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商品描述
This book takes the viewpoint that plain symbol processing techniques have little hope of reproducing the depth and breadth of capabilities found in human beings. The book introduces new foundational principles to AI: connectionist/neural networking methods, case based and memory based methods and picture processing.
The book looks at methods of AI as different ways of doing pattern recognition. One way to do pattern recognition is to compare a problem to stored cases. At the other end of the spectrum, Classical Symbol Processing AI compresses cases down to a small set of rules and then works only with this condensed knowledge. In between these two extremes are neural networks, especially backprop type networks. As much as possible the book compares these three basic methods using actual AI programs.
The structure of the book starts at the bottom of human abilities with vision and other simple pattern recognition abilities and moves on to the higher levels of problem solving and game playing and finally to the level of natural language and understanding of the world. At the higher levels more complex computer architectures are needed that include methods for structuring thoughts.
The book is organized in a manner in which the reader will get an intuitive feeling for the principles of AI. Throughout the book applications of basic principles are demonstrated by examining some classic AI programs in detail. The book can serve as a text for juniors, seniors and first year graduate students in Computer Science or Psychology and includes sample problems and data for exercises and a list of frequently asked questions.
The book looks at methods of AI as different ways of doing pattern recognition. One way to do pattern recognition is to compare a problem to stored cases. At the other end of the spectrum, Classical Symbol Processing AI compresses cases down to a small set of rules and then works only with this condensed knowledge. In between these two extremes are neural networks, especially backprop type networks. As much as possible the book compares these three basic methods using actual AI programs.
The structure of the book starts at the bottom of human abilities with vision and other simple pattern recognition abilities and moves on to the higher levels of problem solving and game playing and finally to the level of natural language and understanding of the world. At the higher levels more complex computer architectures are needed that include methods for structuring thoughts.
The book is organized in a manner in which the reader will get an intuitive feeling for the principles of AI. Throughout the book applications of basic principles are demonstrated by examining some classic AI programs in detail. The book can serve as a text for juniors, seniors and first year graduate students in Computer Science or Psychology and includes sample problems and data for exercises and a list of frequently asked questions.
商品描述(中文翻譯)
本書採取的觀點是，單純的符號處理技術幾乎無法重現人類所擁有的深度和廣度的能力。本書介紹了人工智慧的新基礎原則：連結主義/神經網絡方法、基於案例和基於記憶的方法以及圖像處理。
本書將人工智慧的方法視為不同的模式識別方式。進行模式識別的一種方法是將問題與儲存的案例進行比較。在光譜的另一端，傳統的符號處理人工智慧將案例壓縮為一小組規則，然後僅使用這些濃縮的知識。在這兩個極端之間是神經網絡，特別是反向傳播類型的網絡。本書儘可能地使用實際的人工智慧程序來比較這三種基本方法。
本書的結構從人類能力的底層開始，涵蓋視覺和其他簡單的模式識別能力，然後進入更高層次的問題解決和遊戲玩法，最後達到自然語言和對世界的理解。在更高的層次上，需要更複雜的計算機架構，這些架構包括結構化思維的方法。
本書的組織方式使讀者能夠對人工智慧的原則產生直觀的感受。在整本書中，通過詳細檢視一些經典的人工智慧程序，展示了基本原則的應用。本書可作為計算機科學或心理學的三年級、四年級及一年級研究生的教材，並包括範例問題和練習數據，以及常見問題的列表。
作者簡介
Donald Tveter is the author of The Pattern Recognition Basis of Artificial Intelligence, published by Wiley.
作者簡介(中文翻譯)
唐納德·特維特（Donald Tveter）是《人工智慧的模式識別基礎》（The Pattern Recognition Basis of Artificial Intelligence）的作者，該書由Wiley出版。
