A data-driven passing interaction model for embodied basketball agents

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• 作者：Divesh Lala ; Toyoaki Nishida
• 关键词：Data modeling ; Intelligent agents ; Human ; agent interaction ; Joint activity theory
• 刊名：Journal of Intelligent Information Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：48
• 期：1
• 页码：27-60
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Information Storage and Retrieval; Data Structures, Cryptology and Information Theory; Artificial Intelligence (incl. Robotics); IT in Business; Document Preparation and Text Processing;
• 出版者：Springer US
• ISSN：1573-7675
• 卷排序：48

文摘

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.