Paper Info
Title
Outer entanglements: a general heuristic technique for improving the efficiency of planning algorithms.
Abstract
Domain independent planning engines accept a planning task description in a language such as PDDL and return a solution plan. Performance of planning engines can be improved by gathering additional knowledge about a class of planning tasks. In this paper we present Outer Entanglements, relations between planning operators and predicates, that are used to restrict the number of operator instances. Outer Entanglements can be encoded within a planning task description, effectively reformulating it. We provide an in depth analysis and evaluation of outer entanglements illustrating the effectiveness of using them as generic heuristics for improving the efficiency of planning engines.
Year
DOI
Venue
2018
10.1080/0952813X.2018.1509377
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Classical planning,outer entanglements,domain reformulation,state space pruning
Heuristic,Planning algorithms,Computer science,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
30.0
6
0952-813X
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
Lukás Chrpa19923.15
Mauro Vallati221646.63
Thomas Leo McCluskey38413.00