Extending the Classical AI Planning Paradigm To order this title, and for more information, click here
By David Wilkins
Description
Planning, or reasoning about actions, is a fundamental element of intelligent behavior--and one that artificial intelligence has found
very difficult to implement. The most well-understood approach to building planning systems has been under refinement since the late
1960s and has now reached a level of maturity where there are good prospects for building working planners.
Practical Planning
is an in-depth examination of this classical planning paradigm through an intensive case study of SIPE, a significantly implemented planning
system. The author, the developer of SIPE, defines the planning problem in general, explains why reasoning about actions is so complex,
and describes all parts of the SIPE system and the algorithms needed to achieve efficiency. Details are discussed in the context of
problems and important issues in building a practical planner; discussions of how other systems address these issues are also included.
Assuming only a basic background in AI, Practical Planning will be of great interest to professionals interested in incorporating
planning capabilities into AI systems.
Contents
Practical Planning: Extending the Classical AI Planning Paradigm David E. Wilkins
List of Figures List of Tables
1 Reasoning about Actions and Planning
1.1 Philosophical and Practical Importance 1.2 The Classical AI Planning
Problem 1.3 Reactive Planning 1.4 The Essence of Planning 1.5 Capabilities of a Planning System 1.6 How Hard is Planning?
1.7 Classical AI Planning Systems 1.8 SIPE
2 Basic Assumptions and Limitations
2.1 Important Features
2.2 Limitations
3 SIPE and Its Representations
3.1 Representation of Domain Objects and Relationship 3.2
Operator Description Language 3.3 Plan Rationale 3.4 Plans
4 Hierarchical Planning as Differing Abstraction Levels
4.1 The Many Guises of Hierarchical Planning 4.2 A Problem with Current Planners 4.3 Solutions
5 Constraints
5.1 SIPE's Constraint Language 5.2 Use of Constraints 5.3 Unification
6. The Truth Criterion
6.1 The
Formula Truth Criterion 6.2 The PTC for Ground, Linear Plans 6.3 Introducing Variables 6.4 Introducing Existential Quantifiers
6.5 Introducing Universal Quantifiers 6.6 Introducing Nonlinearity 6.7 Summary
7 Deductive Causal Theories
7.1 A Motivating Example 7.2 Domain Rules 7.3 problems with Domain Rules 7.4 Heuritic Adequacy and Expressive Power
10 Search
10.1 Automatic Search 10.2 Intermingling Planning
and Execution 10.3 Interactive Control 10.4 Domain-Dependent Search Control 10.5 Other Search Strategies
11
Replanning During Execution
11.1 Overview of SIPE's Execution-Monitoring System 11.2 Unknowns 11.3 Interpreting
the Input 11.4 The Problem Recognizer 11.5 Replanning Actions 11.6 The General Replanner 11.7 Examples 11.8
Searching the Space of Modified Plans 11.9 Summary
12 Planning and Reactivity
12.1 Level of the Interface
12.2 Who is in Control?
13 Achieving Heuristic Adequacy
13.1 Summary of Heuristics 13.2 Subsumption
of Pred Constraints 13.3 Encoding Domains in SIPE
14 Comparison with Other Systems
14.1 Nonclassical Planning
Systems 14.2 Previous Classical Planners 14.3 Constraints 14.4 Critics 14.5 Replanning 14.6 Heuristic Adequacy
Bibliography Index
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