Motivated Learning for Goal Selection in Goal Nets

In Psychology, goal-setting theory, which has been studied by psychologists for over 35 years, reveals that goals play significant roles in incentive, action and performance for human beings. Based on this theory, the model of goal net has been proposed as a goal oriented agent model. The previous investigation has shown that the goal net model can support well multiple action and goal selection. In this paper, we will further show that the goal net model can simulate motivated learning of goal selections. More specifically, a reorganization algorithm is proposed to convert an original goal net to its counterpart that our learning algorithm can operate on. Our experiments show that in dynamic environments, agents with learning algorithms outperform agents with the recursive searching algorithm. In addition, the reorganization algorithm is not limited to the goal net model. It is applicable to other agent models.