Intelligent systems for engineers and scientists (3^rd Ed.)

The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance.

IntroductionIntelligent SystemsA Spectrum of Intelligent BehaviorKnowledge-Based SystemsThe Knowledge Base Rules and Facts Inference Networks Semantic NetworksDeduction, Abduction, and InductionThe Inference EngineDeclarative and Procedural ProgrammingExpert SystemsKnowledge AcquisitionSearchComputational IntelligenceIntegration with Other SoftwareFurther Reading Rule-Based SystemsRules and FactsA Rule-Based System for Boiler ControlRule Examination and Rule FiringMaintaining ConsistencyThe Closed-World AssumptionUse of Local Variables within RulesForward Chaining (a Data-Driven Strategy) Single and Multiple Instantiation of Local Variables Rete AlgorithmConflict Resolution First Come, First Served Priority Values MetarulesBackward Chaining (a Goal-Driven Strategy) The Backward-Chaining Mechanism Implementation of Backward Chaining Variations of Backward Chaining Format of Backward-Chaining RulesA Hybrid StrategyExplanation FacilitiesSummaryFurther Reading Handling Uncertainty: Probability and Fuzzy LogicSources of UncertaintyBayesian Updating Representing Uncertainty by Probability Direct Application of Bayes-- Theorem Likelihood Ratios Using the Likelihood Ratios Dealing with Uncertain Evidence Combining Evidence Combining Bayesian Rules with Production Rules A Worked Example of Bayesian Updating Discussion of the Worked Example Advantages and Disadvantages of Bayesian UpdatingCertainty TheoryIntroduction Making Uncertain Hypotheses Logical Combinations of Evidence Conjunction Disjunction Negation A Worked Example of Certainty Theory Discussion of the Worked Example Relating Certainty Factors to ProbabilitiesFuzzy Logic: Type-1 Crisp Sets and Fuzzy Sets Fuzzy Rules Defuzzification Stage 1: Scaling the Membership FunctionsStage 2: Finding the Centroid Defuzzifying at the Extremes Sugeno Defuzzification A Defuzzification AnomalyFuzzy Control Systems Crisp and Fuzzy Control Fuzzy Control Rules Defuzzification in Control SystemsFuzzy Logic: Type-2Other Techniques Dempster---Shafer Theory of Evidence InfernoSummaryFurther Reading Agents, Objects, and FramesBirds of a Feather: Agents, Objects, and FramesIntelligent AgentsAgent Architectures Logic-Based Architectures Emergent Behavior Architectures Knowledge-Level Architectures Layered ArchitecturesMultiagent Systems Benefits of a Multiagent System Building a Multiagent System Contract Nets Cooperative Problem-Solving (CPS) Shifting Matrix Management (SMM) Comparison of Cooperative Models Communication between AgentsSwarm IntelligenceObject-Oriented Systems Introducing OOP An Illustrative Example Data Abstraction Classes Instances Attributes (or Data Members) Operations (or Methods or Member Functions) Creation and Deletion of Instances Inheritance Single Inheritance Multiple and Repeated Inheritance Specialization of Methods Class Browsers Encapsulation Unified Modeling Language (UML) Dynamic (or Late) Binding Message Passing and Function CallsMetaclasses Type Checking Persistence Concurrency Active Values and Daemons OOP SummaryObjects and AgentsFrame-Based SystemsSummary: Agents, Objects, and FramesFurther ReadingSymbolic LearningIntroductionLearning by Induction Overview Learning Viewed as a Search Problem Techniques for Generalization and Specialization Universalization Replacing Constants with Variables Using Conjunctions and Disjunctions Moving up or down a Hierarchy ChunkingCase-Based Reasoning (CBR) Storing Cases Abstraction Links and Index Links Instance-of Links Exemplar Links Failure Links Retrieving Cases Adapting Case Histories Null Adaptation Parameterization Reasoning by Analogy Critics Reinstantiation Dealing with Mistaken ConclusionsSummaryFurther Reading S

Engineers and scientists working in research and academia.