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align="center">26:198:722<br> <font size="6">Expert Systems</font><br> Fall 1999<br> </h2> <div align="left"><table border="0" cellpadding="3"> <tr> <td valign="top" colspan="2" width="30"><img src="../../../images/dot_clear.gif" width="30" height="1"></td> <td valign="top" width="90"><img src="../../../images/dot_clear.gif" width="90" height="1"></td> <td valign="top"> </td> <td valign="top"><img src="../../../images/dot_clear.gif" width="75" height="1"></td> <td valign="top"><br> </td> </tr> <tr> <td valign="top" colspan="2"><font size="2" face="Arial"><strong>Class</strong></font><font face="Arial"><strong> </strong></font></td> <td valign="top"><font size="2" face="Arial"><strong>Date</strong></font><font face="Arial"><strong> </strong></font></td> <td valign="top"><font size="2" face="Arial"><strong>Topic </strong></font></td> <td valign="top"><font size="2" face="Arial"><strong>Readings</strong></font><font face="Arial"><strong> </strong></font></td> <td valign="top"><font size="2" face="Arial"><strong>Assignments</strong></font><font face="Arial"><strong><br> </strong></font></td> </tr> <tr> <td valign="top" colspan="2">1 </td> <td valign="top"><a name="September">September</a> 2 </td> <td valign="top">Introduction;<br> Review of Graph Theory</td> <td valign="top">Handout (Lauritzen Chapter 2)</td> <td valign="top"><br> <br> <br> </td> </tr> <tr> <td valign="top" colspan="2">2 </td> <td valign="top">September 9 </td> <td valign="top">Review of Probability Theory and Independence</td> <td valign="top">Handout (Lauritzen Chapter 3);<br> Reference 1</td> <td valign="top"><br> <br> <br> <br> </td> </tr> <tr> <td valign="top" colspan="2">3 </td> <td valign="top">September 16 </td> <td valign="top">Introduction to Expert Systems and Artificial Intelligence</td> <td valign="top"><em>Chapters 1, 2, 11, 12 & 17</em></td> <td valign="top"><br> <br> <br> <br> </td> </tr> <tr> <td valign="top" colspan="2">4 </td> <td valign="top">September 23 </td> <td valign="top">Knowledge Engineering; Machine Learning</td> <td valign="top"><em>Chapters 3, 10 & 20<br> </em>Handout (Durkin Chapter 17)</td> <td valign="top"><br> <br> <br> <br> <br> </td> </tr> <tr> <td valign="top" colspan="2">5</td> <td valign="top">September 30<br> </td> <td valign="top">Rule-based Expert Systems;<br> CLIPS</td> <td valign="top"><em>Chapter 5 & Appendix A</em></td> <td valign="top">Assignment 1<br> <br> </td> </tr> <tr> <td valign="top" colspan="2">6</td> <td valign="top"><a name="October">October</a> 7 <br> </td> <td valign="top">Representing Uncertainty;<br> Certainty Factors;<br> MYCIN &<br> PROSPECTOR</td> <td valign="top"><em>Chapter 9<br> </em>Handouts (Durkin Chapter 11 & 12)<br> References 2, 3, 4</td> <td valign="top"><em>SP6<br> SP7</em><br> Review CLIPS Documentation<br> <br> <br> </td> </tr> <tr> <td valign="top" colspan="2">7 </td> <td valign="top">October 14 </td> <td valign="top">Bayesian Networks</td> <td valign="top">References 5</td> <td valign="top"><em>SP8</em><br> Assignment 2<br> </td> </tr> <tr> <td valign="top" colspan="2">8 </td> <td valign="top">October 21 <br> </td> <td valign="top">Bayesian Networks; Local Propagation<br> </td> <td valign="top">References 6 & 7</td> <td valign="top"><em>SP18</em><br> <br> <br> </td> </tr> <tr> <td valign="top" colspan="2">9 </td> <td valign="top">October 28 </td> <td valign="top">The Aalborg Architecture<br> HUGIN</td> <td valign="top">References 8 & 9</td> <td valign="top"><em>SP19</em><br> Assignment 3<br> </td> </tr> <tr> <td valign="top" colspan="2">10 </td> <td valign="top"><a name="November">November</a> 4</td> <td valign="top">The Shenoy-Shafer Architecture</td> <td valign="top">References 10, 11a, 11b, 12, 13, & 14</td> <td valign="top"><em>SP22<br> </em>CLIPS Programming Project<br> </td> </tr> <tr> <td valign="top" colspan="2">11 </td> <td valign="top">November 11 <br> </td> <td valign="top">Summary of Probabilistic Expert Sytems</td> <td valign="top">Reference 15</td> <td valign="top">Assignment 4<em><br> <br> <br> </em></td> </tr> <tr> <td valign="top" colspan="2">12 </td> <td valign="top">November 18 </td> <td valign="top">Dempster-Shafer Belief Funcions</td> <td valign="top"><em>Chapter 21<br> </em>References 16, 17, 18, 19 & 20</td> <td valign="top"><em>SP23<br> </em><a name="MIDTERM">Take-home