UNIVERSITY OF KERALA - Marian Engineering College

B.Tech Comp. Sc. & Engg., University of Kerala 84
08.806 (1) ARTIFICIAL INTELLIGENCE (ELECTIVE IV) 3 – 1 – 0
Module I (16 hours)
Artificial Intelligence: History and Applications, Production Systems, Structures and Strategies for state space
search- Data driven and goal driven search, Depth First and Breadth First Search, DFS with Iterative
Deepening, Heuristic Search- Best First Search, A* Algorithm, AO* Algorithm, Constraint Satisfaction, Using
heuristics in games- Minmax Search, Alpha Beta Procedure.
Module II (18 hours)
Knowledge representation - Propositional calculus, Predicate Calculus, Theorem proving by Resolution,
Answer Extraction, AI Representational Schemes- Semantic Nets, Conceptual Dependency, Scripts, Frames,
Introduction to Agent based problem solving. Machine Learning- Symbol based - A frame work for Symbol
based Learning, Vision space search, Inductive Bias and learnability, Knowledge and learning, Connectionist
– Foundation for connectionist Networks, Perceptron Learning, Back propagation learning. Social and
Emergent models of learning – Genetic algorithm, Classifier Systems and Genetic Programming.
Module III (18 hours)
Overview of Expert System Technology- Rule based Expert Systems, Natural Language Processing- Natural
Language understanding problem, Deconstructing Language, Syntax, Stochastic tools for Language analysis,
Natural Language applications- Story Understanding and Question answering, An information Extraction and
Summarization System for the Web.
Language and Programming Techniques for AI- Introduction to PROLOG, Syntax for predicate calculus
programming, Abstract Data Types in PROLOG, A production system example in PROLOG, Meta-
Predicates, Types and Unification, Meta-Interpreters, Learning algorithms in PROLOG, Natural Language
processing in PROLOG.
Text Books:
1. Artificial Intelligence: Structures and Strategies for Complex Problem Solving – G. F. Luger, Pearson
Education.
Reference Books:
1. Introduction to Artificial Intelligence and Expert Systems – D. W. Patterson, PHI.
2. Artificial Intelligence, 2 nd Edn – E. Rich, K.Knight, Tata McGraw Hill.
3. Artificial Intelligence: A New Synthesis – N. J. Nilsson, Elsevier.
Internal Continuous Assessment (Maximum Marks-50)
25 Marks - Tests (minimum 2)
15 Marks - Assignments (minimum 3) such as home work, problem solving, literature survey, seminar,
term-project, software exercises, etc.
10 Marks - Regularity in the class
University Examination Pattern
PART A: Short answer questions 10 x 4 marks=40 marks
All questions are compulsory. There should be at least three questions
from each module and not more than four questions from any module.
PART B: Descriptive/Analytical/Problem solving questions 3 x 20 marks=60 marks
Candidates have to answer one question out of two or two questions
out of four from each module
Maximum Total Marks: 100