MTRA-101	INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Lecture	Tutorial	Practical	Credit	Major Test	Minor Test	Total	Time
3	0	0	3	60	40	100	3 Hrs.
Program Objective (PO)	Familiar with basic principles of AI, capable of using heuristic searches, aware of knowledge based systems, Able to use fuzzy logic and neural networks, Learn various applications domains AI
Course Outcomes (CO)
CO1	Understand the basics of the theory and practice of Artificial Intelligence as a discipline and about intelligent agents capable of problem formulation.
CO2	Evaluation of different uninformed search algorithms on well formulate problems alongwith stating valid conclusions that the evaluation supports
CO3	Design and Analysis of informed search algorithms on well formulated problems. Formulate and solve given problem using Propositional and First order logic.
CO4	Apply planning and neural network learning for solving AI problems. Apply reasoning for non-monotonic AI problems.

##### UNIT-1

Introduction, A.I. Representation, Non-AI &AI Techniques, Representation of Knowledge, KnowledgeBase Systems, State Space Search, Production Systems, ProblemCharacteristics, types of production systems, Intelligent Agents and Environments, concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation Formulation of real world problems, Breadth First Search, Depth First Search, Depth Limited Search, Iterative Deepening Depth First Search, Bidirectional Search, Comparison of Uninformed search Strategies, Searching with partial information, Sensor-less problems, Contingency problems.

##### UNIT-2

Generate& test, Hill Climbing, Best First Search, A* and AO* Algorithm, Constraint satisfaction, Game playing: Minimax Search, Alpha-Beta Cutoffs, Waiting for Quiescence

##### UNIT-3

Knowledge based agents, Wumpus world. Propositional Logic: Representation, Inference, Reasoning Patterns, Resolution, Forward and Backward Chaining.First order Logic: Representation, Inference, Reasoning Patterns, Resolution, Forwardand Backward Chaining. Basics of PROLOG: Representation, Structure, Backtracking. Expert System: Case study of Expert System in PROLOG

##### UNIT-4

Blocks world, STRIPS, Implementation using goal stack, Introduction to Neural networks:- basic, comparison of human brain and machine, biological neuron, general neuron model, activation functions, Perceptron learning rule, applications and advantages of neural networks. Brief introduction to single layer and multiplayer networks. Non Monotonic Reasoning, Logics for Non Monotonic Reasoning, Justification based Truth Maintenance Systems, Semantic Nets, Statistical Reasoning, Fuzzy logic: fuzzy set definition and types, membership function, designing a fuzzy set for a given application. Probability and Bayes' theorem, Bayesian Networks.

### Books and References :

- 1. Elaine Rich and Kevin Knight: "Artificial Intelligence." Tata McGraw Hill.
- 2. Stuart Russell & Peter Norvig : "Artificial Intelligence : A ModernApproach",Pearson Education, 2nd Edition.
- 3. Ivan Bratko : "Prolog Programming For Artificial Intelligence" , 2nd Edition AddisonWesley, 1440.
- 4. Eugene, Charniak, Drew Mcdermott: "Introduction to Artificial Intelligence.", AddisonWesley
- 5. Patterson: -Introduction to AI and Expert Systems‖, PHI
- 6. Nilsson : -Principles of Artificial Intelligence‖, Morgan Kaufmann.
- 7. Carl Townsend, -Introduction to turbo Prolog‖, Paperback, 1483
- 8. Jacek M. Zurada, Introduction to artificial neural systems, Jaico Publication

Note for paper setter for paper setter : Eight question will be set in all. Two questions from each unit. The candidate will be required to attempt four question in all and selecting one question from each unit.