Introduction to artificial intelligence
Wolfgang Ertel
- London ; New York : Springer, c2011.
- xi, 316 p. : ill. (some col.) ; 24 cm.
- Undergraduate topics in computer science .
- Undergraduate topics in computer science. .
1. Introduction. -- What is artificial intelligence? -- The history of AI -- Agents -- Knowledge-based systems -- 2. Propositional Logic. -- Syntax -- Semantics -- Proof systems -- Resolution -- Horn clauses -- Computability and complexity -- Applications and limitations -- 3. First-order Predicate Logic. -- Syntax -- Semantics -- Quantifiers and normal forms -- Proof calculi -- Resolution -- Automated Theorem Provers --Mathematical examples -- Applications -- 4. Limitations of Logic. -- The search space problem -- Decidability and incompleteness -- The flying penguin -- Modeling uncertainty -- 5. Logic Programming with PROLOG. -- PROLOG systems and implementations -- Simple exercises -- Execution control and procedural elements -- Lists -- Self-modifying programs -- A planning example -- Constraint logic programming -- 6. Search, Games and Problem Solving. -- Introduction -- Uninformed search -- Heuristic search -- Games with opponents -- Heuristic evaluation functions -- State of the art -- 7. Reasoning with Uncertainty. -- Computing with probabilities -- The principle of maximum entropy -- LEXMED, and expert system for diagnosing appendicitis -- Reasoning with Bayesian networks -- 8. Machine Learning and Data Mining. -- Data analysis -- The perceptron, a linear classifier -- The nearest neighbor method -- Decision tree learning -- Learning of Bayesian networks -- The naive Bayes classifier -- Clustering -- Data mining in practice -- 9. Neural Networks. -- From biology to simulation -- Hopfield networks -- Neural Associative memory -- Linear networks with minimal errors -- The backpropagation algorithm -- Support vector machines -- Applications -- 10 Reinforcement Learning. -- Introduction -- The task -- Uninformed combinatorial search -- Value iteration and dynamic programming -- A learning walking robot and its simulation -- Q-learning -- Exploration and exploitation -- Approximation, generalization and convergence -- Applications -- Curse of dimensionality -- 11. Solutions for the Exercises. -- Introduction -- Propositional logic -- First-order predicate logic -- Limitations of logic -- PROLOG -- Search, games and problem solving -- Reasoning with uncertainty -- Machine learning and data mining -- Neural networks -- Reinforcement learning.
This accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. It provides study exercises at the end of each chapter, plus examples, definitions, theorems, and illustrations.