Visible to the public Deliberative and Affective Reasoning: a Bayesian Dual-Process Model

TitleDeliberative and Affective Reasoning: a Bayesian Dual-Process Model
Publication TypeConference Paper
Year of Publication2019
AuthorsHoey, Jesse, Sheikhbahaee, Zahra, MacKinnon, Neil J.
Conference Name2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
Date Publishedsep
Keywordsactive inference, affect control theory, affective alignment, affective computing, affective reasoning, Artificial agents, artificial intelligence, Bayes methods, BayesAct agents, Bayesian dual-process model, Biological system modeling, chatbots, Cognition, Collaboration, Complexity theory, Computational modeling, computationally tractable model, decision theoretic reasoning, emotional level, free energy, human experience, human factors, human interaction, human social networks, inference mechanisms, intelligent agents, intelligent computational agents, Markov Decision Process, multi-agent systems, plausible model, psychology, pubcrawl, Scalability, Social Agents, social experiences, social interactions, social order, social sciences computing, social-psychological Bayesian affect control theory, social-psychological theory, socio-technical system, sociological theory, technological agents
AbstractThe presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological and sociological theory.
Citation Keyhoey_deliberative_2019