Towards emotion-aware intelligent agents by utilizing knowledge graphs of experiences

Because of the increasing presence of intelligent agents in various aspects of human social life, social skills play a vital role in ensuring these systems exhibit acceptable and realistic behavior in social communication. The importance of emotional intelligence in social capabilities is noteworthy, so incorporating emotions into the behaviors of intelligent agents is essential. Therefore, some computational models of emotions have been presented to develop intelligent agents that exhibit emotional human-like behaviors. However, most current computational models of emotions neglect the dynamic learning of the affective meaning of events based on agents’ experiences. Such models evaluate the events in the environment according to emotional aspects without considering the context of the situations. Also, these models capture the emotional states of agents by using predefined rules determined according to psychological theories. Therefore, they disregard the data-driven methods that can obtain the relationships between appraisal variables and emotions based on natural human data with fewer assumptions on the nature of such relationships. To address these issues, we proposed a novel and unified affective-cognitive framework (EIAEC) to facilitate the development of emotion-aware intelligent agents. EIAEC uses appraisal theories to acquire the emotional states of the agent in various situations. This paper contains four main contributions: 1- We have designed an efficient episodic memory that uses events and their conditional contexts to store and retrieve knowledge and experiences. This memory facilitates emotional expressions and decision-making adapted to the situations of the agent. 2- A novel method has been proposed that learns context-dependent affective values associated with events by using the agent’s experiences in various contexts. Subsequently, we acquired appraisal variables using the elements and related meta-data in episodic memory. 3- We have proposed a new data-driven method that maps appraisal variables to emotional states. 4- Moreover, a method has been developed to update the activation values regarding actions by using the emotional states of the agent. This method models the influence of emotions on the agent’s decision-making. Finally, we simulate a driving scenarios in our proposed framework to manifest the generated emotions in different situations and conditions. Moreover, we show how the proposed method learns the affective meaning of events and actions used in appraisal computing.