Automated Planning: Theory and Practice

Front Cover
Elsevier, 2004 - Business & Economics - 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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Contents

Chapter 1 Introduction and Overview
1
Classical Planning
17
Neoclassical Planning
111
Heuristics and Control Strategies
193
Planning with Time and Resources
281
Planning under Uncertainty
375
Case Studies and Applications
449
Conclusion
525
Appendices
541
Bibliography
573
Index
609
Copyright

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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.
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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.
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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.
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About the author (2004)

Malik Ghallab is Director of Research at the Laboratoire d'Analyse et d'Architecture des SystŤmes, Centre National de la Recherche Scientifique, LAAS-CNRS, Toulouse. He was the director of the French national AI program, coordinated the five national research programs in information science, and served as the chair of ASTI, the French technical society in information sciences and technologies. Currently, he is also the director of the French national interdisciplinary program on robotics and artificial entities (Robea).

Dana Nau is a professor at the University of Maryland, and an AAAI Fellow. His research interests include AI planning and searching, and computer-integrated design and manufacturing. He holds appointments in the Department of Computer Science, the Institute for Systems Research, the Institute for Advanced Computer Studies, and the Department of Mechanical Engineering. He has more than 250 technical publications, and has co-authored computer programs that won the 1997 world championship of computer bridge and one of the top four awards in the 2002 International Planning Competition. Other awards he has received include an NSF graduate fellowship, an NSF Presidential Young Investigator award, an Outstanding Faculty award, and several "best paper" awards.

Paolo Traverso is the Head of Sistemi di Ragionamento Automatico at the Instituto Trentino di Cultura - Instituto per la Ricerca Scientifica e Tecnologica, (ITC-IRST). He was the project leader of industrial and experimental projects such as the development of Rail Traffic Management Systems, the design of tools for Automatic Train Protection, the synthesis of industrial controllers, and the development of systems for planning and control in space environment.

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