S.A. Khan, J.A. Streater, T.S. Bhatia, S. Fiore, and L. Bölöni

Learning social calculus with genetic programing


Cite as:

S.A. Khan, J.A. Streater, T.S. Bhatia, S. Fiore, and L. Bölöni. Learning social calculus with genetic programing. In Proc. of the 26th International FLAIRS Conference, pp. 88–93, May 2013.

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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.

BibTeX:

@inproceedings{Khan-2013-FLAIRS,
   title = "Learning social calculus with genetic programing",
   author = "S.A. Khan and J.A. Streater and T.S. Bhatia and S. Fiore and L. B{\"o}l{\"o}ni",
   booktitle = "Proc. of the 26th International FLAIRS Conference",
   year = "2013",
   month = "May",
   pages = "88-93",
   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.
},
}

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