Bayesian Affect Control Theory of Self (Bayesact-S)

Notions of identity and of the self have long been studied in social psychology and sociology as key guiding elements of social interaction and coordination. In the AI of the future, these notions will also play a role in producing natural, socially appropriate artificially intelligent agents that encompass subtle and complex human social and affective skills. This project investigates a Bayesian generalization of the sociological affect control theory of self as a theoretical foundation for socio-affectively skilled artificial agents. This theory posits that each human maintains an internal model of his or her deep sense of "self" that captures their emotional, psychological, and socio-cultural sense of being in the world. The "self" is then externalised as an identity within any given interpersonal and institutional situation, and this situational identity is the person's local (in space and time) representation of the self. Situational identities govern the actions of humans according to affect control theory. Humans will seek situations that allow them to enact identities consistent with their sense of self. This consistency is cumulative over time: if some parts of a person's self are not actualized regularly, the person will have a growing feeling of inauthenticity that they will seek to resolve. In the Bayesian Affect Control Theory of Self (Bayesact-S), the self is represented as a probability distribution, allowing it to be multi-modal (a person can maintain multiple different identities), uncertain (a person can be unsure about who they really are), and learnable (agents can learn the identities and selves of other agents).

Bayesact-S is a generalisation of Bayesact. It is based upon the Affect Control Theory of Self describes in Neil J. MacKinnon and David R. Heise Self, identity and social institutions. New York, NY: Palgrave and Macmillan.

The main paper describing Bayesact-S: See also other papers on the Bayesact page.
The following is a video showing the bayesact of self simulator.
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