Improving Domain-Independent Planning via Critical Section Macro-Operators

Result code in 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>

Alternative codes found

RIV/00216208:11320/19:10408243

Result on the web

<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>

Result language

angličtina

Original language name

Improving Domain-Independent Planning via Critical Section Macro-Operators

Original language description

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 We also introduce an “aggressive” variant of our technique that removes original operators superseded by macros from the domain model.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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

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/GA18-07252S" target="_blank" >GA18-07252S: MoRePlan: Modeling and Reformulating Planning Problems</a><br>

Continuities

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

Publication year

2019

Confidentiality

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

Article name in the collection

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-809-1

ISSN

2159-5399

e-ISSN

—

Number of pages

8

Pages from-to

7546-7553

Publisher name

AAAI Press

Place of publication

Menlo Park, California

Event location

Honolulu

Event date

Jan 27, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000486572502010