Change search
CiteExportLink to record
Permanent link

Direct link
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Cost-optimal and Net-benefit Planning--A Parameterised Complexity View
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. (TCSLAB)
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. (TCSLAB)
2015 (English)In: 24th International Joint Conference on Artificial Intelligence (IJCAI-15), 2015Conference paper, Published paper (Refereed)
Abstract [en]

Cost-optimal planning (COP) uses action costs and asks for a minimum-cost plan. It is sometimes assumed that there is no harm in using actions with zero cost or rational cost. Classical complexity analysis does not contradict this assumption; planning is PSPACE-complete regardless of whether action costs are positive or non-negative, integer or rational. We thus apply parameterised complexity analysis to shed more light on this issue. Our main results are the following. COP is W[2]-complete for positive integer costs, i.e. it is no harder than finding a minimum-length plan, but it is para-NPhard if the costs are non-negative integers or positive rationals. This is a very strong indication that the latter cases are substantially harder. Net-benefit planning (NBP) additionally assigns goal utilities and asks for a plan with maximum difference between its utility and its cost. NBP is para-NP-hard even when action costs and utilities are positive integers, suggesting that it is harder than COP. In addition, we also analyse a large number of subclasses, using both the PUBS restrictions and restricting the number of preconditions and effects.

Place, publisher, year, edition, pages
National Category
Transport Systems and Logistics
URN: urn:nbn:se:liu:diva-128181ISBN: 9781577357384 OAI: diva2:929808
24th International Joint Conference on Artificial Intelligence (IJCAI-15)
CUGS (National Graduate School in Computer Science), 1054Swedish Research Council, 621- 2014-4086
Available from: 2016-05-20 Created: 2016-05-20 Last updated: 2017-10-06Bibliographically approved
In thesis
1. Computational Complexity of some Optimization Problems in Planning
Open this publication in new window or tab >>Computational Complexity of some Optimization Problems in Planning
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Automated planning is known to be computationally hard in the general case. Propositional planning is PSPACE-complete and first-order planning is undecidable. One method for analyzing the computational complexity of planning is to study restricted subsets of planning instances, with the aim of differentiating instances with varying complexity. We use this methodology for studying the computational complexity of planning. Finding new tractable (i.e. polynomial-time solvable) problems has been a particularly important goal for researchers in the area. The reason behind this is not only to differentiate between easy and hard planning instances, but also to use polynomial-time solvable instances in order to construct better heuristic functions and improve planners. We identify a new class of tractable cost-optimal planning instances by restricting the causal graph. We study the computational complexity of oversubscription planning (such as the net-benefit problem) under various restrictions and reveal strong connections with classical planning. Inspired by this, we present a method for compiling oversubscription planning problems into the ordinary plan existence problem. We further study the parameterized complexity of cost-optimal and net-benefit planning under the same restrictions and show that the choice of numeric domain for the action costs has a great impact on the parameterized complexity. We finally consider the parameterized complexity of certain problems related to partial-order planning. In some applications, less restricted plans than total-order plans are needed. Therefore, a partial-order plan is being used instead. When dealing with partial-order plans, one important question is how to achieve optimal partial order plans, i.e. having the highest degree of freedom according to some notion of flexibility. We study several optimization problems for partial-order plans, such as finding a minimum deordering or reordering, and finding the minimum parallel execution length.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. 35 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1854
National Category
Computer Systems
urn:nbn:se:liu:diva-136280 (URN)10.3384/diss.diva-136280 (DOI)978-91-7685-519-5 (ISBN)
Public defence
2017-06-16, Ada Lovelace, B-hus, Linköping University, SE-58183 Linköping, Linköping, 13:15 (English)
CUGS (National Graduate School in Computer Science)
Available from: 2017-05-17 Created: 2017-04-05 Last updated: 2017-09-01Bibliographically approved

Open Access in DiVA

fulltext(240 kB)41 downloads
File information
File name FULLTEXT01.pdfFile size 240 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Aghighi, MeysamBäckström, Christer
By organisation
Software and SystemsFaculty of Science & Engineering
Transport Systems and Logistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 41 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available


Altmetric score