Preface for any AI (Artificial Intelligence) project
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Artificial Intelligence: Foundations of Computational Agents is a book about the science of artificial intelligence (AI). AI is the study of the design of intelligent computational agents. The book is structured as a textbook but it is designed to be accessible to a wide audience.
We wrote this book because we are excited about the emergence of AI as an integrated science. As with any science being developed, AI has a coherent, formal theory and a rambunctious experimental wing. Here we balance theory and experiment and show how to link them together intimately. We develop the science of AI together with its engineering applications. We believe the adage, “There is nothing so practical as a good theory.” The spirit of our approach is captured by the dictum, “Everything should be made as simple as possible, but not simpler.” We must build the science on solid foundations; we present the foundations, but only sketch, and give some examples of, the complexity required to build useful intelligent systems. Although the resulting systems will be complex, the foundations and the building blocks should be simple.
This second edition results from extensive revision throughout the text. We have restructured the material based on feedback from instructors who have used the book in classes. We have brought it up to date to reflect the current state of the art, made parts that were difficult for students more straightforward, added more intuitive explanations, and coordinated the pseudocode algorithms with new open-source implementations of the algorithms in Python and Prolog. We have resisted the temptation to just keep adding more material. AI research is expanding so rapidly now that the volume of potential new text material is vast. However, research teaches us not only what works but also what does not work so well, allowing us to be highly selective. We have included more material on machine learning techniques that have proven successful. However, research also has trends and fashions. We have removed techniques that have been shown to be less promising, but we distinguish them from the techniques that are merely out of fashion. We include some currently unfashionable material if the problems attacked still remain and the techniques have the potential to form the basis for future research and development. We have further developed the concept of a single design space for intelligent agents, showing how many bewilderingly diverse techniques can be seen in a simple, uniform framework. This allows us to emphasize the principles underlying the foundations of computational agents, making those ideas more accessible to students.
The book can be used as an introductory text on artificial intelligence for advanced undergraduate or graduate students in computer science or related disciplines such as computer engineering, philosophy, cognitive science, or psychology. It will appeal more to the technically minded; parts are technically challenging, focusing on learning by doing: designing, building, and implementing systems. Any curious scientifically oriented reader will benefit from studying the book. Previous experience with computational systems is desirable, but prior study of the foundations upon which we build, including logic, probability, calculus, and control theory, is not necessary, because we develop the concepts as required.
The serious student will gain valuable skills at several levels ranging from expertise in the specification and design of intelligent agents to skills for implementing, testing, and improving real software systems for several challenging application domains. The thrill of participating in the emergence of a new science of intelligent agents is one of the attractions of this approach. The practical skills of dealing with a world of ubiquitous, intelligent, embedded agents are now in great demand in the marketplace.