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Artificial Intelligence Technique

By Smita Pareek, Nimish Kumar

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Rs. 180

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Specifications of Artificial Intelligence Technique

Book Details

  • 978-93-82247-40-1
  • English
  • 2013, 2014
  • Paper Back
  • 296

Contents

  • 1. Artificial Intelligence
    Introduction, Historical Sketch of Conventional AI Approaches and Soft Computing Constituents, What is Artificial Intelligence?, Definitions of Artificial Intelligence, What is Intelligence?, What is An Artificial Device?, Advantages of Artificial Intelligence over Natural Intelligence, Disadvantages of Artificial Intelligence as Compared to Natural Intelligence, What is the Turing Test?, What a Computer Need to Pass the Turing Test?, Approaches to Artificial Intelligence, Comparison between AI Programs and Conventional Software, Comparison between Numeric and Symbolic Processing Techniques, Characteristics of Artificial Intelligence, Artificial Intelligence Languages, Typical Artificial Intelligence Problems, Artificial Intelligence Techniques, Application Areas of Artificial Intelligence, Limits of Artificial Intelligence, What can Artificial Intelligence Systems do?, What can Artificial Intelligence Systems not do yet?, Real Life Examples of Artificial Intelligent Systems, Applications of Artificial Intelligence in Various Industries, Review Questions.
     
    2. Expert System
    Introduction, Expert System Evolution, What is Knowledge based System (KBS)?, What is an Expert System?, Typical Task Domains of Expert Systems, Characteristics of an Expert System, Comparison of Expert-Systems, Human-Experts and Conventional-Programs, Advantages and Disadvantages of Expert Systems, Expert System Architecture, Components and Interfaces, Roles in Expert System Development, Expert System Building Tools and Shells, Expert System Shells, Some Knowledge System Building Tools, Examples of Expert Systems, Application Areas of Expert Systems, Four Major Problems Faces in Current Expert System, Review Questions.
     
    3. Knowledge Representation and Control Strategies
    Introduction, Knowledge Abstraction Level, Types of Knowledge, Representations and Mappings, Properties of Knowledge Representation Systems, Knowledge Representation Approaches, Knowledge Representation Schemes, Knowledge Representation using Logic, Production System, Semantic Networks (Associative Network), Frames, Conceptual Dependency (CD), Scripts, Conceptual Graphs, Issues in Knowledge Representation, Defining the Problem as a State Space Search, Control Strategies, Search Techniques, Uninformed Search (Brute Force Search or Blind Search or Exhaustive Search or Unguided Search), Heuristic Search (or Guided Search or Informed Search or Weak Methods), Review Questions.
     
    4. Artificial Neural Network
    Introduction, Historical Development of Neural Networks, How Do Humans Do Intelligent Things?, Artificial Neural Network, Characteristics of Neural Network, Applications of Artificial Neural Networks, Advantages and Disadvantages of Neural Network, The Biological Model, The Basic Artificial Neuron, Commonly Associated Terminology, Comparison between Brain and the Computer, The Mathematical Model, Artificial Neural Network (ANN) Terminologies, Weights, Bias, Threshold, Activation Function, Calculation of Net Input using Matrix Multiplication Method, McCulloch-Pitts Neuron Model (First Neuron Model-1943), Real World Applications, Review Questions.
     
    5. Learning in Artificial Neural Network
    Introduction, Neural Network Topologies, Training of Artificial Neural Networks, Difference Between Supervised and Unsupervised Learning, Hebbian Learning, Perceptron, Single Layer Perceptron, Multi-Layer Perceptron (MLP), The Delta Learning Rule, Perceptron Vs. Delta Rule, Review Questions.
     
    6. Learning in Multi-Layered Artificial Neural Network
    Introduction, Backpropagation Networks, Principles of Training Multi-layer Neural Network using Back-Propagation, How to Calculate Derivative of a Sigmoid Function?, Merits and Demerits of Back-Propagation Network, Applications of Back-propagation Perceptrons, Kohonen Self Organizing Feature Maps (SOM), Hopfield Network, Review Questions.
     
    7. Fuzzy Logic
    Introduction, History of Fuzzy Logic, What is Fuzzy Logic?, Why to Use Fuzzy Logic?, Crisp Sets (or Classical Set), Fuzzy Sets, Membership Functions, Linguistic Variables and Linguistic Values, Fuzzy Set Representation, Fuzzy Operators, Crisp and Fuzzy Relations, Crisp Relation, Fuzzy Relation, Fuzzy Rules, Classical Vs Fuzzy Rules, Firing Fuzzy Rules, Fuzzy Inference System (FIS) or Fuzzy Logic Controller (FLC), Review Questions.
     
    8. Genetic Algorithm
    Introduction, Definitions, Operators of GA, Components of a GA, Coding/Representation of GA, Fitness, Scaling, Applications of Genetic Algorithms, Genetic Algorithms versus Conventional Programs, Advantages and Disadvantages of GA, Review Questions.
     
    P. Papers