While most plan recognition systems make use of a plan library that contains the set of available plan hypotheses, little effort has been devoted to the question of how to create such a library. This problem is particularly difficult to deal with when only little domain knowledge is available—a common situation when e.g. developing a help system for an already existing software system. This pap...

Intelligent agents are often called upon to form plans that direct their own or other agents' activities. For these systems, the ability to describe plans to people in natural ways is an essential aspect of their interface. In this paper, we present the Cooperative Plan Identi cation (CPI) architecture, a computational model that generates concise, e ective textual descriptions of plan data str...

Terminological Plan Recognition Diane Litman, AT&T Bell Laboratories, USA Description logics are widely used in AI to construct concept taxonomies based on subsumption inferences. However, current description logics are unable to handle complex compositions of concepts, for example constraint networks where each node is described by an associated concept. Plans can be thought of as constraint n...

A significant amount of prior research effort in the field of Artificial Intelligence has gone into the design and analysis of planning algorithms. For the most part, this work has been guided by several strong, simplifying assumptions, most notably, that the plans will be performed in static, deterministic environments. Although these assumptions have made rigorous formal analysis possible, th...

The ability of a planner to modify a plan is considered as a valuable tool for improving ee-ciency of planning by avoiding the repetition of the same planning eeort. From a computational complexity point of view, however, it is by no means obvious that modifying a plan is com-putationally as easy as planning from scratch if the modiication has to follow the principle of \conservatism," i.e., to...

My Ph.D. dissertation (Calistri 1990) extends traditional methods of plan recognition to handle situations in which agents have flawed plans.1 This extension involves solving two problems: determining what sorts of mistakes people make when they reason about plans and figuring out how to recognize these mistakes when they occur. I have developed a complete classification of plan-based misconcep...

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