Credits and Contact Hours

3 credits, 43 hours

Course Instructor Name

Dr. Abdullah Mutawa

Textbook

• Artificial Intelligence: a Modern Approach, Stuart Russel and Peter Norvig
• Programming for Artificial Intelligence, Ivan Bratko, Prolog, 2nd Edition

Catalog Description

Topics include history and overview of intelligent systems. Overview of technologies. Fundamental issues in intelligent systems. Intelligent system design methodologies. Search and constraint satisfaction. Knowledge representation and reasoning, and agents.

Prerequisite

CpE-300

Specific Goals for the Course

Upon successful completion of this course, students will be able to:

• Differentiate between real, natural, and artificial intelligence.
• Describe a range of search algorithms, and show how a search tree would be traversed using these algorithms. (Student outcomes: 1, 2).
• Show how simple puzzles can be formulated as search problems. (Student outcomes: 1, 2).
• Explain how Minmax search is used in game playing system, and outline how it may be made more efficient using Alpha-Beta pruning. (Student outcomes: 1, 2).
• Use different knowledge representation methods to represent fragments of knowledge, given an English description of that knowledge.
• Read and write (at least simple versions of) the major knowledge representation formalisms.
• Familiarize with the design of Knowledge Based Systems (KBSs) and production rules, through study of classic exemplars. (Student outcomes: 1).
• Implement a simple forward chaining KBS. (Student outcomes: 1, 2).
• Understand the limitations of AI, which problems are still hard, and why. (Student outcomes: 1, 4).
• Understand the principal ethical and social issues in AI research and development. (Student outcomes: 4)

Topics to Be Covered

• Introduction to AI.
• Knowledge representation and inference.
• Expert systems.
• Using search for problem solving.
• Agents and robotics.
• Concept learning.
• Decision Trees.
• Neural Networks.
• Evolutionary Algorithms (optional if time permitted)