Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition)

Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. However, available tools and their uses are described, and program examples are given in Java. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contemporary coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques, particularly in intelligent agents and knowledge discovery. Cover Contents Preface Preface to the third edition Overview of the book Acknowledgements Introduction to knowledge based intelligent systems Intelligent machines, or what machines can do The history of artificial intelligence, or from the ‘Dark Ages’ to knowledge-based systems Summary Questions for review References Rule-based expert systems Introduction, or what is knowledge? Rules as a knowledge representation technique The main players in the expert system development team Structure of a rule-based expert system Fundamental characteristics of an expert system Forward chaining and backward chaining inference techniques MEDIA ADVISOR: a demonstration rule-based expert system Conflict resolution Advantages and disadvantages of rule-based expert systems Summary Questions for review References Uncertainty management in rule-based expert systems Introduction, or what is uncertainty? Basic probability theory Bayesian reasoning FORECAST: Bayesian accumulation of evidence Bias of the Bayesian method Certainty factors theory and evidential reasoning FORECAST: an application of certainty factors Comparison of Bayesian reasoning and certainty factors Summary Questions for review References Fuzzy expert systems Introduction, or what is fuzzy thinking? Fuzzy sets Linguistic variables and hedges Operations of fuzzy sets Fuzzy rules Fuzzy inference Building a fuzzy expert system Summary Questions for review References Bibliography Frame-based expert systems Introduction, or what is a frame? Frames as a knowledge representation technique Inheritance in frame-based systems Methods and demons Interaction of frames and rules Buy Smart: a frame-based expert system Summary Questions for review References Bibliography Artificial neural networks Introduction, or how the brain works The neuron as a simple computing element The perceptron Multilayer neural networks Accelerated learning in multilayer neural networks The Hopfield network Bidirectional associative memory Self-organising neural networks Summary Questions for review References Evolutionary computation Introduction, or can evolution be intelligent? Simulation of natural evolution Genetic algorithms Why genetic algorithms work Case study: maintenance scheduling with genetic algorithms Evolution strategies Genetic programming Summary Questions for review References Bibliography Hybrid intelligent systems Introduction, or how to combine German mechanics with Italian love Neural expert systems Neuro-fuzzy systems ANFIS: Adaptive Neuro-Fuzzy Inference System Evolutionary neural networks Fuzzy evolutionary systems Summary Questions for review References Knowledge engineering Introduction, or what is knowledge engineering? Will an expert system work for my problem? Will a fuzzy expert system work for my problem? Will a neural network work for my problem? Will genetic algorithms work for my problem? Will a hybrid intelligent system work for my problem? Summary Questions for review References Data mining and knowledge discovery Introduction, or what is data mining? Statistical methods and data visualisation Principal component analysis Relational databases and database queries The data warehouse and multidimensional data analysis Decision trees Association rules and market basket analysis Summary Questions for review References Glossary Appendix: AI tools and vendors Index "Artificial Intelligence is one of the most rapidly evolving subjects within the computing/engineering curriculum, with an emphasis on creating practical applications from hybrid techniques. Despite this, the traditional textbooks continue to expect mathematical and programming expertise beyond the scope of current undergraduates and focus on areas not relevant to many of today's courses. Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also data mining. The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. However, available tools and their uses will be described and program examples will be given in MATLAB. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contemporary coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques."--Publisher's website Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. Keeping the maths to a minimum, Negnevitsky explains the principles of AI, demonstrates how systems are built, what they are useful for and how to choose the right tool for the job