Automated Planning: Theory and Practice

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Elsevier, 2004 - Computers - 635 pages
3 Reviews
Automated planning technology now plays a significant role in a variety of demanding applications, ranging from controlling space vehicles and robots to playing the game of bridge. These real-world applications create new opportunities for synergy between theory and practice: observing what works well in practice leads to better theories of planning, and better theories lead to better performance of practical applications.

Automated Planning mirrors this dialogue by offering a comprehensive, up-to-date resource on both the theory and practice of automated planning. The book goes well beyond classical planning, to include temporal planning, resource scheduling, planning under uncertainty, and modern techniques for plan generation, such as task decomposition, propositional satisfiability, constraint satisfaction, and model checking.

The authors combine over 30 years experience in planning research and development to offer an invaluable text to researchers, professionals, and graduate students.

*Comprehensively explains paradigms for automated planning.
*Provides a thorough understanding of theory and planning practice, and how they relate to each other.
*Presents case studies of applications in space, robotics, CAD/CAM, process control, emergency operations, and games.

*Provides a thorough understanding of AI planning theory and practice, and how they relate to each other.
*Covers all the contemporary topics of planning, as well as important practical applications of planning, such as model checking and game playing.
*Presents case studies and applications in planning engineering, space, robotics, CAD/CAM, process control, emergency operations, and games.
*Provides lecture notes, examples of programming assignments, pointers to downloadable planning systems and related information online.

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Chapter 1 Introduction and Overview
Classical Planning
Neoclassical Planning
Heuristics and Control Strategies
Planning with Time and Resources
Planning under Uncertainty
Case Studies and Applications

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Page 578 - V. Boor, MH Overmars, and AF van der Stappen. The Gaussian sampling strategy for probabilistic roadmap planners. In Proc.
Page 582 - J. Davis and A. Bobick. The representation and recognition of action using temporal templates.
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Page 585 - H. Fargier, J. Lang, and T. Schiex. Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge. In AAAI'96: Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 175-180.
Page 573 - R. Alami, S. Fleury, M. Herrb. F. Ingrand, and F. Robert. Multi-robot cooperation in the MARTHA project.
Page 575 - H. Katsuno and AO Mendelzon. On the difference between updating a knowledge database and revising it. In Proceedings of the International Conference on Knowledge Representation and Reasoning (KR), pages 387-394, Boston, Mass., April 1991.
Page 583 - The use of optimistic and pessimistic resource profiles to inform search in an activity based planner. In Proceedings of the International Conference on AI Planning Systems (AIPS), pp.

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About the author (2004)

Paolo Traverso is Head of Division at Center for Scientific and Technological Research (ITC/IRST), Trento, Italy.

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