AAAI Publications, The Twenty-Sixth International FLAIRS Conference

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Learning Social Calculus with Genetic Programing
Saad Ahmad Khan, Jonathan Streater, Tarajeet Singh Bhatia, Steve Fiore, Ladislau Boloni

Last modified: 2013-05-19


Physical or simulated agents sharing an environmentwith humans must evaluate the impact of their own and other agents'actions in the specific social and cultural context. It is desirablethat this social calculus aligns itself with the models developed insociology and psychology — however, it needs to be expressed in anoperational, algorithmic form, suitable for implementation. While we can develop the framework of social calculus based onpsychological theories of human behavior, the actual form of thealgorithms can only be acquired from the knowledge of the specificculture. In this paper we consider social calculus based onculture-sanctioned social values (CSSMs). A critical component of thismodel is the set of action-impact functions (AIFs), which describe howthe actions of the agents change the CSSMs in specific settings. Wedescribe a technique to evolve the AIFs using genetic programming basedon a limited set of data pairs which can be obtained by surveying humansimmersed in the specific culture. We describe the proposed model througha scenario involving a group of soldiers and a robot acting on apeacekeeping mission.


Social learning, human-robot interaction, genetic programming

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