CSE 630: Survey of Artificial Intelligence I: Basic Techniques
Description
A survey of the basic concepts and techniques, problem solving, and knowledge representation, including an introduction to expert systems.
Level, Credits, Class Time Distribution, Prerequisites
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Level
|
Credits
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Class Time Distribution
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Prerequisites
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|
UG |
3 |
3 cl |
222/H222 and Math 366 |
Quarters Offered
General Information, Exclusions, Cross-listings, etc.
Intended Learning Outcomes
For details of terminology see http://www.cse.ohio-state.edu/cgi-bin/syllabus-view.cgi?cgi_state=loexpl
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Master basic search techniques for problem-solving, including systematic blind search, heuristically-guided search, and optimal search.
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Be familiar with game tree search methods and the requirements for expert-level game play.
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Be familiar with using logic and proof as a basis for knowledge representation and automated reasoning.
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Be familiar with using semantic nets and frame systems as knowledge-representation formalisms.
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Be exposed to problems in common sense reasoning and language understanding.
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Be exposed to integrated AI architectures as a platform for building AI systems.
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Be exposed to machine learning techniques and the kinds of problem they solve.
Representative Texts and Other Course Materials
Textbook(s) and other materials listed are representative only. Please visit or contact a campus-area bookstore before the term starts to determine the textbook(s) to be used in a particular section of the course.
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Artificial Intelligence, A Modern Approach (2nd edition), Prentice Hall, 2002 - Stuart Russell and Peter Norvig
Representative Topics List
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Number of Hours
|
Topic
|
| 6 |
Basic representation and problem solving methods |
| 6 |
Search techniques and game playing |
| 6 |
Knowledge representation using logic, automated proof techniques |
| 6 |
Machine learning or probabilistic inference |
| 3 |
Planning and common sense reasoning |
| 2 |
Perception and communication |
| 1 |
Exam |
Representative Lab Assignments
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Compare breadth-first, depth-first, and A* search on a problem domain
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Apply reinforcement learning to maze navigation
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Construct STRIPS-style planning operators for a domain
Representative Grading Plan
| Homeworks and Labs |
40% |
| Midterm |
30% |
| Final |
40% |
Relationship to BS-CSE Program Outcomes
For details see http://www.cse.ohio-state.edu/cgi-bin/syllabus-view.cgi?cgi_state=abet;SYLLABUS_ID=340
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Course Coordinator: James William Davis
Last modified: 2006-08-02 09:53:57
