A Probabilistic Analysis of Marker-Passing Techniques for Plan-Recognition
Glenn Carroll, Eugene Charniak
TL;DR
This paper presents a probabilistic analysis of marker-passing techniques in plan recognition, proposing a method to efficiently reject useless paths and identifying conditions for optimal performance.
Contribution
It introduces a probabilistic approach to improve marker-passing by quickly rejecting useless paths and clarifies conditions for effective plan recognition.
Findings
Probabilistic analysis effectively identifies useless paths.
Key conditions for marker-passing success are outlined.
Method improves efficiency in plan recognition tasks.
Abstract
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.
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Taxonomy
TopicsAI-based Problem Solving and Planning · Bayesian Modeling and Causal Inference · Data Management and Algorithms