Understanding how individual traits influence team performance is valuable, but these traits are not always directly observable. Prior research has inferred traits like trust from behavioral data. We analyze conversational data to identify team traits and their correlation with teaming outcomes. Using transcripts from a Minecraft-based search-and-rescue experiment, we apply topic modeling and clustering to uncover key interaction patterns. Our findings show that variations in teaming outcomes can be explained through these inferences, with different levels of predictive power derived from individual traits and team dynamics.

A widely cited explanation for how humans think about trustworthiness posits that people consider three factors, or traits, of a person (or agent) when they evaluate trustworthiness: ability, benevolence, and integrity [20]. It is common practice for intelligent agent researchers to adapt a psychometric inventory of this three-factor model of trustworthiness for assessing users' perceived trustworthiness of agents [19]. In theory, administering the inventory prior to an interaction allows researchers to assess the role of anticipated agent trustworthiness in users' behavior, while post hoc administration allows researchers to assess whether particular elements of an interaction, perhaps an experimental manipulation, impacted users' opinions of the agent. In practice, however, people frequently misuse information when they form judgments and make decisions [11, 17]. For example, a person who is momentarily happy (sad), perhaps from reminiscing about a positive (negative) event from their recent past, is likely to rate their life satisfaction as higher (lower) than if you asked them when they were in a neutral state [25]. Regardless of the saliency of information, the normative approach is to always use it the same way.

The AAAI-07 workshop program was held Sunday and Monday, July 22-23, in Vancouver, British Columbia, Canada. The program included the following thirteen workshops: (1) Acquiring Planning Knowledge via Demonstration; (2) Configuration; (3) Evaluating Architectures for Intelligence; (4) Evaluation Methods for Machine Learning; (5) Explanation-Aware Computing; (6) Human Implications of Human-Robot Interaction; (7) Intelligent Techniques for Web Personalization; (8) Plan, Activity, and Intent Recognition; (9) Preference Handling for Artificial Intelligence; (10) Semantic e-Science; (11) Spatial and Temporal Reasoning; (12) Trading Agent Design and Analysis; and (13) Information Integration on the Web.

The AAAI-07 workshop program was held Sunday and Monday, July 22-23, in Vancouver, British Columbia, Canada. The program included the following thirteen workshops: (1) Acquiring Planning Knowledge via Demonstration; (2) Configuration; (3) Evaluating Architectures for Intelligence; (4) Evaluation Methods for Machine Learning; (5) Explanation-Aware Computing; (6) Human Implications of Human-Robot Interaction; (7) Intelligent Techniques for Web Personalization; (8) Plan, Activity, and Intent Recognition; (9) Preference Handling for Artificial Intelligence; (10) Semantic e-Science; (11) Spatial and Temporal Reasoning; (12) Trading Agent Design and Analysis; and (13) Information Integration on the Web.