Solving the Rubik’s Cube with Learned Guidance Functions

Johnson, Colin G. (2019) Solving the Rubik’s Cube with Learned Guidance Functions. In: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence. . pp. 2082-2089. ISBN 978-1-5386-9277-6. E-ISBN 978-1-5386-9276-9. (doi:10.1109/SSCI.2018.8628626)

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http://dx.doi.org/10.1109/SSCI.2018.8628626

Abstract

This paper introduces move sequence problems— problems where a system can exist in a number of states, including a goal state, with moves between those states. This paper introduces Learned Guidance Functions (LGFs) as a machine learning method to tackle these. An LGF is a function learned by supervised machine learning that predicts how far a particular state is from the goal state. These methods are applied to the challenging problem of unscrambling a Rubik’s Cube.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/SSCI.2018.8628626
Additional information: Link to software: https://www.cs.kent.ac.uk/people/staff/cgj/software/IEEE_SSCI_2018/cube.py
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 16 Oct 2018 12:35 UTC
Last Modified: 29 May 2019 21:17 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69595 (The current URI for this page, for reference purposes)
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
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