17MCA543 Artificial Intelligence syllabus for MCA

What is Artificial Intelligence:

The AI Problems, The Underlying assumption, What is an AI Technique?, The Level of the model, Criteria for success, some general references, One final word and beyond. Problems, problem spaces, and search: Defining, the problem as a state space search, Production systems, Problem characteristics, Production system characteristics,

Heuristic search techniques:

Generate-and-test, Hill climbing, Best-first search, Problem reduction, Constraint satisfaction, Mean-ends analysis. Knowledge representation issues: Representations and mappings, Approaches to knowledge representation, Issues in knowledge representation, The frame problem.

Using predicate logic:

Representing simple facts in logic, representing instance and ISA relationships, Computable functions and predicates, Resolution, Natural Deduction

Symbolic Reasoning Under Uncertainty:

Introduction to non-monotonic reasoning, Logic for non-monotonic reasoning

Implementation:

Depth-first search, Implementation: Breadth-first search. Statistical Reasoning: Probability and Bayes Theorem, Certainty factors and rule-based systems, Bayesian Networks, Fuzzy logic.

Weak Slot-and-filter structures:

Semantic Nets Frames, Strong slot-and –filler structures: Conceptual dependency, scripts, CYC.

Course Outcomes (CO):

After studying this course, students will be able to:

CO1: Acquire knowledge of - Uncertainty and Problem solving techniques - Symbolic knowledge representation to specify domains - Reasoning tasks of a situated software agent

CO2: Comprehend on - different logical systems for inference over formal domain representations - trace on particular inference algorithm working on a given problem specification

CO3: Apply and Analyse AI technique to any given concrete problem

CO4: Interpret and Implement non-trivial AI techniques in a relatively large system

Question paper pattern:

• The question paper will have ten questions.
• Each full question consists of 16 marks.
• There will be 2full questions (with a maximum of four sub questions) from each module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from each module.

Text Books:

1. Elaine Rich, Kevin Knight, Shivashankar B Nair: Artificial Intelligence, Tata McGraw Hill 3rd edition. 2013

Reference Books:

1. Stuart Russel, Peter Norvig: Artificial Intelligence A Modern Approach, Pearson 3rd edition 2013.

2. Nils J. Nilsson: “Principles of Artificial Intelligence”, Elsevier, ISBN-13: 9780934613101