Introduction to Information Representation

Learn the computational techniques used by computers in the representation of knowledge in this free online course.

Publisher: NPTEL
This free online course on knowledge representation explains the variations between man and computer. The course explains how computers use logic in storing information, and how the process of representing and storing information has evolved over time. This course describes how computers use previous experience to solve problems. The course also goes into details in explaining how computers use certain techniques for knowledge representation.
Introduction to Information Representation
  • Duration

    1.5-3 Hours
  • Students

  • Accreditation






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This free course on introduction to knowledge representation begins by describing how search graphs are used for the representation of solutions of problems that can be decomposed into smaller problems. The course explains in well detailed form, how computational solutions to large problems are arrived at by first solving small component problems. The course introduces you to the concept of game playing and how they have been an integral part of the development of artificial intelligence.

This course then introduces you to the techniques required in selecting the right algorithm for game playing.The course outlines the distinction between symbolic and connectionist artificial intelligence. You will learn about alpha-beta layering, the search space reduction paradigm and knowledge representation hypothesis . You will also get to learn about the syntax and semantics of a knowledge representation language, along with the desired features of a knowledge representation scheme. 

Finally, this course explains formal logic knowledge representation for artificial intelligence. The course takes you through how knowledge representation in reasoning forms the backbone of any intelligent behaviour through computational means. You will learn about the relationship between propositional logic, statement logic and sentential logic. The course concludes by detailing how computational models of intelligence require models of knowledge in solving problems.

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