Theory & Practice
Verlag: Elsevier Science & Technology
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.
Primary audience: Senior undergraduate and graduate students in AI, Robotics, and Operations Research (in CS, EE and CompEng departments, mainly)
Secondary audience: Researchers and practitioners in artificial intelligence, robotics, and operations research
Weitere Infos & Material
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
24 Conclusion and Other Topics
A Search Procedures and Computational Complexity*B First Order Logic*C Model Checking