ARTIFICIAL INTELLIGENCE

Paper Code: 
DCAI 701A
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

This course will help students to

  1. Explore the concepts of artificial intelligence, expert systems and their applications.
  2. Apply problem solving techniques in AI.

Learning Outcome

Learning and Teaching Strategies

Assessment Strategies-

The students will:

CO76. Formalise AI problem using state space tree

CO77. Identify suitable search techniques to solve the complex problem

CO78. Describe knowledge representation schemes in AI

CO79. Categorise the applications of expert system

CO80. Design a simple recommender system

Approach in teaching.

Interactive Lectures, Group Discussion, Tutorials, Case Study

 

Learning activities for the students.

Self-learning assignments, Machine Learning exercises, presentations

Class test, Semester end examinations, Quiz, Practical Assignments, Presentation

 

 

9.00
Unit I: 

Overview of Artificial Intelligence: Introduction, Importance of AI, AI and Related Field. Knowledge: General Concepts: Introduction, Definition and Importance of Knowledge. Introduction to Knowledge-Based Systems. The AI Problems, AI Techniques, Defining the Problem as a State Space Search (water jug problem), Production systems.

 

9.00
Unit II: 

Search space control strategy, Breadth First Search and Depth First Search. Heuristic Search Techniques: Generate-and-Test, Hill Climbing: Simple and Steepest-Ascent Hill Climbing, Best-First Search: OR Graphs, The A* Algorithm, Problem Reduction: AND-OR Graphs, The AO* Algorithm.

 

9.00
Unit III: 

Representations and Mappings. Formalized Symbolic Logics: Introduction, Syntax and Semantics for Propositional Logic, Syntax and Semantics for FOPL, Properties of Wffs, Conversion of Clausal Form, Inference Rules, Unification, Resolution by refutation, Non-deductive Inference Methods and Representations Using Rules

 

9.00
Unit IV: 

Weak Slot-and-Filler Structures: Semantic nets, Frames, Frames as Sets and Instances.

Strong Slot-and-Filler Structures: Conceptual Dependency, Scripts.

 

9.00
Unit V: 

Expert Systems: introduction, features, need, applications & importance. Representing and using domain knowledge, expert systems shells, and knowledge acquisition. Recommendation System and types of recommendation system, Content-based recommender systems, collaborative filtering (CF). Advantages and drawbacks. Applications of recommendation systems.

 

ESSENTIAL READINGS: 
  • E. Rich and K. Knight, “Artificial Intelligence”, Tata Mc Graw Hill, 2010.
  • D.W. Patterson, “Introduction to AI and Expert Systems”, PHI, 1999.
  • C.C. Aggarwal, Recommender Systems: The Textbook, Springer, 2016
  • I.Gupta and G. Nagpal. Artificial Intelligence and Expert Systems. (n.p.): Mercury Learning and Information,2020.

 

REFERENCES: 

Suggested Reference Books

 

  • N.J. Nilsson, “Principles of AI”, Narosa Publ. House, 2014.
  • Peter Jackson, “Introduction to Expert Systems”, AWP, M.A., 1992.
  • M. Sasikumar, S. Ramani, “Rule Based Expert Systems”, Narosa Publishing House, 1994

E-Resources including links

Reference Journals

 

 

 

Academic Year: 
2023-24
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