A computational model of achievement motivation for artificial agents

Computational models of motivation are tools that artificial agents can use to autonomously identify, prioritize, and select the goals they will pursue. Previous research has focused on developing computational models of arousal-based theories of motivation, including novelty, curiosity and interest. However, arousal-based theories represent only one aspect of motivation. In humans, for example, curiosity is tempered by other motivations such as the need for health, safety, competence, a sense of belonging, esteem from others or influence over others. To create artificial agents that can identify and prioritize their goals according to this broader range of needs, new kinds of computational models of motivation are required. This paper expands our ‘motivation toolbox’ with a new computational model of achievement motivation for artificial agents. The model uses sigmoid curves to model approach of success and avoidance of failure. An experiment from human psychology is simulated to test the new model in virtual agents. The results are compared to human results and existing theoretical and computational models. Results show that virtual agents using our model exhibit statistically similar goal-selection characteristics to humans with corresponding motive profiles. In addition, our model outperforms existing models of achievement motivation in this respect.