Hybrid Neural Systems

2000
ISBN: 978-3-540-67305-7
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 1778, 408 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1290 g

Reihe: Lecture Notes in Computer Science

Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Colorado, USA, and competently reflects the state of the art of research and development in hybrid neural systems.
The 26 revised full papers presented together with an introductory overview by the volume editors have been through a twofold process of careful reviewing and revision. The papers are organized in the following topical sections: structured connectionism and rule representation; distributed neural architectures and language processing; transformation and explanation; robotics, vision, and cognitive approaches.

Zielgruppe

Research

Autoren/Hrsg.

Sun, Ron

Wermter, Stefan

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften Neurobiologie, Verhaltensbiologie
• Mathematik | Informatik EDV | Informatik Technische Informatik Hochleistungsrechnen, Supercomputer
• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Neurologie, Klinische Neurowissenschaft
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

An Overview of Hybrid Neural Systems.- An Overview of Hybrid Neural Systems.- Structured Connectionism and Rule Representation.- Layered Hybrid Connectionist Models for Cognitive Science.- Types and Quantifiers in SHRUTI – A Connectionist Model of Rapid Reasoning and Relational Processing.- A Recursive Neural Network for Reflexive Reasoning.- A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning.- Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference.- Towards a Hybrid Model of First-Order Theory Refinement.- Distributed Neural Architectures and Language Processing.- Dynamical Recurrent Networks for Sequential Data Processing.- Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective.- Combining Maps and Distributed Representations for Shift-Reduce Parsing.- Towards Hybrid Neural Learning Internet Agents.- A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations.- Large Patterns Make Great Symbols: An Example of Learning from Example.- Context Vectors: A Step Toward a “Grand Unified Representation”.- Integration of Graphical Rules with Adaptive Learning of Structured Information.- Transformation and Explanation.- Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks.- Symbolic Rule Extraction from the DIMLP Neural Network.- Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics.- Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification.- High Order Eigentensors as Symbolic Rules in Competitive Learning.- Holistic Symbol Processing and theSequential RAAM: An Evaluation.- Robotics, Vision and Cognitive Approaches.- Life, Mind, and Robots.- Supplementing Neural Reinforcement Learning with Symbolic Methods.- Self-Organizing Maps in Symbol Processing.- Evolution of Symbolisation: Signposts to a Bridge between Connectionist and Symbolic Systems.- A Cellular Neural Associative Array for Symbolic Vision.- Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots.