This book is an attempt to present basic artificial intelligence (AI) concepts and techniques and their use in development of software systems. Authors of this book provide a practical approach to artificial intelligence techniques, with lot of Lisp code, implementation details and practical programming advices.

The book begins with basic principles of Lisp programming. From there, the book moves on to the topic of solving problems by searching and search strategies, formalisms for knowledge representation (predicate logic, frames and production system), and machine learning techniques (ID3 algorithm, neural networks). All these topics are accompanied by small case studies of varying sizes, and implementation details with Lisp code.

Chapter content
This book is organized into eighth Chapters followed by section with References:

Chapter 1, Programming languages of Artificial intelligence, describes basic principles of AI programming. Chapter gives a manual of Lisp programming, with list of Lisp functions and description of their use, and examples of user-defined Lisp functions. Also, chapter contains set of lab exercises. This chapter is good introductory guide for Lisp programming.

Chapter 2, Intelligent agents, describes properties and structure of intelligent agents, types of agents and agent environment, and methods for agent implementation. Chapter gives set of task for student homework, and lot of useful Web addresses.

Chapter 3, Solving problem as Searching, contains basic concepts and principles of solving problem agents. Chapter describes how to formulate problem and basic search strategies. Also, chapter gives description of search algorithms and useful pieces of Lisp code, which students will be able to use in their projects. At the end of this chapter Lisp code is given for solution of a classical AI problem.

Chapter 4, Predicate Logic, describes basic principles of predicate logic and resolution as a complete inference procedure. Special attention is given to translation from natural language sentences to well formed formulas, pattern matching and unification. Chapter contains two lab exercises and short description of ProLOGic system.

Chapter 5, Production Systems, describes components of rule-based production systems. Chapter describes basic components of a production system. Also, chapter gives set of Lisp functions useful for implementation of an inference engine. In lab exercise students have to implement a rule-based system in Lisp, and check it with knowledge base for animal classification, which is given.

Chapter 6, Frames, describes frames as knowledge representation formalism. Also, chapter gives instructions for Lisp implementation of frames, and gives set of basic Lisp functions. In given lab exercise students have to implement composite Lisp functions for manipulating with frames.

Chapter 7, Machine Learning, describes goals, tasks and techniques of machine learning in artificial intelligence. Chapter gives detailed description algorithm for learning decision trees, and ID3 algorithm, which uses information content to provide the heuristic function. Lab exercises for students are at the end of this chapter.

Chapter 8, Neural networks, describes basic principles of artificial neural networks, model of artificial neuron and model of an artificial neural network. Chapter contains UML diagrams and C++ source code for implementation of neural networks. In given lab exercise student have to use given code to apply neural network in real world problem.

This book was written primarily for students, and it contains the information needed for discovering artificial intelligence principles and techniques and their use in implementation of software systems. Reader of this book can learn about Lisp programming in general, and find particular information about knowledge representation formalism and machine learning techniques. Chapter 1 is a good grounding in both the syntax and use of Lisp programming language. In the rest chapter's reader can find lot of Lisp code and implementation details.

Also, this book is useful for teachers in AI. Chapters provide an in-depth discussion of artificial intelligence principles and practices. It will give you guidance on how to build concrete AI systems. Our recommendation is that this book is useful as manual for lab exercises, but also as an introductory book of AI principles and programming in an Artificial intelligence course.