Non-optimal Semi-autonomous Agent Behavior Policy Recognition

Mathieu Lelerre, Abdel-Illah Mouaddib

2016

Abstract

The coordination between cooperative autonomous agents is mainly based on knowing or estimating the behavior policy of each others. Most approaches assume that agents estimate the policies of the others by considering the optimal ones. Unfortunately, this assumption is not valid when we face the coordination problem between semi-autonomous agents where an external entity can act to change the behavior of the agents in a non-optimal way. We face such problems when the external entity is an operator guiding or tele-operating a system where many factors can affect the behavior of the operator such as stress, hesitations, preferences, ... In such situations the recognition of the other agent policies become harder than usual since considering all situations of hesitations or stress is not feasible. In this paper, we propose an approach able to recognize and predict future actions and behavior of such agents when they can follow any policy including non-optimal ones and different hesitations and preferences cases by using online learning techniques. The main idea of our approach is based on estimating, initially, the policy by the optimal one then we update it according to the observed behavior to derive a new estimated policy. In this paper, we present three learning methods of updating policies, show their stability and efficiency and compare them with existing approaches.

Paper Citation

in Harvard Style

Lelerre M. and Mouaddib A. (2016). Non-optimal Semi-autonomous Agent Behavior Policy Recognition . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 193-200. DOI: 10.5220/0006054401930200

in Bibtex Style

@conference{ecta16,
author={Mathieu Lelerre and Abdel-Illah Mouaddib},
title={Non-optimal Semi-autonomous Agent Behavior Policy Recognition},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={193-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006054401930200},
isbn={978-989-758-201-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - Non-optimal Semi-autonomous Agent Behavior Policy Recognition
SN - 978-989-758-201-1
AU - Lelerre M.
AU - Mouaddib A.
PY - 2016
SP - 193
EP - 200
DO - 10.5220/0006054401930200