Summary

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.

Expected Learning

At the conclusion of this class, you will be able to accomplish three primary tasks. First, you will be able to design and implement a knowledge-based artificial intelligence agent that can address a complex task using the methods discussed in the course. Second, you will be able to use this agent to reflect on the process of human cognition. Third, you will be able to use both these practices to address practical problems in multiple domains.

Syllabus

Unit 1: 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?

Unit 2: Fundamentals

• Semantic Networks
• Generate & Test
• Means-Ends Analysis
• Problem Reduction
• Production Systems

Unit 3: Common Sense Reasoning

• Frames
• Understanding
• Common Sense Reasoning
• Scripts

Unit 4: Planning

• Logic
• Planning

Unit 5: Learning

• Learning by Recording Cases
• Incremental Concept Learning
• Classification
• Version Spaces & Discrimination Trees

Unit 6: Analogical Reasoning

• Case-Based Reasoning
• Explanation-Based Learning
• Analogical Reasoning

Unit 7: Visuospatial Reasoning

• Constraint Propagation
• Visuospatial Reasoning

Unit 8: Design & Creativity

• Configuration
• Diagnosis
• Design
• Creativity

Unit 9: Metacognition

• Learning by Correcting Mistakes
• Meta-Reasoning
• AI Ethics

Required Knowledge

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:

1. Are you comfortable with computer programming?
2. Are you familiar with concepts of data structures and object-oriented programming, such as inheritance and polymorphism?
3. Are you familiar with concepts of algorithms, such as sorting and searching algorithms?
4. Are you confident with either Java or Python?