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A Concise Introduction to Models and Methods for Automated PlanningBeyond Classical Planning: Transformations

A Concise Introduction to Models and Methods for Automated Planning: Beyond Classical Planning:... [We have considered models of planning where a goal is to be achieved by performing actions that are deterministic given an initial situation that is fully known. Often, however, planning problems exhibit features that do not fit into this format, features such as goals that are desirable but which are not to be achieved at any cost (soft goals), goals that refer not only to end states but to the intermediate states as well (temporally extended goals), or initial situations that are not fully known (conformant planning). In this chapter, rather than reviewing more powerful algorithms for dealing with such features, we illustrate how features such as these can be handled by off-the-shelf classical planners through suitable transformations that can be performed automatically. Similar transformations will also be introduced for dealing with a different task, plan recognition, where a probability distribution over the possible goals of the agent is to be inferred from partial observations of the agent behavior.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Concise Introduction to Models and Methods for Automated PlanningBeyond Classical Planning: Transformations

Part of the Synthesis Lectures on Artificial Intelligence and Machine Learning Book Series
Geffner, Hector; Bonet, Blai
Springer Journals — Jan 1, 2013
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13 pages

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2013
ISBN
978-3-031-00436-0
Pages
51 –64
DOI
10.1007/978-3-031-01564-9_4
Publisher site
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Abstract

[We have considered models of planning where a goal is to be achieved by performing actions that are deterministic given an initial situation that is fully known. Often, however, planning problems exhibit features that do not fit into this format, features such as goals that are desirable but which are not to be achieved at any cost (soft goals), goals that refer not only to end states but to the intermediate states as well (temporally extended goals), or initial situations that are not fully known (conformant planning). In this chapter, rather than reviewing more powerful algorithms for dealing with such features, we illustrate how features such as these can be handled by off-the-shelf classical planners through suitable transformations that can be performed automatically. Similar transformations will also be introduced for dealing with a different task, plan recognition, where a probability distribution over the possible goals of the agent is to be inferred from partial observations of the agent behavior.]

Published: Jan 1, 2013

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