A Probabilistic Analysis of Marker-Passing Techniques for Plan-Recognition

Useless paths are a chronic problem for marker-passing techniques. We use a probabilistic analysis to justify a method for quickly identifying and rejecting useless paths. Using the same analysis, we identify key conditions and assumptions necessary for marker-passing to perform well.Comment: Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991

The Automated Mapping of Plans for Plan Recognition

To coordinate with other agents in its environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with a specification of the possible plans the other agents may be following, and develop special techniques for discriminating among the possibilities. Perhaps more desirable would be a uniform procedure for mapping plans to general structures supporting inference based on uncertain and incomplete observations. In this paper, we describe a set of methods for converting plans represented in a flexible procedural language to observation models represented as probabilistic belief networks.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994

A Probabilistic Analysis of Marker-Passing Techniques for Plan-Recognition

Useless paths are a chronic problem for marker-passing techniques. We use a probabilistic analysis to justify a method for quickly identifying and rejecting useless paths. Using the same analysis, we identify key conditions and assumptions necessary for marker-passing to perform well. This work has been supported by the National Science Foundation under grant IRI-8911122 and by the Office of Naval Research, under contract N00014-88-K-0589. c fl1990 Eugene Charniak 1 Introduction A recognition problem is one of inferring the presence of some entity from some input, typically from observing the presence of other entities and the relations between them. We will make the common assumption that high-level recognition is accomplished by selecting an appropriate schema from a schema library. A schema is a generalized internal description of a class of entities in terms of their parts, their properties, and the relations between them. In the schema selection paradigm, to recognize a "foo"..