Game State Retrieval with Keyword Queries

There are many databases of game records available online. In order to retrieve a game state from such a database, users usually need to specify the target state in a domain-specific language, which may be difficult to learn for novice users. In this work, we propose a search system that allows users to retrieve game states from a game record database by using keywords. In our approach, we first train a neural network model for symbol grounding using a small number of pairs of a game state and a commentary on it. We then apply it to all the states in the database to associate each of them with characteristic terms and their scores. The enhanced database thus enables users to search for a state using keywords. To evaluate the performance of the proposed method, we conducted experiments of game state retrieval using game records of Shogi (Japanese chess) with commentaries. The results demonstrate that our approach gives significantly better results than full-text search and an LSTM language model.