Artificial Intelligence

Artificial Intelligence course focuses on the development of algorithms and statistical models.

Course Objectives

  • To learn the difference between optimal reasoning vs human like reasoning.
  • To understand the notions of state space representation, exhaustive search, heuristic search.
  • To learn different knowledge representation techniques.
  • To understand the applications of AI like Game Playing and Expert Systems.
  • To introduce the concept of Machine Learning.

Course Syllabus

UNIT 1

Introduction: History, Intelligent Systems, Foundations of AI, Sub areas of AI & Applications.

Problem Solving – State-Space Search and Control Strategies, Characteristics of Problem, General Problem Solving Techniques, Exhaustive Searches

Heuristic Search Techniques

Iterative-Deepening A*, Constraint Satisfaction

Game Playing, Bounded Look-ahead Strategy and use of Evaluation Functions, Alpha-Beta Pruning

UNIT 2

Logic Concepts: Introduction, Propositional Calculus, Propositional Logic, Natural Deduction System, Axiomatic System, Semantic Tableau System in Propositional Logic, Resolution Algorithm

Predicate Logic

Logic Programming

UNIT 3

Knowledge Representation: Introduction, Approaches to Knowledge Representation, Knowledge Representation using Semantic Network

Extended Semantic Networks for KR, Knowledge Representation using Frames. Advanced Knowledge Representation Techniques: Case Grammers, Semantic Web

*Knowledge Representation

UNIT 4

Uncertainty Measure – Bayesian Belief Networks, Certainty Factor Theory, Dempster-Shafer Theory.

Introduction to Machine Learning: Machine Learning Systems, Supervised and unsupervised learning.

Uncertainty Measure

Introduction to Machine Learning

UNIT 5

Expert System and Applications: Introduction, Phases in Building Expert Systems, Expert System Architecture, Expert Systems Vs Traditional Systems, Rule based Expert Systems, Truth Maintenance Systems, Applications of Expert Systems, List of Shells and Tools.

Expert Systems

Expert Systems

Text Books

Saroj Kaushik, Artificial Intelligence, Cengage Learning, 2011.

Reference Books

  • Rich, Knight, Nair: Artificial Intelligence, Tata McGraw Hill, 3rd Edition, 2009.
  • Eugene Charniak, Introduction to Artificial Intelligence, Pearson, 2007
  • Dan W.Patterson, Introduction to Artificial Intelligence and Expert Systems, PHI, 1990.
  • George Fluger, Artificial Intelligence, 5th Edition, Pearson.

Online Resources

  • http://www.vssut.ac.in/lecture_notes/lecture1428643004.pdf
  • http://nptel.ac.in/courses/106105077/
  • https://onlinecourses.nptel.ac.in/noc18_cs18/preview
  • https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-4

Course Outcomes

After completion of the course, students will be able to:

  • Understand the basics of AI and to formulate efficient problem space and select a search algorithm for a problem.
  • Apply AI techniques to solve problems related to Game Playing, Expert Systems.
  • Understand and apply Logic programming in problem solving.
  • Represent knowledge using appropriate techniques.
  • Interpretation of probabilistic and logical reasoning in knowledge base.
  • Understand the concepts of machine learning.