Intelligent Systems [electronic resource] : A Modern Approach /
Grosan, Crina.author.
Abraham, Ajith.author.
Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted.   Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.
Description based upon print version of record.
Title; Preface; Contents; Evolution of Modern Computational Intelligence; Introduction; Roots of Artificial Intelligence; Modern Artificial Intelligence; Metamodern AI; Problem Solving by Search; Introduction; What Is Search?; Tree Based Search; Graph Search; Search Methods Classification; Uninformed Search Methods; Performance Evaluation of the Uninformed Search Strategies; References; Informed (Heuristic) Search; Introduction; Heuristics; Best First Search; Greedy Search; A* Search; Comparisons and Remarks; A* Variants; References; Iterative Search; Introduction; Hill Climbing
Simulated AnnealingTabu Search; Means Ends; Summary; References; Adversarial Search; Introduction; MIN-MAX Algorithm; Alpha-beta Pruning; Comparisons and Discussions; References; Knowledge Representation and Reasoning; Introduction; Propositional Logic; First Order Predicate Logic (FOPL); Resolution in Propositional Logic and FOPL; References; Rule-Based Expert Systems; Introduction; Elements of a Rule-Based System; Structure of a Rule-Based Expert System; Types of Rule-Based Expert Systems; Conflict Resolution; Benefits and Capabilities of Rule Based Expert Systems; Types of Expert Systems
Examples of Expert SystemsReferences; Managing Uncertainty in Rule Based Expert Systems; What Is Uncertainty and How to Deal With It?; Bayesian Theory; Certainty Factors; References; Fuzzy Expert Systems; Introduction; Fuzzy Sets; Fuzzy Rules; Fuzzy Inference; References; Machine Learning; Introduction; Terminology; Learning Steps; Learning Systems Classification; References; Decision Trees; Introduction; Building a Decision Tree; Overfitting in Decision Trees; Decision Trees Variants; References; Artificial Neural Networks; Introduction
Similarities between Biological and Artificial Neural NetworksNeural Networks Types; The Perceptron; Multi-layer Perceptron; References; Advanced Artificial Neural Networks; Introduction; Jordan Network; Elman Network; Hopfield Network; Self Organizing Networks; Neocognitron; Application of Neural Networks; References; Evolutionary Algorithms; Introduction; How to Build an Evolutionary Algorithm?; Genetic Algorithms; References; Evolutionary Metaheuristics; Introduction; Representation; Mutation; Recombination; Controlling the Evolution: Survival Selection; Evolutionary Programming
Genetic ProgrammingReferences; Swarm Intelligence; Introduction; Particle Swarm Optimization; Ant Colonies Optimization; References; Hybrid Intelligent Systems; Introduction; Models of Hybrid Computational Intelligence Architectures; Integrated Intelligent System; Evolutionary Fuzzy Systems; Evolutionary Neural Networks (EANN); Hybrid Evolutionary Algorithms; References
Includes bibliographical references.
Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted.   Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.
Computational intelligence.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Intelligent Systems Reference Library