Plan Recognition algorithms require to recognize a complete hierarchy explaining the agent’s actions and goals. While the output of such algorithms is informative to the recognizer, the cost of its calculation is high in run-time, space, and completeness. Moreover, performing plan recognition online requires the observing agent to reason about future actions that have not yet been seen and main...

Plan recognition is the task of determining what a person is doing based on a partial view of their behavior. For example, a plan recognizer might observe a computer user executing a sequence of ls, cd, and compress commands in a Unix shell and infer that the computer user is compressing all large les that she owns. Plan recognition is often studied in domains in which people's behavior is char...

Previous work on the YAPPR plan recognition system provided algorithms for translating conventional HTN plan libraries into lexicalized grammars and treated the problem of plan recognition as one of parsing. To produce these grammars required a fixed bound for any loops within the grammar and a presented a problem for optional actions within HTN plans. In this work we show that well known trans...

The human brain is the most powerful plan recognition system we know. Central to the brain’s remarkable plan recognition capacity is a theory of mind (ToM): our intuitive conception of other agents’ mental states – chiefly, beliefs and desires – and how they cause behavior. We present a Bayesian framework for ToM-based plan recognition, expressing generative models of beliefand desire-dependent...

We extend a recent formalization of multi-agent plan recognition (MAPR), to accommodate compact multi-agent plan libraries in the form of context free grammars (CFG), incomplete plan executions, and uncertainty in the observation trace. Some existing approaches for single agent plan recognition cast it as a problem of parsing a single agent activity trace. With the help of our multi-agent CFG, ...

Plan recognition, the problem of inferring the goals or plans of an observed agent, is a key element of situation awareness in human-machine and machine-machine interactions for many applications. Some plan recognition algorithms require knowledge about the potential behaviours of the observed agent in the form of a plan library, together with a decision model about how the observed agent uses ...

Techniques for plan recognition under uncertainty require a stochastic model of the plangeneration process. We introduce probabilistic state-dependent grammars (PSDGs) to represent an agent’s plan-generation process. The PSDG language model extends probabilistic contextfree grammars (PCFGs) by allowing production probabilities to depend on an explicit model of the planning agent’s internal and ...

Di erent program understanding algorithms often use di erent representational frameworks and take advantage of numerous heuristic tricks This situation makes it is di cult to compare these approaches and their performance This paper addresses this problem by proposing constraint sat isfaction as a general framework for describing program understanding algorithms demonstrating how to tranform a ...

A model of plan recognition in discourse must be based on intended recognition, distinguish each agent's beliefs and intentions from the other's, and avoid assumptions about the correctness or completeness of the agents' beliefs. In this paper, we present an algorithm for plan recognition that is based on the Shared-Plan model of collaboration (Grosz and Sidner, 1990; Lochbaum et al., 1990) and...

The research areas of plan recognition and natural language parsing share many common features and even algorithms. However, the dialog between these two disciplines has not been effective. Specifically, significant recent results in parsing mildly context sensitive grammars have not been leveraged in the state of the art plan recognition systems. This paper will outline the relations between n...