Modeling social intelligence through attachment-based learning

Publication Type:

Conference Paper

Source:

Proceedings of the Japanese Society for Artificial Intelligence Workshop on Social Intelligence Design, JSAI Press, Matsue, Shimane, Japan (2001)

Keywords:

artificial intelligence

Abstract:

Augmenting social intelligence is particularly challenging because it requires a nuanced, multidisciplinary understanding of human social dynamics. Unfortunately, building a comprehensive model of human interaction is an "AI complete" problem. Nevertheless, it is possible to develop representations of particular types of interaction that can serve as a theoretical foundation for a broad range of social intelligence systems.

This paper focuses on one class of interactions: social learning. Social activity is central to learning (see Vygotski, 1980). We develop and expand a theory of social learning based on a series of seminars given by professor Marvin Minsky at MIT. Professor Minsky argues that human attachments are essential to learning, a theory he will present in his forthcoming book, The Emotion Machine. We position his theory here with the goal of providing social intelligence researchers with a theoretical model on which to base new system designs.

A model of attachment-based learning is particularly germane to this task because it illuminates the social interactions that promote learning. Often models of mind (see Minsky, 1985, Piaget, 1990) focus on the self-organization of the mind, and do not provide a theory of how people (teachers, parents, role-models, etc.) can influence and enhance the mental restructuring of others. Minsky's theory of attachment-based learning begins to address this problem.

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