MCA

Type: Compulsory
Contact Hours: 4 hours/week
Examination Duration: 3 Hours
Mode: Lecture
External Maximum Marks: 75
External Pass Marks: 30(i.e. 40%)
Internal Maximum Marks: 25
Total Maximum Marks: 100
Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester exam:
Total number of questions shall be nine. Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to provide the in-depth coverage of Artificial Intelligence techniques and their applications. It focuses on various search techniques and expert systems along with other parts of artificial intelligence in computer science.

Course Outcomes (COs)  At the end of this course, the student will be able to:
MCA-20-33.1                         understand the different knowledge representation schemes specially FOPL;
MCA-20-33.2                        apply various search methods to solve AI problems efficiently;
MCA-20-33.3                       understand the Expert System and techniques to manage the uncertainty in Expert Systems;
MCA-20-33.4 understand the learning techniques and Genetic Algorithm.
                                                                                              Unit – I
Introduction: Background and history, Overview of AI applications areas.
The predicate calculus: Syntax and semantic for propositional logic and FOPL, Clausal form, inference rules, resolution and unification.
Knowledge representation: Network representation-Associative network & conceptual graphs, Structured representation- Frames & Scripts.
                                                                                            Unit – II
Search strategies: Strategies for state space search-data driven and goal driven search; Search algorithms- uninformed search (depth first, breadth first, depth first with iterative deepening) and informed search (Hill climbing, best first, A* algorithm, mini-max etc.), computational complexity, Properties of search algorithms – Admissibility, Monotonicity, Optimality, Dominance.
                                                                                            Unit – III
Production system: Types of production system-commutative and non-commutative production systems, Decomposable and non-decomposable production systems, Control of search in production systems.
Rule based expert systems: Architecture, development, managing uncertainty in expert systems – Bayesian probability theory, Stanford certainty factor algebra, Nonmonotonic logic and reasoning with beliefs, Fuzzy logic, Dempster/Shaffer and other approaches to uncertainty.
                                                                                             Unit – IV
Knowledge acquisition: Types of learning, learning by automata, intelligent editors, learning by induction.
Genetic algorithms: Problem representation, Encoding Schemes, Operators: Selection, Crossover, Mutation, Replacement etc.

Text Books:
⦁ George F. Luger, Artificial Intelligence, Pearson Education.
⦁ Dan W. Patterson Introduction to Artificial Intelligence and Expert system, PHI.
Reference Books:
⦁ Ben Coppin, Artificial Intelligence Illuminated, Narosa Publishing House.
⦁ Eugene Charniak, Drew McDermott Introduction to Artificial Intelligence, Pearson Education.
⦁ Nils J. Nilsson Principles of Artificial Intelligence, Narosa Publishing House.
⦁ Jackson Peter, Introduction to Expert systems, Pearson-Education.