Inner entanglements: Narrowing the search in classical planning by problem reformulation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10408238" target="_blank" >RIV/00216208:11320/19:10408238 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/19:00334343

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=prXvsY__oC" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=prXvsY__oC</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/coin.12203" target="_blank" >10.1111/coin.12203</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Inner entanglements: Narrowing the search in classical planning by problem reformulation

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

Inner entanglements: Narrowing the search in classical planning by problem reformulation

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50803 - Information science (social aspects)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Computational Intelligence

ISSN

0824-7935

e-ISSN

—

Svazek periodika

35

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

35

Strana od-do

395-429

Kód UT WoS článku

000466182600006

EID výsledku v databázi Scopus

2-s2.0-85063265042