Techniques for plan recognition under uncertainty require a stochastic
model of the plan-generation process. We introduce probabilistic
state-dependent grammars (PSDGs) to represent an agent's
plan-generation process. The PSDG language model extends probabilistic
context-free grammars (PCFGs) by allowing
production probabilities to depend on an explicit model of the
planning agent's internal and external state. Given a PSDG
description of the plan-generation process, we can then use inference
algorithms that exploit the particular independence properties of the PSDG
language to efficiently answer plan-recognition queries. The combination
of the PSDG language model and inference algorithms extends the range of
plan-recognition domains for which practical probabilistic inference is
possible, as illustrated by applications in traffic monitoring and
air combat.
This page will eventually have more information about this work. Right now, it serves as an appendix to the following paper:
The following PS files provide pseudocode for the algorithms described in the corresponding sections of the paper:
Bear in mind that these files are under construction to remove inconsistencies with notation from the paper and other formatting.