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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Wickramasinghe, K., Alahakoon, D. (2005). Representation of Procedural Knowledge of an Intelligent Agent Using a Novel Cognitive Memory Model. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_102
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DOI: https://doi.org/10.1007/11552413_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28894-7
Online ISBN: 978-3-540-31983-2
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