Abstract
To accomplish the autonomous behavior of agents, several top-down and bottom-up agent learning architectures that consist of procedural and declarative knowledge have been implemented. They use mechanisms such as production rules, supervised neural networks and unsupervised neural networks to declare procedural knowledge. An efficient representation of procedural knowledge enhances learning and decision making. Inspired by the representation of rules, facts and knowledge in human memory, this paper presents a novel cognitive memory model, Episodic Associative Memory with a Neighborhood Effect (EAMwNE) as a method to define procedural knowledge.
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