  The humans are affective and cognitive beings relying on memories for their
individual and social identities. Also, human dyadic bonds require some common
beliefs 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 memory
as cognition aids towards emotional valence prediction on the interacting
human. Further, an adaptive empathy score is always present in order to engage
the human in a recognisable interaction outcome. [...] The agent is resilient
on collecting data, adapts its cognitive processes to each human individual in
a learning best effort for proper contextualised prediction. The current study
make use of an achieved adaptive process. Also, the use of individual
prediction models with specific options of the learning algorithm and
evaluation metric from a previous research study. The accomplished solution
includes a highly performant prediction ability, an efficient energy use, and
feature importance explanation for predicted probabilities. Results of the
present study show evidence of significant emotional valence behaviour
differences between some age ranges and gender combinations. Therefore, this
work contributes with an artificial intelligent agent able to assist on
cognitive science studies. This ability is about affective disturbances by
means of predicting human emotional valence contextualised in space and time.
Moreover, contributes with learning processes and heuristics fit to the task
including economy of cognition and memory to cope with the environment.
Finally, these contributions include an achieved age and gender neutrality on
predicting emotional valence states in context and with very good performance
for each individual.
