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Tractable Cost-Optimal Planning over Restricted Polytree Causal Graphs
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska högskolan. (TCSLAB)
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska högskolan. (TCSLAB)
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska högskolan. (TCSLAB)
2015 (Engelska)Ingår i: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI Press, 2015Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Causal graphs are widely used to analyze the complexity of planning problems. Many tractable classes have been identified with their aid and state-of-the-art heuristics have been derived by exploiting such classes. In particular, Katz and Keyder have studied causal graphs that are hourglasses (which is a generalization of forks and inverted-forks) and shown that the corresponding cost-optimal planning problem is tractable under certain restrictions. We continue this work by studying polytrees (which is a generalization of hourglasses) under similar restrictions. We prove tractability of cost-optimal planning by providing an algorithm based on a novel notion of variable isomorphism. Our algorithm also sheds light on the k-consistency procedure for identifying unsolvable planning instances. We speculate that this may, at least partially, explain why merge-and-shrink heuristics have been successful for recognizing unsolvable instances.

Ort, förlag, år, upplaga, sidor
AAAI Press, 2015.
Serie
Proceedings of the AAAI Conference on Artificial Intelligence, ISSN 2159-5399, E-ISSN 2374-3468
Nyckelord [en]
automated planning, causal graph, polynomial-time algorithm, cost-optimal planning, polytree
Nationell ämneskategori
Datorsystem
Identifikatorer
URN: urn:nbn:se:liu:diva-118729ISBN: 978-1-57735-703-2 (tryckt)OAI: oai:DiVA.org:liu-118729DiVA: diva2:816591
Konferens
29th AAAI Conference on Artificial Intelligence (AAAI-15), January 25–30, Austin, TX, USA
Forskningsfinansiär
CUGS (National Graduate School in Computer Science)
Tillgänglig från: 2015-06-03 Skapad: 2015-06-03 Senast uppdaterad: 2017-05-17
Ingår i avhandling
1. Computational Complexity of some Optimization Problems in Planning
Öppna denna publikation i ny flik eller fönster >>Computational Complexity of some Optimization Problems in Planning
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2017. 35 s.
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1854
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:liu:diva-136280 (URN)10.3384/diss.diva-136280 (DOI)978-91-7685-519-5 (ISBN)
Disputation
2017-06-16, Ada Lovelace, B-hus, Linköping University, SE-58183 Linköping, Linköping, 13:15 (Engelska)
Opponent
Handledare
Forskningsfinansiär
CUGS (National Graduate School in Computer Science)
Tillgänglig från: 2017-05-17 Skapad: 2017-04-05 Senast uppdaterad: 2017-09-01Bibliografiskt granskad

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http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9650

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Aghighi, MeysamJonsson, PeterStåhlberg, Simon
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Totalt: 306 träffar
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