Course Title: Introduction to Artificial Intelligence
Course no: CSC-355 Full Marks: 72
Credit hours: 3 Pass Marks: 28
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: This course introduces the problem solving techniques, problem
representation and machine learning.
Goal: The main objective of this course is to provide basic knowledge of Artificial
Intelligence, with acquaintance of different search techniques and AI applications.
Course Contents:
Unit 1. Introduction to Artificial Intelligence 4 Hrs.
Artificial Intelligence and related fields, brief history of AI, applications of Artificial
Intelligence, Definition and importance of Knowledge, and Learning.
Unit 2. Problem Solving 6 Hrs.
Problem Definition, Problem as a state space search, Problem formulation, Problem
types, Well-defined problems, Constraint satisfaction problem, Game playing,
Production systems.
Unit 3. Search Techniques 9 Hrs.
Uninformed search techniques- depth first search, breadth first search, depth limit
search, and search strategy comparison, Informed search techniques-hill climbing,
best first search, greedy search, A* search, Adversarial search techniques-minimax
procedure, alpha beta procedure
Unit 4. Knowledge Representation, Inference and Reasoning 12 Hrs.
Formal logic-connectives, truth tables, syntax, semantics, tautology, validity, wellformed-formula,
propositional logic, predicate logic, FOPL, interpretation,
quantification, horn clauses, rules of inference, unification, resolution refutation
system (RRS), answer extraction from RRS, rule based deduction system, Statistical
Reasoning-Probability and Bayes' theorem and causal networks, reasoning in belief
network
Unit 5. Structured Knowledge Representation 4 Hrs.
Representations and Mappings, Approaches to Knowledge Representation, Issues in
Knowledge Representation, Semantic nets, frames, conceptual dependencies and
scripts
Unit 6. Machine Learning 4 Hrs.
Concepts of learning, learning from examples, explanation based learning, learning
by analogy, learning by simulating evolution, learning by training neural nets,
learning by training perceptrons.
Unit 7. Applications of Artificial Intelligence 6 Hrs.
Expert Systems, Neural Network, Natural Language Processing, Machine Vision
Laboratory work: Laboratory exercises should be conducted in either LISP or
PROLOG. Laboratory exercises must cover the fundamental
search techniques, simple question answering, inference and
reasoning.
Text / Reference books:
E. Rich and Knight, Artificial Intelligence, McGraw Hill.
D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
P. H. Winston, Artificial Intelligence, Addison Wesley.
Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson
Ivan Bratko, PROLOG Programming for Artificial Intelligence, Addison Wesley.
Course no: CSC-355 Full Marks: 72
Credit hours: 3 Pass Marks: 28
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: This course introduces the problem solving techniques, problem
representation and machine learning.
Goal: The main objective of this course is to provide basic knowledge of Artificial
Intelligence, with acquaintance of different search techniques and AI applications.
Course Contents:
Unit 1. Introduction to Artificial Intelligence 4 Hrs.
Artificial Intelligence and related fields, brief history of AI, applications of Artificial
Intelligence, Definition and importance of Knowledge, and Learning.
Unit 2. Problem Solving 6 Hrs.
Problem Definition, Problem as a state space search, Problem formulation, Problem
types, Well-defined problems, Constraint satisfaction problem, Game playing,
Production systems.
Unit 3. Search Techniques 9 Hrs.
Uninformed search techniques- depth first search, breadth first search, depth limit
search, and search strategy comparison, Informed search techniques-hill climbing,
best first search, greedy search, A* search, Adversarial search techniques-minimax
procedure, alpha beta procedure
Unit 4. Knowledge Representation, Inference and Reasoning 12 Hrs.
Formal logic-connectives, truth tables, syntax, semantics, tautology, validity, wellformed-formula,
propositional logic, predicate logic, FOPL, interpretation,
quantification, horn clauses, rules of inference, unification, resolution refutation
system (RRS), answer extraction from RRS, rule based deduction system, Statistical
Reasoning-Probability and Bayes' theorem and causal networks, reasoning in belief
network
Unit 5. Structured Knowledge Representation 4 Hrs.
Representations and Mappings, Approaches to Knowledge Representation, Issues in
Knowledge Representation, Semantic nets, frames, conceptual dependencies and
scripts
Unit 6. Machine Learning 4 Hrs.
Concepts of learning, learning from examples, explanation based learning, learning
by analogy, learning by simulating evolution, learning by training neural nets,
learning by training perceptrons.
Unit 7. Applications of Artificial Intelligence 6 Hrs.
Expert Systems, Neural Network, Natural Language Processing, Machine Vision
Laboratory work: Laboratory exercises should be conducted in either LISP or
PROLOG. Laboratory exercises must cover the fundamental
search techniques, simple question answering, inference and
reasoning.
Text / Reference books:
E. Rich and Knight, Artificial Intelligence, McGraw Hill.
D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
P. H. Winston, Artificial Intelligence, Addison Wesley.
Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson
Ivan Bratko, PROLOG Programming for Artificial Intelligence, Addison Wesley.
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