Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.
Table of Contents
Preface
Planning and Autonomous Behavior
Classical Planning: Full Information and Deterministic Actions
Classical Planning: Variations and Extensions
Beyond Classical Planning: Transformations
Planning with Sensing: Logical Models
MDP Planning: Stochastic Actions and Full Feedback
POMDP Planning: Stochastic Actions and Partial Feedback
Discussion
Bibliography
Author’s Biography
About the Author(s)
Hector Geffner, ICREA and Universitat Pompeu Fabra, Barcelona, Spain
Hector is interested in artificial intelligence and cognitive science, having worked on both planning and plan recognition methods for generating and recognizing autonomous behavior using model-based methods. He is an ICREA Research Professor at the Universitat Pompeu Fabra in Barcelona where he heads the AI group and directs the Master in Intelligent Interactive Systems. He was born and grew up in Buenos Aires, and then obtained an EE degree from the Universidad Simón BolÃvar in Caracas, and a Ph.D. in Computer Science from UCLA. He received the 1990 ACM Dissertation Award for a thesis done under the supervision of Judea Pearl, and the 2009 and 2010 ICAPS Influential Paper Awards. Hector is a fellow of AAAI and ECCAI, and Associate Editor of Artificial Intelligence and the Journal of Artificial Intelligence Research. He is the author of the book
Default Reasoning, MIT Press, 1992, and co-editor with Rina Dechter and Joseph Halpern of the book
Heuristics, Probability and Causality: A Tribute to Judea Pearl, College Publications, 2010.
Blai Bonet, Universidad Sim
Blai is a Professor in the Computer Science Department at Universidad Simon Bolivar in Caracas, Venezuela. His main research interests are automated planning, knowledge representation and search. He obtained B.Sc. and M.Sc. degrees in Computer Science from Universidad Simon Bolivar and a Ph.D. in Computer Science from UCLA. He received the 2009 ICAPS Influential Paper Award. Blai is Associate Editor of Artificial Intelligence and the Journal of Artificial Intelligence Research, and member of the Executive Council of ICAPS (Int. Conf. on Automated Planning and Scheduling).
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