Fact-Alternating Mutex Groups for Classical Planning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00319881" target="_blank" >RIV/68407700:21230/18:00319881 - isvavai.cz</a>

Result on the web

<a href="http://www.jair.org/papers/paper5321.html" target="_blank" >http://www.jair.org/papers/paper5321.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1613/jair.5321" target="_blank" >10.1613/jair.5321</a>

Result language

angličtina

Original language name

Fact-Alternating Mutex Groups for Classical Planning

Original language description

Mutex groups are defined in the context of STRIPS planning as sets of facts out of which, maximally, one can be true in any state reachable from the initial state. The importance of computing and exploiting mutex groups was repeatedly pointed out in many studies. However, the theoretical analysis of mutex groups is sparse in current literature. This work provides a complexity analysis showing that inference of mutex groups is as hard as planning itself (PSPACE-Complete) and it also shows a tight relationship between mutex groups and graph cliques. This result motivates us to propose a new type of mutex group called a fact-alternating mutex group (fam-group) of which inference is NP-Complete. Moreover, we introduce an algorithm for the inference of fam-groups based on integer linear programming that is complete with respect to the maximal fam-groups and we demonstrate how beneficial fam-groups can be in the translation of planning tasks into finite domain representation. Finally, we show that fam-groups can be used for the detection of dead- end states and we propose a simple algorithm for the pruning of operators and facts as a preprocessing step that takes advantage of the properties of fam-groups. The experimental evaluation of the pruning algorithm shows a substantial increase in a number of solved tasks in domains from the optimal deterministic track of the last two planning competitions (IPC 2011 and 2014).

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GJ15-20433Y" target="_blank" >GJ15-20433Y: Heuristic Search for Multiagent and Factored Planning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Artificial Intelligence Research

ISSN

1076-9757

e-ISSN

1943-5037

Volume of the periodical

61

Issue of the periodical within the volume

March

Country of publishing house

US - UNITED STATES

Number of pages

47

Pages from-to

475-521

UT code for WoS article

000432399000006

EID of the result in the Scopus database

2-s2.0-85044158070