Computer Science 470
Introduction to Artificial Intelligence
Fall 2010
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<h3> Course Goals </h3> At the end of this course, and for at least one year after, you should be able to: <br><br> <table border="3" summary="Goals for CS470"> <tr><td> Recognize an AI problem, no matter the source of the problem <ul> <li> Business</li> <li> Medicine </li> <li> Gaming</li> <li> Robotics </li> <li> Research, etc. </li> </ul> <tr><td> Identify the component elements of the problem: <ul> <li> Does it require basic control? </li> <li> Does it involve uncertain reasoning? </li> <li> Does it have a simple goal, sophisticated utility, or multiple attributes? </li> <li> Does it require sequential choice/planning?</li> <li> How many of decision makers are involved?</li> </ul> </td> <tr> <td>Formalize the problem in a way that is amenable to a solution. <ul> <li> Representation or reaction </li> <li> PEAS and the nature of environment </li> <li> CSA, states, and sequencing </li> <li> Random variables, pdfs, cdfs, joint distributions </li> <li> Values </li> </ul> </td> <tr> <td>Recall the names and use for different classes of algorithms <ul> <li> Uninformed, informed, constraint-satisfaction, and hill-climbing search </li> <li> Markov processes, Bayes rule, Bayes nets, HMMs, grid filters, and Kalman filters </li> <li> Expected utility theory and multi-attribute utility theory </li> <li> Sequential choice under uncertainty: value and policy iteration </li> <li> Game theory </li> </ul> </td> <tr><td> Implement several algorithms using the specifications from a book </td> <tr><td> Determine whether your solution is correct and what to do if it's not </td> <tr><td> Value the quality of a well-communicated solution </td> </table>Back to top