Description
Wiley Artificial Intelligence, As per AICTE Making a System Intelligent 2019 Edition by Dr. Nilakshi Jain
Artificial Intelligence: Making a System Intelligent explains concept of intelligent systems, techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation, and intelligent agents. The principles behind these techniques have been explained in this book without resorting to complex mathematics. The lack of assumed prior knowledge makes this book ideal for any introductory course in artificial intelligence or intelligent systems design. The contemporary coverage of this book is highly beneficial to advanced students as it facilitates in discovering state-of-the-art techniques, particularly in intelligent agents and knowledge discovery.About the AuthorDr. Nilakshi Jain currently serves as an Associate Professor at the Shah & Anchor Kutchhi Engineering College in the Information Technology Department, Mumbai, India. She is the Research Coordinator of SAKEC Research Cell and President of Institution Innovation Council. She is a Certified Ethical Hacker CEH (EC-Council- USA). She has a rich experience of working in the Digital Forensic field. Her areas of research include Artificial Intelligence, Human Computer Interaction, and Usability Engineering. Table of Contents : - 1 Introduction to Artificial Intelligence1.1 Introduction1.2 Definition of AI1.3 Goals of AI1.4 Abridged History and Foundation of AI1.5 Branches or Subareas of AI1.6 Applications of AI1.7 Categorization of AI1.8 Components of AI1.9 Current Trends in AI1.10 AI Programming Languages 2 Intelligent Agents2.1 Introduction2.2 Intelligent Systems2.3 The Concept of Rationality2.4 Types of Agents2.5 Environments and Its Properties2.6 PEAS Representation for an Agent2.7 Intelligent Agent Application 3 Problem Solving3.1 Introduction to Problem Solving3.2 Problem Formulation3.3 State–Space Representation3.4 Problem Formulation of the Eight Tile Puzzle3.5 Problem Formulation of Water Jug Problem3.6 Problem Formulation Vacuum Cleaner World Problem3.7 Problem Formulation of Wumpus World Problem3.8 Problem Formulation of Missionaries and Carnivals Problem3.9 Production System3.10 Difference between Conventional Problems and AI Problems3.11 Searching3.12 Problem Characteristics and Issues in the Design of Search Programs3.13 Solving Problems by Searching3.14 Types of Search Strategies 4 Uninformed Search Strategies4.1 Introduction4.2 Brute Force or Blind Search4.3 Breadth-First Search4.4 Depth-First Search4.5 Difference between BFS and DFS4.6 Uniform Cost Search4.7 Depth-Limited Search4.8 Iterative Deeping DFS4.9 Bidirectional Search4.10 Comparing Uniform Search Strategies 5 Informed Search5.1 Introduction5.2 Hill Climbing5.3 Best-First Search (Greedy Search)5.4 A* Search5.5 AO* Search: (AND–OR) Graph5.6 Memory Bounded Heuristic Search5.7 Simulated Annealing Search5.8 Local Beam Search5.9 Branch and Bound Search 6 Adversarial Search6.1 Introduction6.2 Optimal Strategies6.3 The Minimax Algorithm6.4 Alpha–Beta Pruning 7 Constraint Satisfaction Problem7.1 Introduction7.2 General Form of the CSP7.3 Map Colouring Problem7.4 N-Queens Problem7.5 N-Queens Problem Formulation7.6 Forward Checking7.7 Crypto Arithmetic Problem 8 Knowledge and Reasoning8.1 A Knowledge-Based Agent8.2 The Wumpus World8.3 Knowledge Representation Issues 9 Predicate Logic9.1 Representation of Simple Fact in Logic9.2 Representing Instance and Is_A Relationship in Predicate Logic9.3 Computable Functions and Predicate Logic9.4 Resolution9.5 Knowledge Engineering in First-Order Logic9.6 Unification9.7 Natural Deduction 10 Representation Knowledge Using Rules10.1 Propositional Logic10.2 Frist-Order Logic/Predicate Logic10.3 Inference in First-Order Logic10.4 Procedural versus Declarative Knowledge10.5 Logic Programming10.6 Forward and Backward Reasoning10.7 Matching10.8 Control Knowledge10.9 Forward and Backward Chaining (Type of Reasoning) 11 Planning and Learning11.1 Introduction11.2 The Language of Planning Problems11.3 Planning with State Space Search11.4 Partial Ordered Planning11.5 Hierarchical Planning11.6 Conditional Planning11.7 Learning Introduction11.8 Forms of Learning11.9 Inductive Learning11.10 Learning Decision Trees11.11 Ensemble Learning11.12 Reinforcement Learning 12 Uncertain Knowledge and Reasoning12.1 Uncertainty12.2 Basic Probability Theorem12.3 Joint Probability12.4 Baye’s Theorem12.5 Representing Knowledge in an Uncertain Domain (Bayesian Belief Network)12.6 Simple Inference in Belief Network12.7 Temporal Model12.8 Markov Decision Process 13 Natural Language Processing13.1 Introduction13.2 Exponential13.3 Natural Language for Communication13.4 Syntactic Analysis13.5 Argumented Grammar13.6 Semantic Interpretation 14 Expert System14.1 Expert System14.2 Need and Justification of ES14.3 Knowledge Representation14.4 Knowledge Acquisition and Variation14.5 Utilisation and Functionality14.6 Basics of Prolog 15 Application15.1 Introduction15.2 Category of Applications of AI15.3 Robotics15.4 Artificial Neural Network15.5 AI Trends in Various Sectors15.6 More About Agents of AI 16 Cognitive Computing16.1 Introduction16.2 Foundation of Cognitive Computing16.3 List of Design Principles for Cognitive Systems16.4 Natural Language Processing in Support of a Cognitive System 17 Introduction to Soft Computing and Fuzzy Logic17.1 Introduction17.2 Soft Computing versus Hard Computing17.3 Various Types of Soft and Hard Computing Techniques17.4 Fuzzy Logic17.5 Fuzzy Set versus Crisp Set17.6 Membership Function17.7 Fuzzy Rules17.8 Fuzzy Reasoning17.9 Fuzzy Inference System17.10 Fuzzification17.11 Defuzzification17.12 Fuzzy Controllers 18 Artificial Neural Network18.1 Introduction to Artificial Neural Networks18.2 Basic Models of Artificial Neural Networks18.3 First Artificial Neurons: McCulloch–Pitts Model18.4 Neural Network Architecture18.5 Single-Layer Feedforward ANN18.6 Multilayer Feedforward ANN18.7 Activation Functions18.8 Supervised Learning18.9 Delta Learning Rule18.10 Backpropagation Algorithm18.11 Unsupervised Learning Algorithm18.12 Self-Organising Maps18.13 Hybrid Approach: Fuzzy Neural Systems SummaryReview QuestionsShort-Type QuestionsMultiple-Choice QuestionsAnswers Further ReadingsIndex