As most users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management system (DBMS) may interact with users and use their feedback on the returned results to learn the information needs behind their queries. Current query interfaces assume that users do not learn and modify the way they express their information needs in the form of queries during their interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS and their learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users’ queries effectively. We model the interaction between the user and the DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in the form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users’ strategies and proves that it improves the effectiveness of answering queries, stochastically speaking. We propose two efficient implementations of this method over large relational databases. Our extensive empirical studies over real-world query workloads indicate that our algorithms are efficient and effective.

中文翻译：

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数据交互的博弈论方法

和大多数用户一样不是准确地知道数据库的结构和/或内容，他们的查询不是准确反映他们的信息需求。数据库管理系统 (DBMS) 可以与用户交互并使用他们对返回结果的反馈来了解他们查询背后的信息需求。当前的查询界面假设用户这样做不是学习和修改他们在与 DBMS 交互期间以查询形式表达信息需求的方式。使用真实世界的交互工作负载，我们展示了用户在与 DBMS 交互期间学习和修改如何表达他们的信息需求，并且他们的学习是通过众所周知的强化学习机制准确建模的。由于当前的数据交互系统假设用户这样做不是修改策略，无法有效发现用户查询背后的信息需求。我们将用户和 DBMS 之间的交互建模为两个理性代理之间具有相同兴趣的游戏，其目标是建立一种通用语言来以查询的形式表示信息需求。我们提出了一种强化学习方法，该方法学习并回答查询背后的信息需求，并适应用户策略的变化，并证明它提高了随机回答查询的有效性。我们提出了这种方法在大型关系数据库上的两种有效实现。我们对现实世界查询工作负载的广泛实证研究表明，我们的算法高效且有效。