Artificial Intelligence: A Guide to Intelligent Systems, 3/e (Paperback)

Michael Negnevitsky




1.A new chapter on Data Mining and Knowledge Discovery, which introduces data mining as an integral part of knowledge discovery in large databases and considers main techniques and tools for turning data into knowledge.
2.New case studies included on clustering with a self-organising neural network and data mining
3.Updated throughout to include new developments in the discipline
1.No mathematical or programming prerequisites.
2.Linked coverage of all the latest artificial intelligence topics.
3.Question and answer format.
4.Accompanying website including student projects, accompanying software tools, software demonstrations, PowerPoint slides and solutions to exercises.


                Ch1: Introduction to knowledge-based intelligent systems

Ch2: Rule-based expert systems

Ch3: Uncertainty management in rule-based expert systems

Ch4: Fuzzy expert systems

Ch5: Frame-based expert systems

Ch6: Artifical neural networks

Ch7: Evolutionary computation

Ch8: Hybrid intelligent systems

Ch9: Knowledge engineering

Ch10: Data mining and knowledeg discovery


AppendixAI tools and vendors





1. 新增了關於數據挖掘和知識發現的新章節,將數據挖掘作為大型數據庫中知識發現的一部分,介紹了將數據轉化為知識的主要技術和工具。
2. 新增了關於使用自組織神經網絡和數據挖掘進行聚類的案例研究。
3. 全書更新了學科中的最新發展。


1. 無需數學或編程先備知識。
2. 全面涵蓋了最新的人工智能主題。
3. 問答格式。
4. 附帶網站,包括學生項目、相關軟件工具、軟件演示、PowerPoint幻燈片和習題解答。


Ch1: 知識基礎智能系統簡介
Ch2: 基於規則的專家系統
Ch3: 規則專家系統中的不確定性管理
Ch4: 模糊專家系統
Ch5: 基於框架的專家系統
Ch6: 人工神經網絡
Ch7: 進化計算
Ch8: 混合智能系統