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.
Highlights
Decision support systems (DSS) have attracted great interest since the beginnings of the Computer Age, being the subject of multiple studies and research
To evaluate the initial level of the users participating in this experiment, they played four games exclusively from the interactive board that the presented tool offers without having access to our system’s recommendations nor the rest of information the tool grants
From phase 1 (G1–G4) to phase 3 (G9–G12)—those without recommendations—the average resulting time per movement decreased by 29.29%, being, respectively, 15.0 and 10.6 seconds. This reduction in time taken per movement comes along with a significant improvement in squares conquered and victories so it cannot be explained just assuming participants are less involved in the game. These results fit quite well with what we reviewed in Section 2.3 about the learning process: Dreyfus et al [22] stated that, as an individual acquires more skill in a matter, their decision process becomes more intuitive, and Shuell [23] who, for his part, said that, when someone reaches the terminal phase of the learning process, some tasks become nearly automatic for them, since they can apply their abstract knowledge to a wide range of different concrete contexts
Summary
Decision support systems (DSS) have attracted great interest since the beginnings of the Computer Age, being the subject of multiple studies and research. DSSs can be defined as computer-based systems which help humans in the decision-making process [1]. Note that, typically, these systems are focused on assisting the decision maker, rather than replacing them. Educational applications and software for self-learning have caught a lot of attention for years due to their great potential for accelerating and facilitating the learning process for a wide variety of tasks and areas. Griffith et al [6] synthesized a series of studies evaluating the potential of educational applications in young children, concluding that interactive apps may be useful and accessible tools for supporting early academic development. Ehrlinger and Wöß [13]
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