Artificial Intelligence

ARTIFICIAL INTELLIGENCE

 

Course Details

Course Title:  ARTIFICIAL INTELLIGENCE

Course Code

MSCSC3001C04

Credits

4

L + T + P

3 + 1 + 0

Course Duration

One Semester

Semester

Odd

Contact Hours

45 (L) + 15 (T) Hours

Methods of Content Interaction

Lecture, Tutorials, Group discussion; self-study, seminar, presentations by students, individual and group drills, group and individual field based assignments followed by workshops and seminar presentation.

Assessment and Evaluation

·         30% - Continuous Internal Assessment (Formative in nature but also contributing to the final grades)

·         70% - End Term External Examination (University Examination)

 

Course Objectives

·         To understand basics of AI.

·         To understand how to set computational goals an achieving strategies.

·         To understand computational development based on neutral system.

·         To understand computational development based on Genetic Algorithm.

Learning Outcomes

After completion of the course the learners will be able to:

•         Understand various search methods.

•         Use various knowledge representation methods.

•         Understand various Game Playing techniques.

•         Understand neural based computation.

•         Understand genetic algorithm based computation.

 

 

Course Contents

UNIT I: Introduction to AI:                                                                             (12% Weightage)

Definitions, Goals of AI, AI Approaches, AI Techniques, Branches of AI, Applications of AI. 

 

UNIT II: Problem Solving, Search and Control Strategies :                        (22% Weightage)

AI Problem Solving: Problem solving as state space search, production system, control strategies and problem characteristics; Search techniques: Breadth First and Depth-first, Hill-climbing, Heuristics, Best-First Search, A* algorithm, Problem reduction and AO* algorithm, Constraints satisfaction problems,

 

UNIT III: Knowledge Representation, Reasoning and Game Playing        (22 % Weightage)

Knowledge Representation Issues,  Predicate Logic, Rules : Knowledge representation, KR using predicate logic, KR using rules .

Reasoning  System  - Symbolic, Statistical :  Reasoning ,  Symbolic reasoning, Statistical reasoning.

Game Playing :  Overview, Mini-Max search procedure, Game playing with Mini-Max, Alpha-Beta  pruning.

 

UNIT IV: Learning and Expert System                                                           (22%Weightage)

Learning :  What is learning, Rote learning, Learning from example : Induction, Explanation Based Learning (EBL), Discovery, Clustering , Analogy, Neural net and genetic learning, Reinforcement learning.

Expert System :  Introduction, Knowledge acquisition, Knowledge base, Working memory, Inference engine, Expert system shells, Explanation, Application of expert systems.

 

UNIT V:  Neural Network, Genetic Algorithm & NLP                                 (22% Weightage)

Fundamentals of Neural Networks :  Introduction and research history, Model of artificial neuron, neural network Characteristics, Learning methods, Single-layer network system,  Applications.

Fundamentals of Genetic Algorithms :  Introduction,  Encoding, Operators of genetic algorithm,  Basic genetic algorithm

Natural Language Processing : Introduction,  Syntactic processing, Semantic and pragmatic analysis .

Content Interaction Plan:

Lecture cum Discussion (Each session of 1 Hour)

 

Unit/Topic/Sub-Topic

1-4

Definitions, Goals of AI, AI Approaches, AI Techniques, Branches of AI, Applications of AI. 

5-8

AI Problem Solving: Problem solving as state space search, production system, control strategies and problem characteristics;

9-14

Search techniques: Breadth First and Depth-first, Hill-climbing, Heuristics, Best-First Search, A* algorithm, Problem reduction and AO* algorithm, Constraints satisfaction problems,

15-17

Knowledge Representation Issues, Predicate Logic, Rules : Knowledge representation, KR using predicate logic, KR using rules .

18-20

Reasoning System - Symbolic, Statistical:  Reasoning ,  Symbolic reasoning, Statistical reasoning.

21-24

Game Playing :  Overview, Mini-Max search procedure, Game playing with Mini-Max, Alpha-Beta  pruning

25-30

Learning :  What is learning, Rote learning, Learning from example : Induction, Explanation Based Learning (EBL), Discovery, Clustering , Analogy, Neural net and genetic learning, Reinforcement learning.

31-34

Expert System :  Introduction, Knowledge acquisition, Knowledge base, Working memory, Inference engine, Expert system shells, Explanation, Application of expert systems.

35-39

Fundamentals of Neural Networks :  Introduction and research history, Model of artificial neuron, neural network Characteristics, Learning methods, Single-layer network system,  Applications..

40-42

Fundamentals of Genetic Algorithms :  Introduction,  Encoding, Operators of genetic algorithm,  Basic genetic algorithm

43-45

Natural Language Processing : Introduction,  Syntactic processing, Semantic and pragmatic analysis

15 Hours

Tutorials

•          Suggested References:

•           E. Rich and K. Knight, Artificial Intelligence, Tata McGraw Hill.

•           S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Pearson Education.

•          N.J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann.

•          Introduction to Artificial Intelligence by Philip C Jackson

 

•         "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter  Norvig, (2002), Prentice Hall, Chapter 1-27,  page 1-1057.

•         "Artificial Intelligence: Structures and Strategies for Complex Problem Solving",        by George F. Luger, (2002), Addison-Wesley,  Chapter 1- 16, page 1-743.

•         "AI: A New Synthesis", by Nils J. Nilsson, (1998), Morgan Kaufmann Inc., Chapter 1-25, Page 1-493.

•         "Artificial Intelligence: Theory and Practice", by Thomas Dean, (1994), Addison Wesley, Chapter 1-10, Page 1-650.

•         "Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications",  by S. Rajasekaran and G.A. VijayalaksmiPai,  (2005), Prentice Hall, Chapter 1-15, page 1-435.

•          "Computational Intelligence: A Logical Approach", by David Poole, Alan Mackworth, and Randy Goebel, (1998), Oxford University Press, Chapter 1-12, page 1-608.