Integrating model verification and self-adaptation
Proceedings of the IEEE/ACM international conference on Automated software engineering
Neuro-symbolic representation of logic programs defining infinite sets
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Learning to adapt requirements specifications of evolving systems (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
Brain-like computing based on distributed representations and neurodynamics
New Generation Computing
A neural-symbolic cognitive agent for online learning and reasoning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
Decision Support Systems
A dynamic binding mechanism for retrieving and unifying complex predicate-logic knowledge
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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Humans are often extraordinary at performing practical reasoning. There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This book is the first to offer a self-contained presentation of neural network models for a number of computer science logics, including modal, temporal, and epistemic logics. By using a graphical presentation, it explains neural networks through a sound neural-symbolic integration methodology, and it focuses on the benefits of integrating effective robust learning with expressive reasoning capabilities. The bookwill be invaluable reading for academic researchers, graduate students, and senior undergraduates in computer science, artificial intelligence, machine learning, cognitive science and engineering. It will also be of interest to computational logicians, and professional specialists on applications of cognitive, hybrid and artificial intelligence systems.