@inproceedings{c7f6733bf0a3421280a09226c88ee181,
title = "Path-finding using reinforcement learning and affective states",
abstract = "During decision making and acting in the environment humans appraise decisions and observations with feelings and emotions. In this paper we propose a framework to incorporate an emotional model into the decision making process of a machine learning agent. We use a hierarchical structure to combine reinforcement learning with a dimensional emotional model. The dimensional model calculates two dimensions representing the actual affective state of the autonomous agent. For the evaluation of this combination, we use a reinforcement learning experiment (called Dyna Maze) in which, the agent has to find an optimal path through a maze. Our first results show that the agent is able to appraise the situation in terms of emotions and react according to them.",
author = "Johannes Feldmaier and Klaus Diepold",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE RO-MAN 2014 ; Conference date: 25-08-2014 Through 29-08-2014",
year = "2014",
month = oct,
day = "15",
doi = "10.1109/ROMAN.2014.6926309",
language = "English",
series = "IEEE RO-MAN 2014 - 23rd IEEE International Symposium on Robot and Human Interactive Communication: Human-Robot Co-Existence: Adaptive Interfaces and Systems for Daily Life, Therapy, Assistance and Socially Engaging Interactions",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "543--548",
editor = "Rui Loureiro and Aris Alissandrakis and Adriana Tapus and Selma Sabanovic and Fumihide Tanaka and Yukie Nagai",
booktitle = "IEEE RO-MAN 2014 - 23rd IEEE International Symposium on Robot and Human Interactive Communication",
}