DocumentCode
3379071
Title
A biologically inspired computational model of Moral Decision Making for autonomous agents
Author
Cervantes, Jose-Antonio ; Rodriguez, Luis-Felipe ; Lopez, Sebastian ; Ramos, Felix
Author_Institution
Dept. of Comput. Sci., Cinvestav, Guadalajara, Mexico
fYear
2013
fDate
16-18 July 2013
Firstpage
111
Lastpage
117
Abstract
In areas such as psychology and neuroscience a common approach to study human behavior has been the development of theoretical models of cognition. In fields such as artificial intelligence, these cognitive models are usually translated into computational implementations and incorporated into the architectures of intelligent autonomous agents (AAs). The main assumption is that this design approach contributes to the development of intelligent systems capable of displaying very believable and human-like behaviors. Decision Making is one of the most investigated and computationally implemented cognitive functions. The literature reports several computational models designed to allow AAs to make decisions that help achieve their personal goals and needs. However, most models disregard crucial aspects of human decision making such as other agents´ needs, ethical values, and social norms. In this paper, we propose a biologically inspired computational model of Moral Decision Making (MDM). This model is designed to enable AAs to make decisions based on ethical and moral judgment. The simulation results demonstrate that the model helps to improve the believability of virtual agents when facing moral dilemmas.
Keywords
cognition; decision making; ethical aspects; mobile agents; AA; MDM; autonomous agents; biologically inspired computational model; ethical judgment; moral decision making; moral judgment; virtual agents; Biological system modeling; Brain modeling; Computational modeling; Decision making; Ethics; Psychology;
fLanguage
English
Publisher
ieee
Conference_Titel
Cognitive Informatics & Cognitive Computing (ICCI*CC), 2013 12th IEEE International Conference on
Conference_Location
New York, NY
Print_ISBN
978-1-4799-0781-6
Type
conf
DOI
10.1109/ICCI-CC.2013.6622232
Filename
6622232
Link To Document