Knowledge extraction from reinforcement learning
New learning paradigms in soft computing
A Hybrid Architecture for Situated Learning of Reactive Sequential Decision Making
Applied Intelligence
Sequence Learning: From Recognition and Prediction to Sequential Decision Making
IEEE Intelligent Systems
Introduction to Sequence Learning
Sequence Learning - Paradigms, Algorithms, and Applications
Asynchronous learning by emotions and cognition
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Learning behavior-selection by emotions and cognition in a multi-goal robot task
The Journal of Machine Learning Research
Transfer of Experience Between Reinforcement Learning Environments with Progressive Difficulty
Artificial Intelligence Review
The importance of cognitive architectures: an analysis based on CLARION
Journal of Experimental & Theoretical Artificial Intelligence
Cognitive architectures and the challenge of cognitive social simulation
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
HIS'12 Proceedings of the First international conference on Health Information Science
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Presents a learning model CLARION, which is a hybrid model based on the two-level approach proposed by Sun. The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that neural to symbolic representations). The model utilizes procedural and declarative knowledge (in neural and symbolic representations, respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and analyses of various ways are reported that shed light on the advantages of the model