Adapting Interaction Environments to Diverse Users through Online Action Set Selection
Adapting Interaction Environments to Diverse Users through Online Action Set Selection
复制标题
通过在线动作集选择来适应不同用户的交互环境
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Pushmeet Kohli
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文献类型：
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作者：
M. H. Mahmud;Benjamin Rosman;S. Ramamoorthy;Pushmeet Kohli
交互界面是从野外机器人到视频游戏的许多系统的共同特征。在大多数应用中，这些接口必须由一组不同的用户使用，在不同配置的情况下，相同接口的有效性会有很大差异。我们解决了个性化这种界面的问题，调整参数以向用户呈现一个就他们的个人特征而言是最佳的环境-使该特定用户能够实现他们的个人最佳。我们在界面个性化中引入了一类新的问题，其中自适应界面的任务是选择完整界面的动作子集来呈现给用户。在形式化这个问题时，我们将用户建模为马尔可夫决策过程(MDP)，其中任务内的转移动态取决于用户的类型(例如，技能或灵活性)，其中类型将MDP参数化。MDP的动作集被分成互不相交的动作集，不同的动作集最适合不同的类型(过渡动力学)。然后，自适应接口的任务是选择正确的动作集。鉴于这种形式化，我们给出了视频游戏领域中模拟用户和人类用户的实验，以表明：(A)动作集选择是一类有趣的问题，(B)自适应地选择正确的动作集比坚持固定的动作集可以提高性能，以及(C)可以改进立即适用的方法，如土匪。
Interactive interfaces are a common feature of many systems ranging from field robotics to video games. In most applications, these interfaces must be used by a heterogeneous set of users, with substantial variety in effectiveness with the same interface when configured differently. We address the issue of personalizing such an interface, adapting parameters to present the user with an environment that is optimal with respect to their individual traits - enabling that particular user to achieve their personal optimum. We introduce a new class of problem in interface personalization where the task of the adaptive interface is to choose the subset of actions of the full interface to present to the user. In formalizing this problem, we model the user as a Markov decision process (MDP), wherein the transition dynamics within a task depends on the type (e.g., skill or dexterity) of the user, where the type parametrizes the MDP. The action set of the MDP is divided into disjoint set of actions, with different action-sets optimal for different type (transition dynamics). The task of the adaptive interface is then to choose the right action-set. Given this formalization, we present experiments with simulated and human users in a video game domain to show that (a) action set selection is an interesting class of problems (b) adaptively choosing the right action set improves performance over sticking to a fixed action set and (c) immediately applicable approaches such as bandits can be improved upon.