Paper Info
Title
Inner entanglements: Narrowing the search in classical planning by problem reformulation
Abstract
In the field of automated planning, the central research focus is on domain-independent planning engines that accept planning tasks (domain models and problem descriptions) in a description language, such as Planning Domain Definition Language, and return solution plans. The performance of planning engines can be improved by gathering additional knowledge about specific planning domain models/tasks (such as control rules) that can narrow the search for a solution plan. Such knowledge is often learned from training plans and solutions of simple tasks. Using techniques to reformulate the given planning task to incorporate additional knowledge, while keeping to the same input language, allows to exploit off-the-shelf planning engines. In this paper, we present inner entanglements that are relations between pairs of operators and predicates that represent the exclusivity of predicate achievement or requirement between the given operators. Inner entanglements can be encoded into a planner's input language by transforming the original planning task; hence, planning engines can exploit them. The contribution of this paper is to provide an in-depth analysis and evaluation of inner entanglements, covering theoretical aspects such as complexity results, and an extensive empirical study using International Planning Competition benchmarks and state-of-the-art planning engines.
Year
DOI
Venue
2019
10.1111/coin.12203
COMPUTATIONAL INTELLIGENCE
Keywords
Field
DocType
classical planning,knowledge representation,machine learning,problem reformulation
Knowledge representation and reasoning,Computer science,Speech recognition
Journal
Volume
Issue
ISSN
35.0
2.0
0824-7935
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Lukás Chrpa19923.15
Mauro Vallati221646.63
Thomas Leo McCluskey38413.00