Computing Science and Artificial Intelligence are concerned with producing devices that help and/or replace human beings in their daily activities. To be successful, adequate modelling of these activities needs to be carried out and this has accelerated the development of both old and new disciplines, including Logic and Computation, Neural Networks, Genetic Algorithms and Probabilistic/Casual Networks. This book looks at how these techniques could complement each other and how, by understanding the role of each in a particular application, we can pave the way towards the development of more effective intelligent systems.
Artificial Intelligence is concerned with producing devices that help or
replace human beings in their daily activities. Neural-symbolic learning
systems play a central role in this task by combining, and trying to benefit
from, the advantages of both the neural and symbolic paradigms of artificial
intelligence. This book provides a comprehensive introduction to the field
of neural-symbolic learning systems, and an invaluable overview of the
latest research issues in this area. It is divided into three sections,
covering the main topics of neural-symbolic integration - theoretical advances
in knowledge representation and learning, knowledge extraction from trained
neural networks, and inconsistency handling in neural-symbolic systems.
Each section provides a balance of theory and practice, giving the results
of applications using real-world problems in areas such as DNA sequence
analysis, power systems fault diagnosis, and software requirements specifications.
Neural-Symbolic Learning Systems will be invaluable reading for researchers
and graduate students in Engineering, Computing Science, Artificial Intelligence,
Machine Learning and Neurocomputing. It will also be of interest to Intelligent
Systems practitioners and anyone interested in applications of hybrid artificial
intelligence systems.
