Functionalist Emotion Model in Artificial General Intelligence

Abstract

The objective of this research is to elucidate motivation and emotion processing inan AGI (Artificial General Intelligence) system NARS (Non-Axiomatic Reasoning System). Under the basic assumption that an artificial general intelligence system should work with insufficient resources and knowledge, the emotion module can help direct the selection of internal tasks, and allow the autonomous allocation of internal resources and rapid response with urgency, so that the inference capability of AGI system can be improved. The psychological and AI theories related to emotion are extensively reviewed,including the source of emotion, the appraisal process in emotional experience, the cognitive processing and coping process, and the necessity of emotion for Artificial General Intelligence design. This dissertation describes the conceptual design, realization process and application process of emotion in NARS. The process of internal resource allocation triggeredby different emotions based on NARS reasoning framework is proposed, and the design can be applied to any scene. The similarity and difference between human emotion and artificial intelligence emotion are discussed. At the same time, the advantages and disadvantages of the design and its theory are also discussed. A recent implementation of the NARS model, will be discussed with examples. and the emotion model has been tested preliminarily in a new version of OpenNARS. New Temporal Induction model, Anticipation model, Goal processing model, and Emotion model which is implemented in the new system will also be discussed in detail. The dissertation concludes with suggestions and ideas that are put forward forthe role of emotion in future human-computer interaction.