Contemporary artificial intelligence

著者

• Neapolitan, Richard E.
• Jiang, Xia

書誌事項

Contemporary artificial intelligence

Richard E. Neapolitan, Xia Jiang

CRC Press, c2013

注記

Includes bibliographical references and index

内容説明・目次

内容説明

The notion of artificial intelligence (AI) often sparks thoughts of characters from science fiction, such as the Terminator and HAL 9000. While these two artificial entities do not exist, the algorithms of AI have been able to address many real issues, from performing medical diagnoses to navigating difficult terrain to monitoring possible failures of spacecrafts. Exploring these algorithms and applications, Contemporary Artificial Intelligence presents strong AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more. One of the first AI texts accessible to students, the book focuses on the most useful problem-solving strategies that have emerged from AI. In a student-friendly way, the authors cover logic-based methods; probability-based methods; emergent intelligence, including evolutionary computation and swarm intelligence; data-derived logical and probabilistic learning models; and natural language understanding. Through reading this book, students discover the importance of AI techniques in computer science.

目次

Introduction to Artificial Intelligence History of Artificial Intelligence Contemporary Artificial Intelligence LOGICAL INTELLIGENCE Propositional Logic Basics of Propositional Logic Resolution Artificial Intelligence Applications Discussion and Further Reading First-Order Logic Basics of First-Order Logic Artificial Intelligence Applications Discussion and Further Reading Certain Knowledge Representation Taxonomic Knowledge Frames Nonmonotonic Logic Discussion and Further Reading PROBABILISTIC INTELLIGENCE Probability Probability Basics Random Variables Meaning of Probability Random Variables in Applications Probability in the Wumpus World Uncertain Knowledge Representation Intuitive Introduction to Bayesian Networks Properties of Bayesian Networks Causal Networks as Bayesian Networks Inference in Bayesian Networks Networks with Continuous Variables Obtaining the Probabilities Large-Scale Application: Promedas Advanced Properties of Bayesian Network Entailed Conditional Independencies Faithfulness Markov Equivalence Markov Blankets and Boundaries Decision Analysis Decision Trees Influence Diagrams Modeling Risk Preferences Analyzing Risk Directly Good Decision versus Good Outcome Sensitivity Analysis Value of Information Discussion and Further Reading EMERGENT INTELLIGENCE Evolutionary Computation Genetics Review Genetic Algorithms Genetic Programming Discussion and Further Reading Swarm Intelligence Ant System Flocks Discussion and Further Reading LEARNING Learning Deterministic Models Supervised Learning Regression Learning a Decision Tree Learning Probabilistic Model Parameters Learning a Single Parameter Learning Parameters in a Bayesian Network Learning Parameters with Missing Data Learning Probabilistic Model Structure Structure Learning Problem Score-Based Structure Learning Constraint-Based Structure Learning Application: MENTOR Software Packages for Learning Causal Learning Class Probability Trees Discussion and Further Reading More Learning Unsupervised Learning Reinforcement Learning Discussion and Further Reading LANGUAGE UNDERSTANDING Natural Language Understanding Parsing Semantic Interpretation Concept/Knowledge Interpretation Information Extraction Discussion and Further Reading Bibliography Index

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BB11518497
• ISBN:
  • 9781439844694
• LCCN: 2012017897
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Boca Raton, Fla.
• ページ数/冊数: xiii, 501 p.
• 大きさ: 25 cm
• 分類:
  • LCC : Q335
  • DC23 : 006.3
• 件名:
  • LCSH : Artificial intelligence