Exploring Artificial Personality Grouping Through Decision Making in Feature Spaces

Human personality (HP) is seen as an individual’s consistent patterns of feeling, thinking, and behaving by today’s psychological studies, in which HPs are characterized in terms of traits—in particular, as relatively enduring characteristics that influence human behavior across many situations. In this sense, more generally, artificial personality (AP) is studied in computer science to develop AI agents who should behave more like humans. However, in this paper, we suggest another approach by which the APs of individual agents are distinguishable based on their behavioral characteristics in achieving tasks and not necessarily in their human-like performance. As an initial step toward AP, we propose an approach to extract human decision-making characteristics as a generative resource for encoding the variability in agent personality. Using an application example, we demonstrate the feasibility of grouping APs, divided into several steps consisting of (1) defining a feature space to measure the commonality of decision making between individual and a group of people; (2) grouping APs by using multidimensional orthogonal features in the feature space to guarantee inter-individual differences between APs in achieving for the same task; and (3) evaluating the consistency of grouping APs by performing a cluster-stability analysis. Finally, our thoughts for the future implementation of APs are discussed and presented.