Achievement, affiliation, and power: Motive profiles for artificial agents

Abstract

Computational models of motivation are tools that artificial agents can use to identify, prioritize, select and adapt the goals they will pursue autonomously. Previous research has focused on developing computational models of motivation that permit artificial agents to exhibit characteristics such as adaptive exploration, problem-finding behavior, competence-seeking behavior, and creativity. This permits self-motivated agents to identify novel or interesting goals not specifically programmed by system engineers, or adapt in complex or uncertain environments where it is difficult for system engineers to identify all possible goals in advance. However, existing computational models of motivation cover only a small subset of psychological motivation theories. There remains potential to draw on other psychological motivation theories to create artificial agents with new behavioral characteristics. This includes agents that can strive for standards of excellence, both internal and external; agents that can proactively socialize and build relationships with others; and agents that can exert their influence to gain control of resources. With these objectives in mind, this article expands our ‘‘motivation toolbox’’ with three new computational models of motivation for achievement, affiliation, and power motivation. The models are designed such that they can be used in isolation or together, embedded in an artificial ‘‘motive profile.’’ To validate the new models of motivation, three experiments are presented that compare the goal-selecting behavior of artificial agents with different motive profiles with that of humans with corresponding motive profiles. Results show that agents with different motive profiles exhibit different goal-selection characteristics, and that these various characteristics are statistically similar to behavioral trends observed experimentally in humans. The article concludes by discussing areas for the future development of each motivation model and the future roles and applications of agents with different motive profiles.