design appropriate predictors for estimating a plan’s success because humans actually use a variety of methods to perform tasks. Thus it may be difficult to assess when execution has dipped below the expected threshold because there are, indeed, many potentially acceptable thresholds.

Next the panel turned to a basic problem of the planning phase of the robotics paradigm: in the real world, environments are uncertain and dynamic; moreover, sometimes, plans are simply infeasible. Because data keep changing, planning is computationally intensive. The challenge is for the planning phase to happen quickly enough to keep the loop robust. The panel speculated that incremental planning algorithms could address the changing data challenges. Hofmann also suggested collaborative plan diagnosis as a promising area of research. This method views plan failure as a diagnostic problem—algorithms look for conflicts that need to be resolved or constraints that need to be removed to make the plan feasible. Some members of the panel also suggested that planning domains could be made more realistic if they were defined by the robot’s action capabilities.

Hofmann concluded his discussion with a set of questions about the mental modeling that constitutes the foundation of intent recognition, execution monitoring, and planning. What is the right level of abstraction—quantitative, qualitative, or hybrid models? How should shared plans be represented? How should agent resource capabilities be represented? How should human resource capabilities be modeled? How should the human psychological or operational safety model be represented? What are the best estimation model learning algorithms that support estimation and control?

Panel Three: Communication
Moderator: GJ Kruijff
Group Members: Frank Dignum, GJ Kruijff, Yukie Nagai, Daniele Nardi, Lin Padgham, Matthias Scheutz, Candy Sidner

GJ Kruijff, the moderator, provided a summary of the panel’s discussions. Kruijff indicated that the panel addressed fundamental problems associated with communication—not simply the sharing of words and gestures but the depth of meaning that words and gestures represent. The panel’s goal was not to solve these problems as much as to describe them. Every dimension of communication, Kruijff noted, is composed of multiple sub-dimensions that affect the communication process. For example, what are the tasks in which communication occurs: single events or repeated ones? Structured or unstructured? Well or poorly understood? How many actors are communicating? What kind of knowledge is necessary for communication: Domain specific? Common sense? What kind of communication is going to take place: Face-to-face or side by side?

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