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A Concise Introduction to Models and Methods for Automated PlanningClassical Planning: Full Information and Deterministic Actions

A Concise Introduction to Models and Methods for Automated Planning: Classical Planning: Full... [In classical planning, the task is to drive a system from a given initial state into a goal state by applying actions whose effects are deterministic and known. Classical planning can be formulated as a pathfinding problem over a directed graph whose nodes represent the states of the system or enviroment, and whose edges capture the state transitions that the actions make possible. The computational challenge in classical planning results from the number of states, and hence the size of the graph, which are exponential in the number of problem variables. State-of-the-art methods in classical planning search for paths in such graphs by directing the search toward the goal using heuristic functions that are automatically derived from the problem. The heuristic functions map each state into an estimate of the distance or cost from the state to the goal, and provide the search for the goal with a sense of direction. In this chapter, we look at the model and languages for classical planning, and at the heuristic search techniques that have been developed for solving it. Variations and extensions of these methods, as well as alternative methods, will be considered in the next chapter.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Concise Introduction to Models and Methods for Automated PlanningClassical Planning: Full Information and Deterministic Actions

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2013
ISBN
978-3-031-00436-0
Pages
15 –36
DOI
10.1007/978-3-031-01564-9_2
Publisher site
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Abstract

[In classical planning, the task is to drive a system from a given initial state into a goal state by applying actions whose effects are deterministic and known. Classical planning can be formulated as a pathfinding problem over a directed graph whose nodes represent the states of the system or enviroment, and whose edges capture the state transitions that the actions make possible. The computational challenge in classical planning results from the number of states, and hence the size of the graph, which are exponential in the number of problem variables. State-of-the-art methods in classical planning search for paths in such graphs by directing the search toward the goal using heuristic functions that are automatically derived from the problem. The heuristic functions map each state into an estimate of the distance or cost from the state to the goal, and provide the search for the goal with a sense of direction. In this chapter, we look at the model and languages for classical planning, and at the heuristic search techniques that have been developed for solving it. Variations and extensions of these methods, as well as alternative methods, will be considered in the next chapter.]

Published: Jan 1, 2013

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