Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games
Open Access
- 26 October 2021
- journal article
- research article
- Published by MDPI AG in Electronics
- Vol. 10 (21), 2609
- https://doi.org/10.3390/electronics10212609
Abstract
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.Keywords
Funding Information
- Fundación Universidad de Oviedo (FUO-21-095)
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