- ホーム
- > 洋書
- > 英文書
- > Computer / General
基本説明
Textbook.
Full Description
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.
Contents
Introduction to Artificial IntelligenceHistory of Artificial IntelligenceContemporary Artificial IntelligenceLOGICAL INTELLIGENCEPropositional LogicBasics of Propositional LogicResolutionArtificial Intelligence ApplicationsDiscussion and Further ReadingFirst-Order LogicBasics of First-Order LogicArtificial Intelligence ApplicationsDiscussion and Further ReadingCertain Knowledge RepresentationTaxonomic KnowledgeFramesNonmonotonic LogicDiscussion and Further ReadingPROBABILISTIC INTELLIGENCEProbabilityProbability BasicsRandom VariablesMeaning of ProbabilityRandom Variables in Applications Probability in the Wumpus WorldUncertain Knowledge RepresentationIntuitive Introduction to Bayesian NetworksProperties of Bayesian NetworksCausal Networks as Bayesian NetworksInference in Bayesian NetworksNetworks with Continuous VariablesObtaining the ProbabilitiesLarge-Scale Application: PromedasAdvanced 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 InformationDiscussion and Further ReadingEMERGENT INTELLIGENCEEvolutionary ComputationGenetics Review Genetic AlgorithmsGenetic ProgrammingDiscussion and Further ReadingSwarm Intelligence Ant System Flocks Discussion and Further ReadingLEARNINGLearning Deterministic Models Supervised Learning RegressionLearning a Decision TreeLearning Probabilistic Model Parameters Learning a Single Parameter Learning Parameters in a Bayesian Network Learning Parameters with Missing DataLearning Probabilistic Model Structure Structure Learning Problem Score-Based Structure LearningConstraint-Based Structure LearningApplication: MENTORSoftware Packages for Learning Causal LearningClass Probability TreesDiscussion and Further ReadingMore Learning Unsupervised Learning Reinforcement Learning Discussion and Further ReadingLANGUAGE UNDERSTANDINGNatural Language Understanding ParsingSemantic Interpretation Concept/Knowledge Interpretation Information Extraction Discussion and Further ReadingBibliographyIndex
