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The humans are affective and cognitive beings relying on memories for theirindividual and social identities. Also, human dyadic bonds require some commonbeliefs such as empathetic behaviour for better interaction. In this sense,research studies involving human-agent interaction should resource on affect,cognition, and memory integration. The developed artificial agent system(SensAI+Expanse) includes machine learning algorithms, heuristics, and memoryas cognition aids towards emotional valence prediction on the interactinghuman. Further, an adaptive empathy score is always present in order to engagethe human in a recognisable interaction outcome. [...] The agent is resilienton collecting data, adapts its cognitive processes to each human individual ina learning best effort for proper contextualised prediction. The current studymake use of an achieved adaptive process. Also, the use of individualprediction models with specific options of the learning algorithm andevaluation metric from a previous research study. The accomplished solutionincludes a highly performant prediction ability, an efficient energy use, andfeature importance explanation for predicted probabilities. Results of thepresent study show evidence of significant emotional valence behaviourdifferences between some age ranges and gender combinations. Therefore, thiswork contributes with an artificial intelligent agent able to assist oncognitive science studies. This ability is about affective disturbances bymeans of predicting human emotional valence contextualised in space and time.Moreover, contributes with learning processes and heuristics fit to the taskincluding economy of cognition and memory to cope with the environment.Finally, these contributions include an achieved age and gender neutrality onpredicting emotional valence states in context and with very good performancefor each individual.

SensAI+Expanse Emotional Valence Prediction Studies with Cognition and Memory Integration