Structure
1. Introduction to Artificial Intelligence
2. Problem Solving,
Search and Control Strategies
3. Problem Reduction and Game playing
4. Logic Concepts and
Prolog programming
5. Advanced Problem
Solving – Planning
6. Knowledge
Representation Techniques
7. Expert Systems and
Applications
8. Handling
Uncertainty
9. Machine Learning
10. Evolutionary Computation
11. Intelligent Agents
12. Natural Language
Processing
1. Introduction
to Artificial Intelligence 2
1.1. General Issues and overview of AI
1.2. Intelligent Systems
1.3. Foundations of AI
1.4. Characteristics of AI
systems
1.5. Sub-areas of AI
1.6. Applications
2.
Problem Solving,
Search and Control Strategies 5
2.1. General Problem Solving
2.1.1. Production System
2.1.2. State Space Search
2.1.3. Control Strategies
2.2. Exhaustive Searches
2.2.1.
Breadth First Search
2.2.2.
Depth-First Search
2.2.3.
Depth First Iterative Deepening
2.2.4.
Bi-Directional Search
2.2.5.
Analysis of Search methods
2.3. Heuristic Search Techniques
2.3.1.
General Purpose Heuristics
2.3.2.
Branch and Bound Search (Uniform Cost Search)
2.3.3.
Hill Climbing
2.3.4.
Beam Search
2.3.5.
Best First Search
2.3.6.
A* Algorithm
2.3.7.
Optimal solution by A* Algorithm
2.4. Iterative–Deepening A*
2.5. Constrained satisfaction
3.
Problem Reduction and Game playing 3
3.1. Problem Reduction
3.2. Game Playing
3.2.1.
Game Problem verses State Space Problem
3.2.2.
Status Labeling Procedure
3.3. Bounded Look-ahead and use of
Evaluation Functions
3.3.1.
Using Evaluation Function
3.3.2.
MINIMAX Procedure
3.4. Alpha-Beta Pruning
3.4.1.
Refinements to a-b pruning
3.4.2.
Alternative to a-b pruning MINMAX
3.4.3.
Iterative Deepening
3.4.4.
Two Player Perfect Information Games
4.
Logic Concepts and
Prolog programming 6
4.1. Propositional Calculus
4.1.1.
Truth table
4.1.2.
Equivalence Laws
4.2. Propositional Logic
4.2.1.
Resolution Refutation in PL
4.2.2.
Conversion of a formula into set of clauses
4.2.3.
Resolvent of Clauses
4.3. Predicate Logic
4.3.1.
Predicate Calculus
4.3.2.
Transformation of Formula into Prenex Normal Form
4.3.3.
Conversion of Formula into PNF notation
4.3.4.
Prenex Normal Form and Skolemization
4.3.5. Resolution Refutation
in Predicate Logic
4.4. PROLOG Programming
4.4.1.
General Syntax and Prolog Control Strategy
4.4.2.
Execution of a Prolog Query
4.5. Programming Techniques in Prolog
4.5.1.
Recursive Programming
4.5.2.
Iterative Programming
4.6. Lists Manipulation
4.7. Redundancy and Termination Issues
4.8. Effect of Rule and Goal orders
4.9. Cut, Fail predicates
5.
Advanced Problem
Solving - Planning 3
5.1. Types of Planning Systems
5.1.1.
Operator Based Planning
5.1.2.
Planning Algorithms
5.1.3.
Case Based Planning
5.1.4.
State Space Linear Planning
5.2. Block World Problem Description
5.3. Logic Based Planning
5.4. Linear Planning Using a Goal Stack
5.5. Means-Ends Analysis (MEA)
5.6. Nonlinear Planning Strategies
5.6.1.
Goal Set Method
5.6.2.
Partial Ordering Planning
6.
Knowledge
Representation Techniques 4
6.1. Approaches to Knowledge Representation
6.2. KR using Semantic Network
6.2.1. Inheritance in
Semantic Net
6.3. Knowledge Representation using Frames
6.3.1. Inheritance in Frames
6.3.2. Implementation of
Frame Knowledge
6.3.3. Representation of
Frames in Prolog
6.4. Conceptual Dependency
6.4.1. Conceptual Primitive
Actions
6.4.2. Conceptual category
6.4.3. Rules for
Conceptualization blocks in CD
6.4.4. Generation of CD representation
6.4.5. Conceptual Parsing
6.4.6. Inferences Associated with Primitive Act
7.
