East Carolina University
Department of Computer Science

CSCI 5800
Artificial Intelligence
Standard Syllabus

Catalog entry

P: CSCI 2540 or CSCI 3200 or consent of instructor. Fundamental problems and techniques of artificial intelligence. Heuristic search. Concepts of expert systems.

Course summary

This course concentrates on the fundamental problems and solution techniques used in problems associated with artificial intelligence. Fundamentals of knowledge representation and search are presented along with a discussion of topic areas such as machine learning, planning, natural language processing, automated reasoning, and reasoning under uncertainty.

Course topics

• Knowledge Representation fundamentals
• Search Fundamentals
• Prolog
• Two or more application areas of artificial intelligence.
• (If applicable) instructor research in the field.

Student learning outcomes

• Specify problem descriptions using logic or some advanced knowledge representation form.
• Explain and simulate several search algorithms for state-space search including both heuristic and non-heuristic search techniques.
• Program solutions to basic problems using the Prolog programming language.
• Explain and simulate algorithms for problem-solving techniques in one or more application areas of artificial intelligence.
• Complete a research-oriented investigation into a suitably scoped problem that requires application of artificial intelligence techniques. Investigation should require both programming as well as a summary writeup that demonstrates an ability to self-critique the success of the investigation relative to the original project goal.

Textbook

Artificial Intelligence: Structures and Strategies for Complex Problem Solving, George F. Luger. Addison Wesley, 2009, 6th.ed.

Grading

Grading will be based on midterm exam, final exam, homework assignments, and a comprehensive project. The midterm exam will count 15%, the final exam 25%, the homework assignments 40% and the project 20%.

Grade meanings