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Logic for Computer Science - Foundations of Automatic Theorem Proving - Jean Gallier
This book focuses on positive result of Godel and its applications to automatic theorem proving. The restriction to the positive result was dictated mostly by the lack of space. Indeed it should be stressed that negative result of Church is also important as well as other fundamental negative results due to Godel.
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C Plus Plus Neural Networks and Fuzzy Logic - V B Rao
A neural network is a computational structure inspired by the study of biological neural processing. There are many different types of neural networks, from relatively simple to very complex, just as there are many theories on how biological neural processing works. We will begin with a discussion of a layered feed-forward type of neural network and branch out to other paradigms later in this chapter and in other chapters.
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Machine Learning- Neural and Statistical Classification - D Michie D Spiegelhalter C Taylor
This book is based on the EC project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.
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Introduction to Machine Learning -- N Nilsson
The notes survey many of the important topics in machine learning circa 1996. My intention was to pursue a middle ground between theory and practice. The notes concentrate on the important ideas in machine learning---it is neither a handbook of practice nor a compendium of theoretical proofs. My goal was to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The draft is just over 200 pages including front matter.
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Artificial Intelligence I - W Jones
The Documantation Conatined with this course is a collection of handouts and associated material for a masters level course. The lecture material is given and was created by Dr W F B Jones. The material was transferred and arranged for HTML by Dr A D Marshall.
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Artificial Intelligence I - Patrick Winston
An quick overview of AI from both the technical and the philosophical points of view. Topics discussed include search, A astrik, Knowledge Representation, Neural Nets.
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Logic and Proof
This course gives a brief introduction to logic, with including the resolution method of theorem-proving and its relation to the programming language Prolog. Formal logic is used for specifying and verifying computer systems and for representing knowledge in Artificial Intelligence programs.
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Building Expert Systems In Prolog - Amzi
The technology has left the confines of the academic world and has spread through many commercial institutions. People wanting to explore the technology and experiment with it have a bewildering selection of tools from which to choose. There continues to be a debate as to whether or not it is best to write expert systems using a high-level shell, an AI language such as LISP or Prolog, or a conventional language such as C.
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Artificial Intelligence II - David Marshall
A good general definition of AI could be:AI is the part of computer science concerned with designing intelligent computer systems, that is, computer systems that exhibit the characteristics we associate with intelligence in human behaviour - understanding language, learning, reasoning and solving problems.
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