人工智能：探昔论今（英文版）

　　《人工智能：探昔论今（英文版）》涵盖了人工智能的广泛领域。首先，《人工智能：探昔论今（英文版）》系统全面地介绍了人工智能的核心知识，包括传统人工智能的基本理论和技术，计算智能的基本原理和方法。其次，该书侧重于发展迅速、应用广泛的人工智能新技术，对神经网络和深度学习及其应用进行了较为全面的介绍。再次，该书理论与实践高度融合，第8章至第12章列举了人工智能的应用实例，如专家系统、智能规划、智能感知、自然语言处理等，有助于读者对人工智能的全面理解。《人工智能：探昔论今（英文版）》可作为高等院校相关专业本科生和研究生的人工智能课程教材，也可供从事人工智能研究与应用的科技工作者和技术人员学习参考。

　　蔡自兴，中南大学信息科学与工程学院教授，博士生导师，湖南省自兴人工智能研究院首席科学家。曾任中国人工智能学会副理事长，智能机器人学会专业委员会主任，中国计算机学会人工智能与模式识别专业委员会委员，中国自动化学会理事，智能自动化专业委员会委员，IEEE 计算智能学会评审委员会委员和进化计算技术委员会委员，曾任《智能系统学报》编委会副主任，《控制理论和应用》、《机器人》、《控制与决策》、《计算技术与自动化》、《冶金自动化》等6家杂志编委。

List of Tabies
List of Figures
About the Authors
Chapter 1．Introduction
1．1 Definition and Development of Artificial Intelligence
1．1．1 Definition of artificial intelligence
1．1．2 Origin and development of artificial intelligence
1．2 Classification of Artificial Intelligence Systems
1．3 Research Objectives and Contents of Artificial Intelligence
1．3．1 Research objectives of artificial intelligence
1．3．2 Research and application fields of artificial intelligence
1．4 Core Elements of Artificial Intelligence
1．5 Outline of the Book
References
Part 1： Knowledge-based Artificial Intelligence
Chapter 2．Knowledge Representation
2．1 State Space Representation
2．1．1 Problem state space description
2．1．2 Graph theory terminology and graphic method
2．1．3 Problem reduction representation
2．2 Knowledge Base
2．2．1 Definition and characteristics of knowledge base
2．2．2 Design and application of knowledge base
2．3 Ontology
2．3．1 Concept and definition of ontology
2．3．2 Composition and classification of ontology
2．3．3 Ontology modeling
2．4 Semantic Network Representation
2．4．1 Composition and characteristics of the semantic network
2．4．2 Representation of a binary semantic network
2．4．3 Representation of a multi-element semantic network
2．4．4 Inference process of a semantic network
2．5 Knowledge Graph
2．5．1 Definition and architecture of knowledge graph
2．5．2 Key technologies of knowledge graph
2．6 Frame Representation
2．6．1 Frame composition
2．6．2 Frame reasoning
2．7 Predicate Logic Representation
2．7．1 Predicate calculus
2．7．2 Predicate formula
2．8 Summary
References
Chapter 3．Knowledge Search and Reasoning
3．1 Graph Search Strategy
3．2 Blind Search
3．2．1 Breadth-first search
3．2．2 Depth-first search
3．2．3 Uniform cost search
3．3 Heuristic Search
3．3．1 Heuristic search strategy and valuation function
3．3．2 Ordered search
3．3．3 Algorithm A*
……
Part 2：Data-based Artificial Intelligence
Part 3：Application Examples of Artificial Intelligence