Logical Model and Verification of Emotion Triggers for BDI Agents Triggering emotions in virtual multiagents is widely believed to enhance medical user interactions. Researchers have investigated the use of logical methods to develop rigorous specifications for how emotions should be triggered in artificial agents. However, verifying whether an emotional model works as intended is challenging. This paper provides a logical formalization of eventbased emotional triggering for multiagent systems psychologically grounded on the cognitive theory of emotions proposed by Ortony, Clore, and Collins, and also addresses how symbolic model checking techniques can be applied to verify the properties of the emotional belief, desire, and intention (BDI) multiagents model proposed herein by employing a modified version of the model checker for knowledge, belief, desire, and intention (MCKBDI). The results confirm that the modified MCKBDI can be applied to verify the emotion specifications of finitestate emotional multiagent systems and that one can program agents capable of using and reasoning over emotions. This approach can be utilized to confirm that the computational model of emotion is formalized as intended.

A Cognitive Model for Emotion Awareness in Industrial Chatbots Industrial applications are increasingly adopting conversational agents (chatbots) for tasks ranging from primitive conversation interfaces to intelligent human assistants. In this emerging field of research, there has been a strong drive towards modelling humanlike characteristics and behaviors in chatbots. However, only a limited number of research endeavors have focused on a chatbot for automatic characterization of enduser emotions. In order to address this limitation, we propose an artificial intelligence based cognitive model for emotion awareness in chatbots using Markov chains, word embedding, and Natural Language Processing. The proposed model is able to extract emotions from conversations, detect emotion transitions over time, predict realtime emotions and intelligently profile human participants based on their distinct emotional characteristics. We conducted experiments using a realworld enduser dataset to demonstrate the functionality of the proposed model. Results from experiments confirm the plausibility of this model for emotion awareness in industrial conversational agents.