Towards Emotion-Based Reputation Guessing Learning Agents

Trust and reputation mechanisms are part of the logical protection of intelligent agents, preventing malicious agents from acting egotistically or with the intention to damage others. Several studies in Psychology, Neurology and Anthropology claim that emotions are part of human's decision making process. However, there is a lack of understanding about how affective aspects, such as emotions, influence trust or reputation levels of intelligent agents when they are inserted into an information exchange environment, e.g. an evaluation system. In this paper we propose a reputation model that accounts for emotional bounds given by Ekman's basic emotions and inductive machine learning. Our proposal is evaluated by extracting emotions from texts provided by two online human-fed evaluation systems. Empirical results show significant agent's utility improvements with p < .05 when compared to non-emotion-wise proposals, thus, showing the need for future research in this area.