A Human Memory Process Modeling

Background: The cognitive models based agents proposed in the existing patents are not able to create knowledge by themselves. They also did not have the inference mechanism to take decisions and perform planning in novel situations. Objective: This patent proposes a method to mimic the human memory process for decision making. Methods: The proposed model simulates the functionality of episodic, semantic and procedural memory along with their interaction system. The sensory information activates the activity nodes which is a binding of concept and the sensory values. These activated activity nodes are captured by the episodic memory in the form of an event node. Each activity node has some participation strength in each event depending upon its involvement among other events. Recalling of events and frequent usage of some coactive activity nodes constitute the semantic knowledge in the form of associations between the activity nodes. The model also learns the actions in context to the activity nodes by using reinforcement learning. The proposed model uses an energy-based inference mechanism for planning and decision making. Results: The proposed model is validated by deploying it in a virtual war game agent and analysing the results. The obtained results show that the proposed model is significantly associated with all the biological findings and theories related to memories. Conclusion: The implementation of this model allows humanoid and game agents to take decisions and perform planning in novel situations.