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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.
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
2 Basic Assumptions and Limitations
2.1 Important Features
3 SIPE and Its Representations
3.1 Representation of Domain Objects and Relationship
3.2 Operator Description Language
3.3 Plan Rationale
4 Hierarchical Planning as Differing Abstraction Levels
4.1 The Many Guises of Hierarchical Planning
4.2 A Problem with Current Planners
5.1 SIPE's Constraint Language
5.2 Use of Constraints
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
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
8 Plan Critics
8.1 Solving the Constraint Network
8.2 Parallel Interactions
8.3 Goal Phantomization
8.4 Solving Harmful Interactions
8.5 Adding Ordering Constraints
9 Resources: Reusable, Consumable, Temporal
9.1 Reusable Resources
9.2 Representation of Numerical Quantities
9.3 Consumable Resources
9.4 Temporal Reasoning
9.5 Manipulating Numerical Quantities
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
11.3 Interpreting the Input
11.4 The Problem Recognizer
11.5 Replanning Actions
11.6 The General Replanner
11.8 Searching the Space of Modified Plans
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
14.6 Heuristic Adequacy
- No. of pages:
- © Morgan Kaufmann 2014
- 1st September 1988
- Morgan Kaufmann
- Paperback ISBN:
- Hardcover ISBN:
- eBook ISBN:
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