This HTML5 document contains 37 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n15http://localhost/temp/predkladatel/
n16http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n12http://linked.opendata.cz/ontology/domain/vavai/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n8http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F14%3A00227821%21RIV15-MSM-21230___/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n11http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n14http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n4http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n17http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n10http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n9http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00227821%21RIV15-MSM-21230___
rdf:type
n12:Vysledek skos:Concept
dcterms:description
In recent years, we have seen a few attempts to use the Answer Set Programming (ASP) to solve the problem of conformant planning with the support for causal laws and negation as failure. Typically, the whole planning domain is encoded into a large ASP meta-program expressing what holds and which actions should be executed in all the time steps of a resulting plan. Answer sets of this meta-program then represent all the possible plans. Since the complexity of answer set computation is exponential in size of input, we propose a technique of dividing it into several smaller meta-programs - one for every state transition. Actual plan finding can then be outsourced to a graph-search algorithm. This method increases the performance of planning with ASP semantics and allows us to produce considerably longer plans. After discussing the worst-case complexity of both approaches, we introduce the prototype ASP planner GRASP that employs our suggested technique and provide the experimental comparison with an alternative planning system DLV K. In recent years, we have seen a few attempts to use the Answer Set Programming (ASP) to solve the problem of conformant planning with the support for causal laws and negation as failure. Typically, the whole planning domain is encoded into a large ASP meta-program expressing what holds and which actions should be executed in all the time steps of a resulting plan. Answer sets of this meta-program then represent all the possible plans. Since the complexity of answer set computation is exponential in size of input, we propose a technique of dividing it into several smaller meta-programs - one for every state transition. Actual plan finding can then be outsourced to a graph-search algorithm. This method increases the performance of planning with ASP semantics and allows us to produce considerably longer plans. After discussing the worst-case complexity of both approaches, we introduce the prototype ASP planner GRASP that employs our suggested technique and provide the experimental comparison with an alternative planning system DLV K.
dcterms:title
Conformant Planning with Static Causal Laws and Negation as Failure: Decomposition of the ASP Approach Conformant Planning with Static Causal Laws and Negation as Failure: Decomposition of the ASP Approach
skos:prefLabel
Conformant Planning with Static Causal Laws and Negation as Failure: Decomposition of the ASP Approach Conformant Planning with Static Causal Laws and Negation as Failure: Decomposition of the ASP Approach
skos:notation
RIV/68407700:21230/14:00227821!RIV15-MSM-21230___
n3:aktivita
n4:I
n3:aktivity
I
n3:cisloPeriodika
1
n3:dodaniDat
n9:2015
n3:domaciTvurceVysledku
n16:5428025
n3:druhVysledku
n17:J
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
8610
n3:idVysledku
RIV/68407700:21230/14:00227821
n3:jazykVysledku
n14:eng
n3:klicovaSlova
ASP; Conformant Planning; Incomplete Knowledge; Static Causal Laws
n3:klicoveSlovo
n13:Incomplete%20Knowledge n13:Conformant%20Planning n13:Static%20Causal%20Laws n13:ASP
n3:kodStatuVydavatele
IN - Indická republika
n3:kontrolniKodProRIV
[845FBAAE180D]
n3:nazevZdroje
International Journal of Artificial Intelligence
n3:obor
n10:JC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:rokUplatneniVysledku
n9:2014
n3:svazekPeriodika
12
n3:tvurceVysledku
Čertický, Michal
s:issn
0974-0635
s:numberOfPages
12
n15:organizacniJednotka
21230