Introduction to Artificial Intelligence
Seiten
2011
Springer London Ltd (Verlag)
978-0-85729-298-8 (ISBN)
Springer London Ltd (Verlag)
978-0-85729-298-8 (ISBN)
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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.
This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website.
This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website.
Dr. Wolfgang Ertel is a professor at the Collaborative Center for Applied Research on Service Robotics at the Ravensburg-Weingarten University of Applied Sciences, Germany.
Introduction.- 1: Propositional Logic.- 2: First-order Predicate Logic.- 3: Limitations of Logic.- 4: Logic Programming with PROLOG.- 5: Search, Games and Problem Solving.- 6: Reasoning with Uncertainty.- 7: Machine Learning and Data Mining.- 8: Neural Networks.- 9: Reinforcement Learning.- 10: Solutions for the Exercises.
| Reihe/Serie | Undergraduate Topics in Computer Science |
|---|---|
| Übersetzer | Nathanael T. Black |
| Zusatzinfo | biography |
| Verlagsort | England |
| Sprache | englisch |
| Original-Titel | Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung |
| Maße | 156 x 234 mm |
| Gewicht | 504 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Künstliche Intelligenz |
| ISBN-10 | 0-85729-298-6 / 0857292986 |
| ISBN-13 | 978-0-85729-298-8 / 9780857292988 |
| Zustand | Neuware |
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