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. INDICE: Introduction.- Logics and Knowledge Representation.- Artificial Neural Networks.- Neural-Symbolic Learning Systems.- Connectionist Modal Logic.-Applications of Connectionist Non-classical Reasoning.- Connectionist Modal Logics in Practice.- Connectionist Temporal Logic.- Connectionist Intuitionistic Logic.- Fibring Neural Networks.- Argumentation Frameworks as Neural Networks.- Probabilistic Reasoning in Neural Networks.- Relational Learning in NeuralNetworks.- Conclusions.
- ISBN: 978-3-540-73245-7
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 265
- Fecha Publicación: 01/11/2008
- Nº Volúmenes: 1
- Idioma: Inglés