Improving Domain-Independent Planning via Critical Section Macro-Operators

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00334347" target="_blank" >RIV/68407700:21230/19:00334347 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/19:10408243

Výsledek na webu

<a href="https://aaai.org/ojs/index.php/AAAI/article/view/4746" target="_blank" >https://aaai.org/ojs/index.php/AAAI/article/view/4746</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/aaai.v33i01.33017546" target="_blank" >10.1609/aaai.v33i01.33017546</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Domain-Independent Planning via Critical Section Macro-Operators

Popis výsledku v původním jazyce

Macro-operators, macros for short, are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros from most frequent sequences of actions in training plans. Such approach priorities frequently used sequences of actions over meaningful activities to be performed for solving planning tasks. This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e.g., a robotic hand) is used. In particular, such macros capture the whole activity in which the resource is “locked” (e.g., the robotic hand is holding an object) and thus “bridge” states in which the resource is locked and cannot be used. We also introduce an “aggressive” variant of our technique that removes original operators superseded by macros from the domain model. Usefulness of macros is evaluated on several stateof-the-art planners, and a wide range of benchmarks from the learning tracks of the 2008 and 2011 editions of the International Planning Competition.

Název v anglickém jazyce

Improving Domain-Independent Planning via Critical Section Macro-Operators

Popis výsledku anglicky

Macro-operators, macros for short, are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros from most frequent sequences of actions in training plans. Such approach priorities frequently used sequences of actions over meaningful activities to be performed for solving planning tasks. This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e.g., a robotic hand) is used. In particular, such macros capture the whole activity in which the resource is “locked” (e.g., the robotic hand is holding an object) and thus “bridge” states in which the resource is locked and cannot be used. We also introduce an “aggressive” variant of our technique that removes original operators superseded by macros from the domain model. Usefulness of macros is evaluated on several stateof-the-art planners, and a wide range of benchmarks from the learning tracks of the 2008 and 2011 editions of the International Planning Competition.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA18-07252S" target="_blank" >GA18-07252S: MoRePlan: Modelování a reformulace plánovacích problémů</a><br>

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 statě ve sborníku

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-809-1

ISSN

2159-5399

e-ISSN

—

Počet stran výsledku

8

Strana od-do

7546-7553

Název nakladatele

AAAI Press

Místo vydání

Menlo Park, California

Místo konání akce

Honolulu

Datum konání akce

27. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000486572502010