Expert Systems and
Applications 3
7.1. Phases in building ES
7.1.1. Knowledge Engineering
7.1.2. Knowledge Representation
7.1.3. Characteristics of ES
7.2. Expert System Architecture
7.2.1. Knowledge base
7.2.2. Inference Engine
7.2.3. Knowledge acquisition
7.2.4. User interfaces
7.2.5. Explanation module
7.3.1. Expert System Shell in Prolog
7.3.2. Problem Independent Forward Chaining
8.
Handling
Uncertainty
5
8.1. Introduction
8.2. Probabilistic Reasoning and Uncertainty
8.2.1. Probability theory
8.2.2. Bayes’ Theorem
8.2.3. Extensions of Bayes'
Theorem
8.2.4. Probabilities in
Rules and Facts of Production System
8.3. Bayesian Belief Network
8.3.1. Formal Definition
of Bayesian Belief Network
8.3.2. Inference using
Bayesian Network
8.3.3. Example of Simple
Bayesian network
8.3.4. Structure Learning
8.3.5. Advantages and
Disadvantages of Bayesian Network
8.4. Certainty Factor
8.5. Dempster-Shafer (D-S) theory
8.5.1. Dempster Theory Formalism
8.5.2. Dempster's rule of combination
8.6. Fuzzy sets and Fuzzy Logic
8.6.1. Fuzzy Sets
8.6.2. Various fuzzy set
operations
8.6.3. Various Types of
Membership Functions
8.6.4. Methods for
Determining Membership Functions
8.6.5. µ-cut and
Representation of Fuzzy Set
8.6.6. Multi-Valued Logic
8.6.7. Fuzzy logic
8.6.8. Linguistic Variables
and Hedges
8.6.9. Fuzzy propositions
8.6.10. Inference Rules for
Fuzzy Propositions
8.6.11. Fuzzy Systems and
Neuro Fuzzy system
9. Machine
Learning 4
9.1. Concept of Learning
9.2. Component of Learning System
9.3. Major Learning Paradigm
9.3.1.
Inductive
Learning
9.3.2.
Example
Based Learning
9.3.3.
Supervised
Learning
9.3.4.
Unsupervised
Learning
9.3.5.
Deductive
Learning
9.4.
Neural
Networks
9.4.1.
Perceptrons
9.4.2.
Multilayer
Feedforward Networks
9.4.3.
Back
Propagation Algorithm
9.4.4.
Recurrent
Network
9.4.5.
Hopfield
Network
10. Evolutionary
Computation 2
10.1.
Genetic
Algorithm
10.1.1.
Biological
evolutionary process
10.1.2.
Search
Space
10.1.3.
Description
of Genetic Algorithm
10.1.4.
Operators
of GA
10.2.
Applications
of GA
10.3.
Genetic
programming Concepts
10.3.1.
Genetic
programming
10.4.
Evolutionary
programming
10.5.
Introduction
to Swarm intelligence
11. Intelligent Agents 2
11.1. Properties of
Agents
11.2. Agent
Classifications
11.3. Agent Architecture
11.4. Learning Agents
11.5. Multi Agents
System
12. Natural Language Processing 3
12.1. Introduction
12.2. Parsing techniques
12.3. Context-free
grammar
12.4. Recursive
Transitions Nets (RTN)
12.5. Augmented
Transition Nets (ATN)
12.6. Case Grammar
12.7. Definite Clause
Grammar (Logic grammar)
1. Elaine Rich and
Kevin Knight, “Artificial Intelligence”, Tata
McGraw-Hills, Reprint 2003.
2. S Russell and Peter Norvig,
Artificial Intelligence – A Modern Approach, Pearson Education, Reprint 2003.
3. Saroj Kaushik,
“Logic and Prolog Programming” , New Age International Ltd, publisher, 2007.
1.
N J. Nilsson, “Artificial Intelligence: A New Approach”,
Morgan Kaufmann, Reprint 2003
2. Dan W. Patterson, “Introduction to
Artificial Intelligence and Expert Systems”, Prentice all of
3.
L. Sterling & E. Shapiro, “Art of Prolog, Advanced Programming
Techniques, Prentice Hall of India, reprint 1996.