Mid-Term</a><em><br> </em></td> </tr> <tr> <td valign="top" colspan="2"> </td> <td valign="top">November 25<br> </td> <td valign="top" colspan="3"><a name="THANKSGIVING"><em>THANKSGIVING</em></a><em> BREAK<br> </em></td> </tr> <tr> <td valign="top" colspan="2">13 </td> <td valign="top"><a name="December">December</a> 2 </td> <td valign="top">Possibility Theory;<br> Spohn's Epistemic (Dis)beliefs;<br> Valuation Networks</td> <td valign="top">References 21, 22, 23, 24 & 25</td> <td valign="top">Assignment 5<br> </td> </tr> <tr> <td valign="top" colspan="2">14 </td> <td valign="top">December 9 </td> <td valign="top"><em>PURSUIVANT</em><br> Research Issues;<br> Summary and Conclusions<br> </td> <td valign="top">Reference 26</td> <td valign="top">Assignment 6<br> <br> <br> </td> </tr> <tr> <td valign="top" colspan="2"> </td> <td valign="top">December 16 </td> <td valign="top" colspan="2">FINAL EXAMINATION </td> <td valign="top"><br> </td> </tr> <tr> <td valign="top" colspan="3"><font size="3"><em>Notes:</em></font><font size="4"><em> </em></font></td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"><br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">1. </td> <td valign="top" colspan="4">Unidentified chapter readings in italics are from Peter Jackson "Introduction to Expert Systems"; assignments and readings from other books and papers will be made available by the instructor as they arise<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">2. </td> <td valign="top" colspan="4">Professor Glenn Shafer will teach classes 1 and 2; other classes will be taught by Professor Peter R. Gillett<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">3.</td> <td valign="top" colspan="4">Assignments numbered <em>SP</em> refer to chapters of the textbook that will be the topics of student presentations in class<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> </tr> <tr> <td valign="top" colspan="3"><em>References</em></td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">1.</td> <td valign="top" colspan="4">Pearl, J., D. Geiger, and T. Verma. 1989. "Conditional Independence and its Representations". <em>Kybernetica</em> 25:2<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">2.</td> <td valign="top" colspan="4">Shortcliffe, E., and B. G. Buchanan. 1975. "A Model of Inexact Reasoning in Medicine". <em>Mathematical Biosciences</em> 23<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">3.</td> <td valign="top" colspan="4">Duda, R. O., P. E. Hart, and N. J. Nilsson. 1976. "Subjective Bayesian Methods for Rule-Based Inference Systems". <em>Proceedings National Computer Conference (AFIPS)</em> 15<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">4.</td> <td valign="top" colspan="4">Heckerman, D. 1986. "Probabilistic Interpretations for MYCIN's Certainty Factors". <em>Uncertainty in Artificial Intelligence<br> </em></td> </tr> <tr> <td valign="top"> </td> <td valign="top">5.</td> <td valign="top" colspan="4">Pearl, J. 1987. "Bayesian Decision Methods". In <em>Encyclopedia of AI</em>: Wiley Intersciences<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">6.</td> <td valign="top" colspan="4">Pearl, J. 1986. "Fusion, Propagation, and Structuring in Belief Networks". <em>Artificial Intelligence</em> 29<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">7.</td> <td valign="top" colspan="4">Lauritzen, S. L., and D. J. Spiegelhalter. 1988. "Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems". <em>Journal of the Royal Statistical Society</em><br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">8.</td> <td valign="top" colspan="4">Jensen, F. V., K. G. Oleson, and S. K. Andersen. 1988. "An Algebra of Bayesian Belief Universes for Knowledge-Based Systems". Institute of Electronic Systems, Aalborg University<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">9.</td> <td valign="top" colspan="4">Jensen, F. V., S. L. Lauritzen, and K. G. Oleson. 1990. "Bayesian Updating in Causal Probabilistic Networks by Local Computations". <em>Computational Statistics Quarterly<br> </em></td> </tr> <tr> <td valign="top"> </td> <td valign="top">10.</td> <td valign="top" colspan="4">Shenoy, P., and G. Shafer. 1990. "Axioms for Probability and Belief-Function Propagation". <em>Uncertainty in Artificial Intelligence<br> </em></td> </tr> <tr> <td valign="top"> </td> <td valign="top">11a. </td> <td valign="top" colspan="4">Lauritzen, S.L., and P. P. Shenoy. 