Computer Science

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Course Goals

At the end of this course, and for at least one year after, you should be able to:

Recognize an AI problem, no matter the source of the problem Business Medicine Gaming Robotics Research, etc.

Identify the component elements of the problem: 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/planning? How many of decision makers are involved?

Formalize the problem in a way that is amenable to a solution. PEAS and the nature of environment CSA, states and sequencing Random variables, pdfs, cdfs, joint distributions Values

Recall the names and use for different classes of algorithms Uninformed, informed, constraint-satisfaction, and hill-climbing search Markov processes, Bayes rule, Bayes nets, HMMs, grid filters, particle filters, and Kalman filters Expected utility theory and multi-attribute utility theory Sequential choice under uncertainty Game theory

Implement several algorithms using the specifications from a book

Determine whether your solution is correct and what to do if it's not

Value the quality of a well-communicated solution