Advancing motivated learning with goal creation

Document Type

Conference Proceeding

Date of Original Version



This paper reports improvements to our Motivated Learning (ML) model. These include modifications to the calculation of need/pain biases, pain-goal weights, and how actions are selected. Resource based abstract pains are complemented with pains related to desired and undesired actions by other agents. Probability based selection of goals is discussed. The minimum amount of desired resources is now set automatically by the agent. Additionally, we have presented several comparisons of Motivated Learning performance against some well-known reinforcement learning algorithms.

Publication Title

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIHLI 2014: 2014 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, Proceedings