1996. "Computing Marginals Using Local Computation". <em>Working Paper</em>. University of Kansas<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">11b. </td> <td valign="top" colspan="4">Shenoy, P. P. 1997. "Binary Join Trees for Computing Marginals in the Shenoy-Shafer Architecture". <i>International Journal of Approximate Reasoning </i>17<i><br> </i></td> </tr> <tr> <td valign="top"> </td> <td valign="top">12.</td> <td valign="top" colspan="4">Lepar, V., and P. P. Shenoy. 1998. "A Comparison of Lauritzen-Spiegelhalter, Hugin and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions". <i>Proceedings of the XIVth Conference on Uncertainty in Artificial Intelligence</i>: Morgan Kaufmann, San Mateo, CA<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">13. </td> <td valign="top" colspan="4">Schmidt, T., and Shenoy, P. P. 1998. "Some improvements to the Shenoy-Shafer and Hugin architectures for computing marginals". <i>Artificial Intelligence<br> </i></td> </tr> <tr> <td valign="top"> </td> <td valign="top">14. </td> <td valign="top" colspan="4">Gillett, P. R., and Shenoy, P. P. 1999. "Computing Marginals in Binary Join Trees". <em>Working Paper</em><br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">15.</td> <td valign="top" colspan="4">Shafer, G. 1996. <em>Probabilistic Expert Systems</em>. SIAM<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">16.</td> <td valign="top" colspan="4">Shafer, G. and R. P. Srivastava. 1990. "The Bayesian and Belief-Function Formalisms - A General Perspective for Auditing". <em>Auditing: A Journal of Practice and Theory<br> </em></td> </tr> <tr> <td valign="top"> </td> <td valign="top">17.</td> <td valign="top" colspan="4">Srivastava, R. P. and G. Shafer. 1992. "Belief_Function Formulas for Audit Risk". <em>The Accounting Review</em>, 67:2<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">18.</td> <td valign="top" colspan="4">R. P. Srivastava. 1995. "A General Scheme for Aggregating Evidence in Auditing: Propagation of Beliefs in Networks". In <em>Artificial Intelligence in Accounting and Auditing</em>. Markus Wiener Publishers<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">19.</td> <td valign="top" colspan="4">Xu, H. and R. Kennes. 1994. "Steps Towards Efficient Implementation of Dempster-Shafer Theory". In <em>Advances in the Dempster-Shafer Theory of Evidence</em>. John Wiley & Sons, Inc<em><br> </em></td> </tr> <tr> <td valign="top"> </td> <td valign="top">20.</td> <td valign="top" colspan="4">Kennes, R. 1991. "Computational Aspects of the Möbius Transfom of a Graph". <em>IEEE Transactions on systems, Man and Cybernetics</em><br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">21.</td> <td valign="top" colspan="4">Dubois, D., and H. Prade. 1988. "An Introduction to Possibilistic and Fuzzy Logics". In <em>Non-Standard Logics for Automated Reasoning</em>: Academic Press Limited<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">22.</td> <td valign="top" colspan="4">Shenoy, P. P. 1992. "Using Possibility Theory in Expert Systems". <em>Fuzzy Sets and Systems</em> 52<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">23.</td> <td valign="top" colspan="4">Spohn, W. 1988 "Ordinal Conditional Functions: A Dynamic Theory of Epistemic States". <em>Causation in Decision, Belief Change and Statistics</em>: D. Reidel<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">24.</td> <td valign="top" colspan="4">Spohn, W. 1990. "A General Non-Probabilistic Theory of Inductive Reasoning". <em>Uncertainty in Artificial Intelligence<br> </em></td> </tr> <tr> <td valign="top"> </td> <td valign="top">25.</td> <td valign="top" colspan="4">Shenoy, P. P. 1991. "On Spohn's Rule for Revision of Beliefs". <em>International Journal of Approximate Reasoning</em><br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top">26.</td> <td valign="top" colspan="4">Gillett, P. R. 1997. <em>A Comparative Study of Audit Evidence and Audit Planning Models Using Uncertain Reasoning</em>. UMI<br> </td> </tr> <tr> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> </tr> <tr> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> <td valign="top"> </td> </tr> </table> </div></td> <td valign="top" width="5" height="23"><img src="../../../images/dot_clear.gif" width="5" height="1"></td> </tr> </table> </div>  </body>