Title

Learning Social Calculus With Genetic Programing

Abstract

Physical or simulated agents sharing an environment with humans must evaluate the impact of their own and other agents' actions in the specific social and cultural context. It is desirable that this social calculus aligns itself with the models developed in sociology and psychology - however, it needs to be expressed in an operational, algorithmic form, suitable for implementation. While we can develop the framework of social calculus based on psychological theories of human behavior, the actual form of the algorithms can only be acquired from the knowledge of the specific culture. In this paper we consider social calculus based on culture-sanctioned social values (CSSMs). A critical component of this model is the set of action-impact functions (AIFs), which describe how the actions of the agents change the CSSMs in specific settings. We describe a technique to evolve the AIFs using genetic programming based on a limited set of data pairs which can be obtained by surveying humans immersed in the specific culture. We describe the proposed model through a scenario involving a group of soldiers and a robot acting on a peacekeeping mission. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Publication Date

12-13-2013

Publication Title

FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference

Number of Pages

88-93

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84889863236 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84889863236

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