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Georgia Institute of Technology

Knowledge-Based AI: Cognitive Systems

Georgia Institute of Technology via Udacity


This class is offered as CS7637 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

This is a core course in artificial intelligence. It is designed to be a challenging course, involving significant independent work, readings, assignments, and projects. It covers structured knowledge representations, as well as knowledge-based methods of problem solving, planning, decision-making, and learning.

The class is organized around three primary learning goals. First, this class teaches the concepts, methods, and prominent issues in knowledge-based artificial intelligence. Second, it teaches the specific skills and abilities needed to apply those concepts to the design of knowledge-based AI agents. Third, it teaches the relationship between knowledge-based artificial intelligence and the study of human cognition.


  • Introduction to KBAI and Cognitive Systems
    • Where Knowledge-Based AI fits into AI as a whole,Cognitive systems: what are they?,AI and cognition: how are they connected?
  • Fundamentals
    • Semantic Networks,Generate & Test,Means-Ends Analysis,Problem Reduction,Production Systems
  • Common Sense Reasoning
    • Frames,Understanding,Common Sense Reasoning,Scripts
  • Planning
    • Logic,Planning
  • Learning
    • Learning by Recording Cases,Incremental Concept Learning,Classification,Version Spaces & Discrimination Trees
  • Analogical Reasoning
    • Case-Based Reasoning,Explanation-Based Learning,Analogical Reasoning
  • Visuospatial Reasoning
    • Constraint Propagation,Visuospatial Reasoning
  • Design & Creativity
    • Configuration,Diagnosis,Design,Creativity
  • Metacognition
    • Learning by Correcting Mistakes,Meta-Reasoning,AI Ethics

Taught by

Ashok Goel


3.0 rating, based on 2 Class Central reviews

Start your review of Knowledge-Based AI: Cognitive Systems

  • This course covers an interesting range in material in a structured way that is easy to follow.

    Unlike many of the other more "hip" AI MOOCs out there, the lectures for this one are rather dry and difficult to follow. It feels a lot more like a traditional "classroom" experience than some other MOOCs I have taken lately.

    Even given the boring lectures, there is a large amount of content and a heavy use of graphics to demonstrate key points.

  • Anonymous
    Too hypothetical. Could use practical programming exercises, like python notebook style that Andrew Ng does in his Coursera courses. The examples sometimes are a bit far of from being applicable. Otherwise it is very interesting and would do well with a deepening follow up course.

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