C S 470

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Introduction to Artificial Intelligence

Computer Science College of Physical and Mathematical Sciences

Course Description

Introduction to core areas of artifical intelligence; intelligent agents, problem solving and search, knowledge-based systems and inference, planning, uncertainty, learning, and perception.

When Taught

Fall

Min

3

Fixed

3

Fixed

3

Fixed

0

Title

Recognize problems that can use AI methods

Learning Outcome

from business, medicine, robotics, gaming, and machine learning

Title

Identify the component elements of the problem

Learning Outcome

Does it require basic control? Does it involve uncertain reasoning? Does it have a simple goal, sophisticated utility, or multiple attributes? Does it require sequential choice or planning? How may decision makers are involved?

Title

Formalize the problem in a way that is amenable to a solutio

Learning Outcome

What is the internal representation? What performance criteria are relevant? What is the nature of the environment? What are the actuators? What are the sensors? What are the agent's utilities? How is uncertainty represented?

Title

Be able to analyze, implement, and experiment with several A

Learning Outcome

Uninformed, informed, constraint-satisfaction, hill-climbing, and stochastic (e.g., genetic algorithms) search Markov provesses, Bayes rule, Bayes nets, Hidden Markov Models, Grid Filters, and Kalman Filters Expected utility theory Multi-attribute utility theory Sequential choice under uncertainty: value and policy iteration Minimax solutions from game theory: alpha-beta pruning

Title

Analyze and communicate solution quality

Learning Outcome

Determine whether your solution is correct Express the quality of your solution Value the need to communicate your solution to others