Automated Planning. The Morgan Kaufmann Series in Artificial Intelligence

  • ID: 1757322
  • Book
  • 635 Pages
  • Elsevier Science and Technology
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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.

  • 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

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

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1 Introduction and Overview

I Classical Planning 2 Representations for Classical Planning 3 Complexity of Classical Planning 4 State-Space Planning 5 Plan-Space Planning II Neoclassical Planning 6 Planning-Graph Techniques 7 Propositional Satisfiability Techniques 8  Constraint Satisfaction Techniques

III Heuristics and Control Strategies 9 Heuristics in Planning 10 Control Rules in Planning 11 Hierarchical Task Network Planning 12 Control Strategies in Deductive Planning IV Planning with Time and Resources 13 Time for Planning 14 Temporal Planning 15 Planning and Resource Scheduling

V Planning under Uncertainty 16 Planning based on Markov Decision Processes 17 Planning based on Model Checking 18 Uncertainty with Neo-Classical Techniques

VI Case Studies and Applications 19 Space Applications 20 Planning in Robotics 21 Planning for Manufacturability Analysis 22 Emergency Evacuation Planning 23 Planning in the Game of Bridge

VII Conclusion 24 Conclusion and Other Topics

VIII Appendices A Search Procedures and Computational Complexity B First Order Logic C Model Checking

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Ghallab, Malik
Malik Ghallab is Director of Research at the Laboratoire d'Analyse et d'Architecture des Systèmes, 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).
Nau, Dana
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.
Traverso, Paolo
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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