Interface agents aim at collaborating with a user in the use of a software application. An interface agent needs to understand what the user is doing to act always in the context of the users intention. Moreover, the agent should gradually learn how to better assist the user, adapting to his interests, habits and preferences. Acting in this way, the agent would not bother the user in an imprope...

A plan recognition architecture is presented which exploits application-specific heuristic knowledge to quickly focus the search to a small set of plausible plan interpretations from the very large set of possible interpretations. The heuristic knowledge is formalized for use in a truth maintenance system where interpretation assumptions and their heuristic justifications are recorded. By forma...

Recognition of goals and plans using incomplete evidence from action execution can be done efficiently by using planning techniques. In many applications it is important to recognize goals and plans not only accurately, but also quickly. In this paper, we develop a heuristic approach for recognizing plans based on planning techniques that rely on ordering constraints to filter candidate goals f...

This pa r er outlines a new theory of plan recognition that is significant y more powerful than previous approaches. Concurrent actions. shared steps between actions, and disjunctive information are all handled. The theory allows one to draw conclusions based on the class of possible plans being performed, rather than having to prematurely commit to a single interpretation. The theory employs c...

Plan recognition algorithms need to maintain all candidate hypotheses which are consistent with the observations, even though there is only a single hypothesis that is the correct one. Unfortunately, the number of possible hypotheses can be exponentially large in practice. This paper addresses the problem of how to disambiguate between many possible hypotheses that are all consistent with the a...

Plan recognition algorithms infer agents’ plans from their observed actions. Due to imperfect knowledge about the agent’s behavior and the environment, it is often the case that there are multiple hypotheses about an agent’s plans that are consistent with the observations, though only one of these hypotheses is correct. This paper addresses the problem of how to disambiguate between hypotheses,...

Modern multi-core computers provide an opportunity to parallelize plan recognition algorithms to decrease runtime. Viewing the problem as one of parsing and performing a complete breadth first search, makes ELEXIR (Engine for LEXicalized Intent Recognition)(Geib 2009; Geib & Goldman 2011) particularly suitable for such parallelism. This paper documents the extension of ELEXIR to utilize such mo...

In this work we aim to narrow the gap between plan recognition and planning by exploiting the power and generality of recent planning algorithms for recognizing the set G∗ of goals G that explain a sequence of observations given a domain theory. After providing a crisp definition of this set, we show by means of a suitable problem transformation that a goalG belongs to G∗ if there is an action ...