Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition
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.
Cite
Text
Bisson et al. "Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition." International Joint Conference on Artificial Intelligence, 2015.Markdown
[Bisson et al. "Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/bisson2015ijcai-using/)BibTeX
@inproceedings{bisson2015ijcai-using,
title = {{Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition}},
author = {Bisson, Francis and Larochelle, Hugo and Kabanza, Froduald},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2015},
pages = {918-924},
url = {https://mlanthology.org/ijcai/2015/bisson2015ijcai-using/}
}