---
title: "Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition"
authors:
  - "Francis Bisson"
  - "Hugo Larochelle"
  - "Froduald Kabanza"
doi: ""
url: "https://ijcai.org/proceedings/2015/134"
pdf: "https://ijcai.org/proceedings/2015/0134.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "918-923"
track: "Main Track"
keywords:
---

# Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition

Francis Bisson, Hugo Larochelle, Froduald Kabanza

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 918-923.

## Abstract

Plan recognition, the problem of inferring the goals or plans of an observed agent, is a key element of situation awareness in human-machine and machine-machine interactions for many applications. Some plan recognition algorithms require knowledge about the potential behaviours of the observed agent in the form of a plan library, together with a decision model about how the observed agent uses the plan library to make decisions. It is however difficult to elicit and specify the decision model a priori. In this paper, we present a recursive neural network model that learns such a decision model automatically. We discuss promising experimental results of the approach with comparisons to selected state-of-the-art plan recognition algorithms on three benchmark domains.