Recommended Books

My personal research library has over 300 volumes mostly purchased from Amazon, and I am totally satisfied with their service. Each of the recommended books contributes to the design of the artificial intelligence system. If the used book prices are too high, then I suggest visiting your local university library to obtain access. Furthermore, you can perform a web search for scholarly articles that are related to each book title or author. For example a search on CiteSeer for “James Albus” yields the excellent paper A Reference Model Architecture for Intelligent Systems Design (1993) that is the earlier work leading to his recommended books.

• Engineering of Mind: An Introduction to the Science of Intelligent Systems
• Intelligent Systems: Architecture, Design, Control
• The RCS Handbook: Tools for Real Time Control Systems Software Development
• Artificial Intelligence: A Modern Approach (2nd Edition)
• Beyond AI: Creating the Conscience of the Machine
• The Singularity Is Near: When Humans Transcend Biology
• Artificial General Intelligence (Cognitive Technologies)
• Nanofuture: What’s Next For Nanotechnology
• How to Solve It: Modern Heuristics
• How to Solve It: A New Aspect of Mathematical Method (Princeton Science Library)
• Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
• Robot: Mere Machine to Transcendent Mind
• Machine Learning (Mcgraw-Hill International Edit)
• Elements of Machine Learning (Morgan Kaufmann Series in Machine Learning)
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
• Goal-Driven Learning (Bradford Books)
• Case-Based Reasoning: Experiences, Lessons, and Future Directions
• Induction: Processes of Inference, Learning, and Discovery
• Abductive Inference: Computation, Philosophy, Technology
• Symbolic Logic and Mechanical Theorem Proving (Computer Science Classics)
• Genetic Programming IV: Routine Human-Competitive Machine Intelligence (Genetic Programming)
• Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
• An Introduction to Neural Networks
• Computer Vision
• Building Problem Solvers (Artificial Intelligence)
• The Fuzzy Systems Handbook: A Practitioner’s Guide to Building, Using, & Maintaining Fuzzy Systems
• Ant Colony Optimization (Bradford Books)
• Action Diagrams: Clearly Structured Program Design
• Developing Software with UML: Object-Oriented Analysis and Design in Practice
• Applying Use Cases: A Practical Guide (2nd Edition)
• Java Concurrency in Practice
• Java Message Service (O’Reilly Java Series)
• Generative Programming: Methods, Tools, and Applications
• Code Generation in Action
• Unit Testing in Java: How Tests Drive the Code (The Morgan Kaufmann Series in Software Engineering and Programming)
• Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp
• Natural Language Understanding (2nd Edition)
• Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition
• Building Natural Language Generation Systems (Studies in Natural Language Processing)
• Comprehension: A Paradigm for Cognition
• Foundations of Computational Linguistics: Human-Computer Communication in Natural Language
• Everything that Linguists have Always Wanted to Know about Logic . . . But Were Ashamed to Ask
• Language, Proof and Logic
• WordNet: An Electronic Lexical Database (Language, Speech, and Communication)
• English Syntax - 2nd Edition
• English Grammar: An Outline
• Events in the Semantics of English: A Study in Subatomic Semantics (Learning, Development, and Conceptual Change)
• Constructions: A Construction Grammar Approach to Argument Structure (Cognitive Theory of Language and Culture Series)
• Constructions at Work: The Nature of Generalization in Language
• Radical Construction Grammar: Syntactic Theory in Typological Perspective (Oxford Linguistics)
• Constructing a Language: A Usage-Based Theory of Language Acquisition