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The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition.
The learning goals of the Knowledge-Based AI course are to develop an understanding of (1) the basic architectures, representations and techniques for building knowledge-based AI agents, and (2) issues and methods of knowledge-based AI. The main learning strategies are learning-by-example and learning-by-doing. Thus, the course puts a strong emphasis on homework assignments and programming projects. The course will cover three kinds of topics: (1) core topics such as knowledge representation, planning, constraint satisfaction, case-based reasoning, knowledge revision, incremental concept learning, and explanation-based learning, (2) common tasks such as classification, diagnosis, and design, and (3) advanced topics such as analogical reasoning, visual reasoning, and meta-reasoning.
View the CS 7637 - Knowledge-Based AI Syllabus for more detail.
A good course on computer programming such as CS 1332 or Udacity’s CS 101 is beneficial for students. An introductory course on Artificial Intelligence, such as Georgia Tech's CS 3600 or CS 6601, is recommended but not required.
To succeed in this course, you should be able to answer 'Yes' to the following four questions:
For more information about grading, course requirements, and more please refer to the CS 7637 course syllabus.
There are no required course readings. However, there will be recommended supplemental readings for each lesson in this course. Here are a few general-purpose readings in knowledge-based artificial intelligence:
All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code.