Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games

Artificial intelligence allows computer systems to make decisions similar to those of humans. However, the expert knowledge that artificial intelligence systems have is rarely used to teach non-expert humans in a specific knowledge domain. In this paper, we want to explore this possibility by propos...

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Autores principales:	Víctor Gonzalo-Cristóbal, Edward Rolando Núñez-Valdez, Vicente García-Díaz, Cristian González García, Alba Cotarelo, Alberto Gómez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Monte Carlo Tree Search neural networks explainable AI learning Dots and Boxes board games Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/824b238f67934bc08fdf098cf4f05c8b
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id	oai:doaj.org-article:824b238f67934bc08fdf098cf4f05c8b
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spelling	oai:doaj.org-article:824b238f67934bc08fdf098cf4f05c8b2021-11-11T15:37:59ZMonte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games10.3390/electronics102126092079-9292https://doaj.org/article/824b238f67934bc08fdf098cf4f05c8b2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2609https://doaj.org/toc/2079-9292Artificial intelligence allows computer systems to make decisions similar to those of humans. However, the expert knowledge that artificial intelligence systems have is rarely used to teach non-expert humans in a specific knowledge domain. In this paper, we want to explore this possibility by proposing a tool which presents and explains recommendations for playing board games generated by a Monte Carlo Tree Search algorithm combined with Neural Networks. The aim of the aforementioned tool is to showcase the information in an easily interpretable way and to effectively transfer knowledge: in this case, which movements should be avoided, and which action is recommended. Our system displays the state of the game in the form of a tree, showing all the movements available from the current state and a set of their successors. To convince and try to teach people, the tool offers a series of queries and all information available about every possible movement. In addition, it produces a brief textual explanation for those which are recommended or not advisable. To evaluate the tool, we performed a series of user tests, observing and assessing how participants learn while using this system.Víctor Gonzalo-CristóbalEdward Rolando Núñez-ValdezVicente García-DíazCristian González GarcíaAlba CotareloAlberto GómezMDPI AGarticleMonte Carlo Tree Searchneural networksexplainable AIlearningDots and Boxesboard gamesElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2609, p 2609 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Monte Carlo Tree Search neural networks explainable AI learning Dots and Boxes board games Electronics TK7800-8360
spellingShingle	Monte Carlo Tree Search neural networks explainable AI learning Dots and Boxes board games Electronics TK7800-8360 Víctor Gonzalo-Cristóbal Edward Rolando Núñez-Valdez Vicente García-Díaz Cristian González García Alba Cotarelo Alberto Gómez Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games
description	Artificial intelligence allows computer systems to make decisions similar to those of humans. However, the expert knowledge that artificial intelligence systems have is rarely used to teach non-expert humans in a specific knowledge domain. In this paper, we want to explore this possibility by proposing a tool which presents and explains recommendations for playing board games generated by a Monte Carlo Tree Search algorithm combined with Neural Networks. The aim of the aforementioned tool is to showcase the information in an easily interpretable way and to effectively transfer knowledge: in this case, which movements should be avoided, and which action is recommended. Our system displays the state of the game in the form of a tree, showing all the movements available from the current state and a set of their successors. To convince and try to teach people, the tool offers a series of queries and all information available about every possible movement. In addition, it produces a brief textual explanation for those which are recommended or not advisable. To evaluate the tool, we performed a series of user tests, observing and assessing how participants learn while using this system.
format	article
author	Víctor Gonzalo-Cristóbal Edward Rolando Núñez-Valdez Vicente García-Díaz Cristian González García Alba Cotarelo Alberto Gómez
author_facet	Víctor Gonzalo-Cristóbal Edward Rolando Núñez-Valdez Vicente García-Díaz Cristian González García Alba Cotarelo Alberto Gómez
author_sort	Víctor Gonzalo-Cristóbal
title	Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games
title_short	Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games
title_full	Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games
title_fullStr	Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games
title_full_unstemmed	Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games
title_sort	monte carlo tree search as a tool for self-learning and teaching people to play complete information board games
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/824b238f67934bc08fdf098cf4f05c8b
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Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games

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