The book "Logical Foundations of Artificial Intelligence" by Michael R. Genesereth and Nils J. Nilsson is a comprehensive resource on logic in AI, covering important topics such as first-order predicate calculus, model theory, resolution theorem proving, and nonmonotonic reasoning. However, it is not suitable for a first course in AI due to its omission of major topics like expert systems, LISP, PROLOG, computer vision, and natural language processing. The book is better suited for a second course focused on the use of logic in AI. The book has thirteen chapters, each with exercises and suggested readings, and includes answers to all exercises, a bibliography, and an index. The chapters provide a good review of logic for students who already know it, but are too short for beginners. The chapters on resolution and nonmonotonic reasoning are particularly well-written, while others, such as those on induction and reasoning with uncertain beliefs, are too brief and lack examples. The book has some issues with terminology and presentation, such as the use of "database" to mean "a finite collection of sentences," and the lack of clarity in defining terms like "model" and "extension." The authors also fail to provide sufficient examples and explanations for important concepts, such as the distinction between logical implication and semantic consequence, and the relationship between PROLOG and pure first-order logic. The book is a good resource for a course on logic in AI, with the middle chapters being the best. However, it would benefit from revisions and additional material to improve its quality. The authors should expand on topics such as knowledge acquisition, the role of logic in AI, and the comparison of different approaches to problem-solving. They should also clarify the relationship between logic and other AI techniques, such as procedural knowledge and semantic networks. The book should also address the limitations of logic-based techniques and provide a more balanced view of AI.The book "Logical Foundations of Artificial Intelligence" by Michael R. Genesereth and Nils J. Nilsson is a comprehensive resource on logic in AI, covering important topics such as first-order predicate calculus, model theory, resolution theorem proving, and nonmonotonic reasoning. However, it is not suitable for a first course in AI due to its omission of major topics like expert systems, LISP, PROLOG, computer vision, and natural language processing. The book is better suited for a second course focused on the use of logic in AI. The book has thirteen chapters, each with exercises and suggested readings, and includes answers to all exercises, a bibliography, and an index. The chapters provide a good review of logic for students who already know it, but are too short for beginners. The chapters on resolution and nonmonotonic reasoning are particularly well-written, while others, such as those on induction and reasoning with uncertain beliefs, are too brief and lack examples. The book has some issues with terminology and presentation, such as the use of "database" to mean "a finite collection of sentences," and the lack of clarity in defining terms like "model" and "extension." The authors also fail to provide sufficient examples and explanations for important concepts, such as the distinction between logical implication and semantic consequence, and the relationship between PROLOG and pure first-order logic. The book is a good resource for a course on logic in AI, with the middle chapters being the best. However, it would benefit from revisions and additional material to improve its quality. The authors should expand on topics such as knowledge acquisition, the role of logic in AI, and the comparison of different approaches to problem-solving. They should also clarify the relationship between logic and other AI techniques, such as procedural knowledge and semantic networks. The book should also address the limitations of logic-based techniques and provide a more balanced view of AI.