ARTIFICIAL INTELLIGENCE

Paper Code: 
MCA 324C
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

Max. Marks: 100.00

 

Course Objectives:

The course enables the students to

  1. Define general artificial intelligence Techniques.
  2. Understand the specific ideas about modeling and analytical skills (e.g., search, logic, and probability) knowledge of many of the most important knowledge representation, reasoning, and machine learning schemes, and a general understanding of AI principles and practice.
  3. Understand the role of semantics of sentences and pragmatics.
  4. Demonstrate work involving the design of computer programs for various application domains.
  5. Compare different search strategies in AI.

6.     Evaluate how AI can be applied to different problems and how Expert system helps in real life.

Course Outcomes(COs):

 

Learning Outcome (at course level)

 

Learning and teaching strategies

Assessment Strategies

 
 

CO153.        Define various problem solving    technique and various control strategies.

CO154.        Classify various search algorithms and also explains their applications for real world problems.

CO155.        Demonstrate use of knowledge representation technique like semantic networks, Frame system, Script etc.

CO156.        Discuss various AI Fields like Natural Language Processing, Probability, Expert System.

CO157.        Evaluate expert system and use of expert system application in the real world.

CO158.        Develop an idea about various Applications of AI.

 

Interactive Lectures,

Modeling, Discussions, Using research papers, student centered approach, Through Video Tutorials

 

Learning activities for the students:

Experiential Learning, Presentations, case based learning, Discussions, Quizzes and Assignments

 

 

·  Assignments

·  Assignments

·  Written test in classroom

·  Classroom activity

·  Continues Assessment

·  Semester End Examination

 
 

 

 

 

 

12.00
Unit I: 
General Issues and overview of AI

General Issues and overview of AI:

The AI problems: what is an AI technique, Characteristics of AI applications Problem Solving, Search and Control Strategies General Problem solving, Production systems, Control strategies, forward and backward chaining Exhaustive searches: Depth first Breadth first search.

12.00
Unit II: 
Heuristic Search Techniques

Heuristic Search Techniques:

Hill climbing, Branch and Bound technique, Best first search and A* algorithm, AND/OR Graphs, Problem reduction and AO* algorithm, Constraint Satisfaction problems Game Playing Min Max Search procedure, Alpha-Beta cutoff, Additional Refinements.

12.00
Unit III: 
Knowledge Representation

Knowledge Representation:

First Order Predicate Calculus, Resolution Principle and Unification, Inference Mechanisms Horn’s Clauses, Semantic Networks, Frame Systems and Value Inheritance, Scripts, Conceptual Dependency AI Programming Languages Introduction to LISP, Introduction to PROLOG.

12.00

Natural Language Processing:

Origins and challenges of NLP – Language Modeling: Grammar-based LM, Statistical LM – Regular Expressions, Finite-State Automata – English Morphology, Tokenization, Unsmoothed N-grams, Evaluating N-grams,  Smoothing, Part-of-Speech Tagging, Issues in Part-of-Speech tagging.

Semantics and pragmatics-Requirements for representation, Syntax-Driven Semantic analysis, Semantic attachment-Word Senses, Relations between Senses.

Syntactic analysis: Context-Free Grammars, Grammar rules for English, Normal Forms for grammar – Dependency Grammar – Syntactic Parsing, and Ambiguity.

12.00
Unit V: 
Probability and Expert Systems

Probability and Expert Systems:

Probabilistic Reasoning and Uncertainty, Probability theory, Bayes Theorem and Bayesian networks, Certainty Factor.

Introduction to Expert Systems, Architecture of Expert Systems, Expert System Shells, Knowledge Acquisition, Case Studies, MYCIN, Learning, Rote Learning, Learning by Induction, explanation based learning.

ESSENTIAL READINGS: 

  • Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill, 3rd edition, 2009.
  • Dan W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Prentice Hall of India, 1st edition, 2015.
  • Winston, Patrick, Henry, “Artificial Intelligence”, Pearson Education, 3rd edition, 2004
  • Subhasree Bhattacharjee, “Artificial Intelligence for Student” Shroff Publishers and Distributors Pvt.LTD., 1st  Edition, 2016
  • Daniel Jurafsky, James H. Martin Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech, Pearson Publication, 2014.
  • Steven Bird, Ewan Klein and Edward Loper, Natural Language Processing with Pythonll, Third Edition, OReilly Media, 2020.

REFERENCES: 

  • Nils J. Nilsson, “Principles of Artificial Intelligence (Symbolic Computation / Artificial Intelligence)”, reprint edition, 2014.

  • Stuart Russell, Peter Norving, “Artificial Intelligence: A Modern Approach”, Pearson Education, 3rd edition, 2010.

Academic Year: 
2022-23
  • Printer-friendly version