| Summary: | Anticipating human subjects' intentions and information needs is considered one of the ultimate goals of Artificial Intelligence. Activity and plan recognition contribute to this goal by studying how low-level observations about subjects and the environment in which they act can be linked to a high-level plan representation. This task is challenging in a dynamic and uncertain environment; the environment may change while the subjects are reasoning about it, and the effects of the subjects' interactions cannot be predicted with certainty. Humans generally struggle to enact plans and maintain situation awareness in such circumstances, even when they work in teams towards a common objective. Intelligent software assistants can support human teams by monitoring their activities and plan progress, thus relieving them from some of the cognitive burden they experience. The assistants' design needs to keep into account that teams can form and disband quickly in response to environmental changes, and that the course of action may change during plan execution. It is also crucial to efficiently and incrementally process a stream of observations in order to enable online prediction of those intentions and information needs. In this thesis we propose an incremental approach for team composition and activity recognition based on probabilistic graphical models. We show that this model can successfully learn team formations and behaviours in highly dynamic domains, and that classification can be performed in polynomial time. We evaluate our model within a simulated scenario provided by an open-source computer game. In addition, we discuss an incremental approach to plan recognition that exploits the results yielded by activity recognition to assess a team's course of action. We show how this model can account for incomplete or inconsistent knowledge about recognised activities, and how it can be integrated into an existing mechanism for plan recognition.
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