Deep Heuristic-learning in the Rubik's Cube Domain: An Experimental Evaluation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10367133" target="_blank" >RIV/00216208:11320/17:10367133 - isvavai.cz</a>

Result on the web

<a href="http://ceur-ws.org/Vol-1885/57.pdf" target="_blank" >http://ceur-ws.org/Vol-1885/57.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Deep Heuristic-learning in the Rubik's Cube Domain: An Experimental Evaluation

Original language description

Recent successes of neural networks in solving combinatorial problems and games like Go, Poker and others inspire further attempts to use deep learning approaches in discrete domains. In the field of automated planning, the most popular approach is informed forward search driven by a~heuristic function which estimates the quality of encountered states. Designing a~powerful and easily-computable heuristics however is still a~challenging problem on many domains. In this paper, we use machine learning to construct such heuristic automatically. We train a~neural network to predict a~minimal number of moves required to solve a~given instance of Rubik&apos;s cube. We then use the trained network as a~heuristic distance estimator with a~standard forward-search algorithm and compare the results with other heuristics. Our experiments show that the learning approach is competitive with state-of-the-art and might be the best choice in some use-case scenarios.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Article name in the collection

Proceedings of the 17th Conference on Information Technologies - Applications and Theory (ITAT 2017)

ISBN

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ISSN

1613-0073

e-ISSN

neuvedeno

Number of pages

8

Pages from-to

57-64

Publisher name

CEUR

Place of publication

Neuveden

Event location

Martinské hole

Event date

Sep 22, 2017

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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