Abstract
An algorithm based on an assessment of the completeness of an explanation can be used to control inference in a plan recognition system: If the explanation is complete, inference is stopped. If the explanation is incomplete, inference is continued. If it cannot be determined whether the explanation is complete, then the system weighs the strength of its interest in continuing the analysis against the estimated cost of doing so. This algorithm places existing heuristic approaches to the control of inference in plan recognition into a unified framework. The algorithm rests on the principle that the decision to continue processing should be based primarily on the explanation chain itself, not on external factors. Only when an analysis of the explanation chain proves inconclusive should outside factors weigh heavily in the decision. Furthermore, a decision to discontinue chaining should never be final; other components of the system should have the opportunity to request that an explanation chain be extended. An implementation of the algorithm, called PAGAN, demonstrates the usefulness of this approach.
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Dr. James Mayfield is Assistant Professor of Computer Science at the University of Maryland at Baltimore County. He completed his Ph.D. in 1989 at the University of California at Berkeley, under the direction of Dr. Robert Wilensky. His paper reflects his ongoing interest in plan recognition. Dr. Mayfield's other research interests include the detection and resolution of ambiguity, and the automatic conversion of linear text to hypertext.
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Mayfield, J. Controlling inference in plan recognition. User Model User-Adap Inter 2, 55–82 (1992). https://doi.org/10.1007/BF01101859
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DOI: https://doi.org/10.1007/BF01101859