By Topic

A New Approach to Modeling Emotions and Their Use on a Decision-Making System for Artificial Agents

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Miguel A. Salichs ; Carlos III University, Leganes ; Maria Malfaz

In this paper, a new approach to the generation and the role of artificial emotions in the decision-making process of autonomous agents (physical and virtual) is presented. The proposed decision-making system is biologically inspired and it is based on drives, motivations, and emotions. The agent has certain needs or drives that must be within a certain range, and motivations are understood as what moves the agent to satisfy a drive. Considering that the well-being of the agent is a function of its drives, the goal of the agent is to optimize it. Currently, the implemented artificial emotions are happiness, sadness, and fear. The novelties of our approach are, on one hand, that the generation method and the role of each of the artificial emotions are not defined as a whole, as most authors do. Each artificial emotion is treated separately. On the other hand, in the proposed system it is not mandatory to predefine either the situations that must release any artificial emotion or the actions that must be executed in each case. Both the emotional releaser and the actions can be learned by the agent, as happens on some occasions in nature, based on its own experience. In order to test the decision-making process, it has been implemented on virtual agents (software entities) living in a simple virtual environment. The results presented in this paper correspond to the implementation of the decision-making system on an agent whose main goal is to learn from scratch how to behave in order to maximize its well-being by satisfying its drives or needs. The learning process, as shown by the experiments, produces very natural results. The usefulness of the artificial emotions in the decision-making system is proven by making the same experiments with and without artificial emotions, and then comparing the performance of the agent.

Published in:

IEEE Transactions on Affective Computing  (Volume:3 ,  Issue: 1 )