Activity and plan recognition are well developed research fields, where much work has been done. There has been, however, almost no effort in combining both to create intelligent software that can effectively support the decisionmaking process faced by human teams enacting a joint plan. If intelligent software assistants are to offer helpful and meaningful support, and interact effectively in h...

We investigate the use of a simple, discriminative reranking approach to plan recognition in an abductive setting. In contrast to recent work, which attempts to model abductive plan recognition using various formalisms that integrate logic and graphical models (such as Markov Logic Networks or Bayesian Logic Programs), we instead advocate a simpler, more flexible approach in which plans found t...

Plan recognition is a ubiquitous task in artificial intelligence and pervasive computing research. The multigoal recognition problem presents a major challenge in the real world of plan recognition. Users often pursue several goals in a concurrent and interleaving manner, where the pursuit of goals may spread over different parts of an activity sequence and may be pursued in parallel. Existing ...

Our research investigates a case-based approach to plan recognition using incomplete incrementallylearned plan libraries. To learn plan libraries, one must be able to process novel input. Retrieval based on similarities among concrete planning situations rather than among planning actions enables recognition despite the occurrence of newly observed planning actions and states. In addition we ex...

My thesis focuses on recognition and interventions of users’ activities using open-ended and flexible software. In such exploratory settings, users’ behavior is characterized by exploration, mistakes and trial-and-error. Exploratory domains provide a flexible and rich interaction environment for their users, but induce challenges for automatic recognition and support of their activities. My the...

Less than half of the individuals of working age with visual impairments are employed and a significant barrier to employment is effective computer access. Screen reader applications offer some help but have limited context sensitivity and are of limited use in applications with dynamic “interfaces” like web pages. Sophisticated screen readers provide aid through application-specific scripts bu...

We are researching the problem of plan recognition in a complex real-world domain consisting of training battles conducted by actual troops at the US Army’s National Training Center. These battles involve hundreds of participants, last several days, and take place over a very large geographical area. Our task is to identify from this data repeated patterns of movement which correspond to planne...

We present a theoretical framework for online probabilistic plan recognition in cooperative multiagent systems. Our model extends the Abstract Hidden Markov Model (AHMM) (Bui, Venkatesh, & West 2002), and consists of a hierarchical dynamic Bayes network that allows reasoning about the interaction among multiple cooperating agents. We provide an in-depth analysis of two different policy terminat...

We present a general framework for plan recognit ion whose formulation is motivated by a general purpose algorithm for effective abduction. The knowledge representation is a restricted form of first order logic, which is made computationally explicit as a graph structure in which plans are manifest as a special kind of graph walk. Intuitively, plans are fabricated by searching an action descrip...