---
title: "A General Model for Online Probabilistic Plan Recognition"
authors:
  - "Hung Hai Bui"
doi: ""
url: "https://ijcai.org/proceedings/2003/186"
pdf: "https://ijcai.org/proceedings/2003/0186.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "1309-1317"
track: ""
keywords:
---

# A General Model for Online Probabilistic Plan Recognition

Hung Hai Bui

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 1309-1317.

## Abstract

We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHMEM can represent a richer class of probabilistic plans, and at the same time derive an efficient algorithm for plan recognition in the AHMEM based on the RaoBiackwellised Particle Filter approximate inference method.