Few plan recognition algorithms are designed to tolerate input errors. We describe a case-based plan recognition algorithm (SET-PR) that is robust to two input error types: missing and noisy actions. We extend our earlier work on SET-PR with more extensive evaluations by testing the utility of its novel action-sequence representation for plans and also investigate other design decisions (e.g., ...

Information graphics, such as bar charts and line graphs, that appear in popular media generally have a message that they are intended to convey. We have developed a novel plan inference system that uses evidence in the form of communicative signals from the graphic to recognize the graphic designer’s intended message. We contend that plan inference research would benefit from examining how eac...

Interpreting the student’s actions and inferring the student’s solution plan during problem solving is one of the main challenges of tutoring based on cognitive apprenticeship, especially in domains with large solution spaces. We present a student modeling framework that performs probabilistic plan recognition by integrating in a Bayesian network knowledge about the available plans and their st...

This paper describes a plan-recognition program built to explore the theory of endorsements (Cohen, 1983). The program evaluates alternative interpretations of user actions and reasons about which are the most likely explanation of the user's intentions. Uncertainty about the various alternatives was encoded in data structures called endorsements. The paper describes the workings of this progra...

Corpora for training plan recognizers are scarce and difficult to gather from humans. However, corpora could be a boon to plan recognition research, providing a platform to train and test individual recognizers, as well as allow different recognizers to be compared. We present a novel method for generating artificial corpora for plan recognition. The method uses a modified AI planner and Monte-...

Plan recognition is an important task whenever a system has to take into account an agent's actions and goals in order to be able to react adequately. Many plan recognizers, however, are only capable of deriving a set of equally plausible plan hypotheses. This is of little use whenever the system actually has to react, e.g., when an intelligent help system is asked to give advice to the user of...

Human-computer collaboration provides a practical and useful application for plan recognition techniques. We describe a plan recognition algorithm which is tractable by virtue of exploiting properties of the collaborative setting, namely: the focus of attention, the use of partially elaborated hierarchical plans, and the possibility of asking for clarification. We demonstrate how the addition o...

Multi-Agent Plan Recognition (MAPR) aims to recognize dynamic team structures and team behaviors from the observed team traces (activity sequences) of a set of intelligent agents. Previous MAPR approaches required a library of team activity sequences (team plans) be given as input. However, collecting a library of team plans to ensure adequate coverage is often difficult and costly. In this pap...

. This paper presents a novel case-based plan recognition system that interprets observations of plan behavior using a case library of past observations. The system is novel in that it represents a plan as a sequence of actionstate pairs rather than a sequence of actions preceded by some initial state and followed by some final goal state. The system utilizes a unique abstraction scheme to repr...

One of the promising approaches to analyzing taskoriented dialogues has involved modeling the plans of the speakers in the task domain. In general, these models work well as long as the topic follows the task structure closely, but they have difficulty in accounting for clarification subdialogues and topic change. We have developed a model based on a hierarchy of plans and metaplans that accoun...