A data-driven passing interaction model for embodied basketball agents
A data-driven passing interaction model for embodied basketball agents
复制标题
数据驱动的篮球代理传球交互模型
DOI：
10.1007/s10844-015-0386-z
复制
发表时间：
2015
影响因子：
3.4
通讯作者：
Divesh Lala and Toyoaki Nishida
中科院分区：
文献类型：
--
作者：
Divesh Lala and Toyoaki Nishida
人类有能力在个人活动和协作活动之间平稳过渡，并识别其他人的这些类型的活动。我们的长期目标是设计一个智能体，它可以在个人和协作任务之间频繁切换的环境中智能地工作。篮球场景就是这样一种环境，然而，目前还不存在适合于该领域的交互式代理。在本文中，我们采取了一个智能篮球代理的数据驱动的广义模型传递的相互作用。我们首先收集虚拟篮球中人与人互动的数据，以发现围绕传球互动的行为模式。通过这些模式，我们产生了一个模型的旋转行为之前和之后的通行证执行。然后，我们将此模型实现到一个实际的篮球代理，然后进行实验与人类代理团队。结果表明，使用该模型的代理至少可以更好地沟通，比一个有能力的任务代理有限的沟通，与参与者评价代理能够识别和表达其意图。此外，我们分析通过互动使用赫伯特克拉克的联合活动理论，并提出的概念，而完全理论，应被视为代理设计的基础。
Human beings have an ability to transition smoothly between individual and collaborative activities and to recognize these types of activity in other humans. Our long-term goal is to devise an agent which can function intelligently in an environment with frequent switching between individual and collaborative tasks. A basketball scenario is such an environment, however there currently do not exist suitable interactive agents for this domain. In this paper we take a step towards intelligent basketball agents by contributing a data-driven generalized model of passing interactions. We first collect data on human-human interaction in virtual basketball to discover patterns of behavior surrounding passing interactions. Through these patterns we produce a model of rotation behavior before and after passes are executed. We then implement this model into an actual basketball agent and then conduct an experiment with a human-agent team. Results show that the agent using the model can at least communicate better than a task-competent agent with limited communication, with participants rating the agent as being able to recognize and express its intention. In addition we analyze passing interactions using Herbert Clark’s joint activity theory and propose that the concepts, while completely theoretical, should be considered as a basis for agent design.