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Solving the Rubik's cube with stepwise deep learning
Author(s) -
Johnson Colin G.
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12665
Subject(s) - computer science , artificial intelligence , fitness function , evolutionary algorithm , cube (algebra) , artificial neural network , machine learning , set (abstract data type) , function (biology) , deep learning , sample (material) , genetic algorithm , mathematics , chemistry , chromatography , combinatorics , evolutionary biology , biology , programming language
This paper explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hillclimbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function ) from a set of sample solutions to the problem. These functions are learned using a supervised learning approach based on deep neural network learning, that is, neural networks with a number of hidden layers. This is applied to a test problem: unscrambling the Rubik's Cube using evolutionary and hillclimbing algorithms. Comparisons are made with a previous LGF approach based on random forests, with a baseline approach based on traditional error‐based fitness, and with other approaches in the literature. This demonstrates how a fitness function can be learned from existing solutions, rather than being provided by the user, increasing the autonomy of AI search processes.