Improving game modeling for the quoridor game state using graph databases

Abstract

Artificial intelligence has gained great importance in the last decades because based on its techniques, it is possible to make autonomous systems. In addition, it is possible to make those systems able to learn based on the previous interactions with users. This paper presents one proposal for an agent to play the Quoridor game based on some improvements in the graph model of the board. It is done by using artificial intelligence techniques to provide the capacity to learn through games played against users. Thus, learning is achieved through the use of game trees, where some of the nodes are going to be stored using a graph database. Since graph databases are one of the subgroup of the noSQL databases, which focuses in the relation representation between nodes, such databases are suitable for this kind of approaches.