This paper addresses the indexing and retrieval issues in the context of the case-based plan recognition. The indexing and storage mechanisms utilize the knowledge about planning situations that enable the recognizer to focus its search to a subset of the plan library containing relevant past plans. A two-level abstract indexing scheme, along with the incremental construction of the plan librar...

We present the PHATT algorithm for plan recognition. Unlike previous approaches to plan recognition, PHATT is based on a model of plan execution. We show that this clarifies several difficult issues in plan recognition including the execution of multiple interleaved root goals, partially ordered plans, and failing to observe actions. We present the PHATT algorithm’s theoretical basis, and an im...

ion and Indexing Although the intermediate states are very helpful when recognizing plans with incomplete plan libraries, the statespace for a given planning domain may be quite large (Kerkez and Cox, 2001). A large number of possible situations negatively affect the retrieval efficiency of the recognizer. We developed an indexing and retrieval scheme based on the concept of state abstraction t...

We present a new abductive, probabilistic theory of plan recognition. This model differs from previous theories in being centered around a model of plan execution: most previous methods have been based on plans as formal objects or on rules describing the recognition process. We show that our new model accounts for phenomena omitted from most previous plan recognition theories: notably the cumu...

This paper addresses the problem of plan recognition for multi-agent teams. Complex multi-agent tasks typically require dynamic teams where the team membership changes over time. Teams split into subteams to work in parallel, merge with other teams to tackle more demanding tasks, and disband when plans are completed. We introduce a new multi-agent plan representation that explicitly encodes dyn...

The problem of generating plan libraries for plan recognition (the inverse of the planning problem), has gained much importance in recent years, because of the dependence of the existing plan recognition techniques on them, and the difficulty of the problem. Even when there is considerable work related to the plan recognition process itself, less work has been done on the generation of such pla...

In this paper we describe an adaptive shopping assistant system utilising plan recognition. Radio Frequency Identification (RFID) sensory is used to observe a shopper’s actions, from which the plan recogniser tries to infer the goals of the user. Using this information, an automated assistant provides help tailored to the shopper’s concrete needs. We discuss why it is crucial to make the plan r...

We present a new abductive, probabilistic theory of plan recognition. This model dif­ fers from previous theories in being centered around a model of plan execution: most previous methods have been based on plans as formal objects or on rules describing the recognition process. We show that our new model accounts for phenomena omitted from most previous plan recognition theories: no­ tably the ...

In this paper, we present a method for recognising an agent’s behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent’s plan. Our contributions in this paper are two fold. In terms of...

An agent can perform erroneous actions. Despite such errors, one might want to understand what the agent tried to achieve. Such understanding is important, for example, in intelligent tutoring and expert critiquing systems. In intelligent tutoring, feedback to the agent--i.e, the student--should focus on which actions made sense and which did not. To achieve such understanding, we propose a com...