EconPapers    
Economics at your fingertips  
 

AI Planning in a Constraint Programming Framework

Alexander Nareyek ()
Additional contact information
Alexander Nareyek: GMD FIRST

A chapter in Communication-Based Systems, 2000, pp 163-178 from Springer

Abstract: Abstract Conventional methods for AI planning use highly specific representations and search methods that can hardly be adapted or extended. Recently, approaches based on more general search frameworks like propositional satisfiability, operations research and constraint programming have been developed. This paper presents a model for domain-independent planning based on an extension of constraint programming. The extension makes it possible to explore the search space without the need to focus on plan length, and to favor other criteria like resource-related properties.

Keywords: Constraint Satisfaction; Constraint Satisfaction Problem; Structural Constraint; Action Task; Graph Grammar (search for similar items in EconPapers)
Date: 2000
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-015-9608-4_13

Ordering information: This item can be ordered from
http://www.springer.com/9789401596084

DOI: 10.1007/978-94-015-9608-4_13

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2026-06-01
Handle: RePEc:spr:sprchp:978-94-015-9608-4_13