Game State Retrieval with Keyword Queries
Game State Retrieval with Keyword Queries
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DOI：
10.1145/3077136.3080668
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发表时间：
2017-08
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影响因子：
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通讯作者：
Atsushi Ushiku;Shinsuke Mori;Hirotaka Kameko;Yoshimasa Tsuruoka
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文献类型：
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作者：
Atsushi Ushiku;Shinsuke Mori;Hirotaka Kameko;Yoshimasa Tsuruoka
网上有许多游戏记录数据库。为了从这样的数据库中检索游戏状态，用户通常需要以域特定语言指定目标状态，这对于新手用户来说可能难以学习。在这项工作中，我们提出了一个搜索系统，允许用户检索游戏状态从游戏记录数据库中使用的关键字。在我们的方法中，我们首先使用少量的游戏状态和对它的评论来训练一个用于符号接地的神经网络模型，然后将其应用于数据库中的所有状态，将每个状态与特征项及其得分相关联。因此，增强的数据库使用户能够使用关键字搜索一个州。为了评估所提出的方法的性能，我们进行了实验的游戏状态检索将棋（日本象棋）的比赛记录与评论。结果表明，我们的方法比全文搜索和LSTM语言模型提供了更好的结果。
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.