Abstract
Formalizing reputation into a complex social model poses significant challenges, mainly due to its distinct social nature. In this paper we introduce the notion of reputation into the child vehicle safety simulation. From a health and safety perspective, the aim of the model is to reduce injury in children by minimizing incorrect usage of child vehicle constraints by influencing driver behaviour. A cultural framework was previously established to enable external injection of knowledge, or intervention, into the artificial society. A dynamic social network allowed the acquisition, and subsequent exchange and evolution of knowledge. We hypothesize that selective intervention criteria would achieve better system convergence. We consequently introduce reputation to be a viable selection criterion. We establish a generic reputation framework that would allow us to test alternate formalizations of reputation models. We report on the generic framework design and three initial reputation models with their respective comparative performance and potential to improve the intervention outcome.
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Kobti, Z., Rahaman, S., Snowdon, A.W., Kent, R.D. (2008). A Reputation Model Framework for Artificial Societies: A Case Study in Child Vehicle Safety Simulation. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_18
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DOI: https://doi.org/10.1007/978-3-540-68825-9_18
Publisher Name: Springer, Berlin, Heidelberg
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