Ebook: Interleaving Planning and Execution for Autonomous Robots

Interleaving Planning and Execution for Autonomous Robots develops a formal representation for interleaving planning and execution in the context of incomplete information. This work bridges the gap between theory and practice in robotics by presenting control architectures that are provably sound, complete and optimal, and then describing real-world implementations of these robot architectures. Dervish, winner of the 1994 AAAI National Robot Contest, is one of the robots featured.
Interleaving Planning and Execution for Autonomous Robots is based on the author's PhD research, covering the same material taught in CS 224, the very popular Introduction to Robot Programming Laboratory taught at Stanford for four years by Professor Michael Genesereth and the author.

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Interleaving Planning and Execution for Autonomous Robots develops a formal representation for interleaving planning and execution in the context of incomplete information. This work bridges the gap between theory and practice in robotics by presenting control architectures that are provably sound, complete and optimal, and then describing real-world implementations of these robot architectures. Dervish, winner of the 1994 AAAI National Robot Contest, is one of the robots featured.
Interleaving Planning and Execution for Autonomous Robots is based on the author's PhD research, covering the same material taught in CS 224, the very popular Introduction to Robot Programming Laboratory taught at Stanford for four years by Professor Michael Genesereth and the author.

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Interleaving Planning and Execution for Autonomous Robots develops a formal representation for interleaving planning and execution in the context of incomplete information. This work bridges the gap between theory and practice in robotics by presenting control architectures that are provably sound, complete and optimal, and then describing real-world implementations of these robot architectures. Dervish, winner of the 1994 AAAI National Robot Contest, is one of the robots featured.
Interleaving Planning and Execution for Autonomous Robots is based on the author's PhD research, covering the same material taught in CS 224, the very popular Introduction to Robot Programming Laboratory taught at Stanford for four years by Professor Michael Genesereth and the author.
Content:
Front Matter....Pages i-xvii
Introduction....Pages 1-8
Perception and Action....Pages 9-19
Formalizing Incomplete Information....Pages 21-33
Goal-Directed Control Systems....Pages 35-51
Interleaving Planning and Execution....Pages 53-64
Using Assumptions to Oversimplify....Pages 65-95
Strategic Subgoaling: Using Abstraction Systems....Pages 97-121
Generalizing Beyond State Sets....Pages 123-129
Conclusions....Pages 131-135
Back Matter....Pages 137-145

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Interleaving Planning and Execution for Autonomous Robots develops a formal representation for interleaving planning and execution in the context of incomplete information. This work bridges the gap between theory and practice in robotics by presenting control architectures that are provably sound, complete and optimal, and then describing real-world implementations of these robot architectures. Dervish, winner of the 1994 AAAI National Robot Contest, is one of the robots featured.
Interleaving Planning and Execution for Autonomous Robots is based on the author's PhD research, covering the same material taught in CS 224, the very popular Introduction to Robot Programming Laboratory taught at Stanford for four years by Professor Michael Genesereth and the author.
Content:
Front Matter....Pages i-xvii
Introduction....Pages 1-8
Perception and Action....Pages 9-19
Formalizing Incomplete Information....Pages 21-33
Goal-Directed Control Systems....Pages 35-51
Interleaving Planning and Execution....Pages 53-64
Using Assumptions to Oversimplify....Pages 65-95
Strategic Subgoaling: Using Abstraction Systems....Pages 97-121
Generalizing Beyond State Sets....Pages 123-129
Conclusions....Pages 131-135
Back Matter....Pages 137-145
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