bigger tasks than one might have imagined.
1112059181
Intelligent Planning: A Decomposition and Abstraction Based Approach
"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 datedo 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 wantsand 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 elaborations necessary for them to be effective in real environments, have proven to be bigger tasks than one might have imagined.
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Intelligent Planning: A Decomposition and Abstraction Based Approach

Intelligent Planning: A Decomposition and Abstraction Based Approach

Intelligent Planning: A Decomposition and Abstraction Based Approach

Intelligent Planning: A Decomposition and Abstraction Based Approach

Overview

"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 datedo 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 wantsand 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 elaborations necessary for them to be effective in real environments, have proven to be bigger tasks than one might have imagined.

Product Details

ISBN-13: 9783642644771
Publisher: Springer Berlin Heidelberg
Publication date: 09/28/2011
Series: Artificial Intelligence
Edition description: Softcover reprint of the original 1st ed. 1997
Pages: 252
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

1. Introduction.- 1.1 The Problem.- 1.2 Key Issues.- 1.3 Planning Versus Scheduling.- 1.4 Contributions and Organization.- 1.5 Background.- I. Representation, Basic Algorithms, and Analytical Techniques.- 2. Representation and Basic Algorithms.- 3. Analytical Techniques.- 4. Useful Supporting Algorithms.- 5. Case Study: Collective Resource Reasoning.- II. Problem Decomposition and Solution Combination.- 6. Planning by Decomposition.- 7. Global Conflict Resolution.- 8. Plan Merging.- 9. Multiple-Goal Plan Selection.- III. Hierarchical Abstraction.- 10. Hierarchical Planning.- 11. Generating Abstraction Hierarchies.- 12. Properties of Task Reduction Hierarchies.- 13. Effect Abstraction.- References.
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