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<LI>Giorgio Brajnik and Daniel J. Clancy. 1996. <B><!WA41><!WA41><!WA41><!WA41><!WA41><!WA41><!WA41><!WA41><!WA41><!WA41><!WA41><!WA41><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Brajnik+Clancy-TIME96.ps.Z">Guidingand refining simulation using temporal logic. </A></B><I>Third InternationalWorkshop on Temporal Representation and Reasoning (TIME'96),</I> 1996.</LI><P><!WA42><!WA42><!WA42><!WA42><!WA42><!WA42><!WA42><!WA42><!WA42><!WA42><!WA42><!WA42><A HREF="http://www.cs.utexas.edu/users/qr/abstracts-tl.html">[Abstract]</A> </P><LI>Giorgio Brajnik and Daniel J. Clancy. 1996. <B><!WA43><!WA43><!WA43><!WA43><!WA43><!WA43><!WA43><!WA43><!WA43><!WA43><!WA43><!WA43><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Brajnik+Clancy-QR96.ps.Z">Temporalconstraints on trajectories in qualitative simulation</A></B>. In <I>WorkingPapers of the Tenth International Workshop on Qualitative Reasoning (QR-96)</I>,Fallen Leaf Lake, California. </LI><P><!WA44><!WA44><!WA44><!WA44><!WA44><!WA44><!WA44><!WA44><!WA44><!WA44><!WA44><!WA44><A HREF="http://www.cs.utexas.edu/users/qr/abstracts-tl.html">[Abstract]</A> </P><LI>Giorgio Brajnik and Daniel J. Clancy. 1996. <B><!WA45><!WA45><!WA45><!WA45><!WA45><!WA45><!WA45><!WA45><!WA45><!WA45><!WA45><!WA45><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Brajnik+Clancy-AAAI96.ps.Z">Temporalconstraints on trajectories in qualitative simulation</A></B>. In <I>Proceedingsof the National Conference on Artificial Intelligence (AAAI-96),</I> AAAI/MITPress, 1996. </LI><P><!WA46><!WA46><!WA46><!WA46><!WA46><!WA46><!WA46><!WA46><!WA46><!WA46><!WA46><!WA46><A HREF="http://www.cs.utexas.edu/users/qr/abstracts-tl.html">[Abstract]</A> </P><P><B>Manuscripts:</B></P><LI>Giorgio Brajnik and Daniel J. Clancy. 1997. <!WA47><!WA47><!WA47><!WA47><!WA47><!WA47><!WA47><!WA47><!WA47><!WA47><!WA47><!WA47><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Brajnik+Clancy-HART97-sub.ps.Z">Controlof Hybrid Systems using Qualitative Simulation</A> . Submitted for publicationto International Workshop on Hybrid and Real-Time Systems (HART-97).</LI><P><!WA48><!WA48><!WA48><!WA48><!WA48><!WA48><!WA48><!WA48><!WA48><!WA48><!WA48><!WA48><A HREF="http://www.cs.utexas.edu/stage/net/www/users/qr/abstracts-tl.html">[Abstract]</A> </P><LI>Giorgio Brajnik and Daniel J. Clancy. 1997. <!WA49><!WA49><!WA49><!WA49><!WA49><!WA49><!WA49><!WA49><!WA49><!WA49><!WA49><!WA49><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Brajnik+Clancy-AMAI96-sub.ps.Z">Focusingqualitative simulation using temporal logic:Theoretical Foundations</A>Submitted for publication to the Annals of Mathematics and Artificial Intelligence.</LI><P><!WA50><!WA50><!WA50><!WA50><!WA50><!WA50><!WA50><!WA50><!WA50><!WA50><!WA50><!WA50><A HREF="http://www.cs.utexas.edu/stage/net/www/users/qr/abstracts-tl.html">[Abstract]</A> </P></UL></UL><P><HR></P><H3><A NAME="TSA"></A>Time-Scale Abstraction </H3><UL><LI>B. J. Kuipers. 1987. <B><!WA51><!WA51><!WA51><!WA51><!WA51><!WA51><!WA51><!WA51><!WA51><!WA51><!WA51><!WA51><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Kuipers-jaie-88.ps.Z">Abstractionby time-scale in qualitative simulation</A></B>. In <I>Proceedings of theNational Conference on Artificial Intelligence (AAAI-87).</I> Los Altos,CA: Morgan Kaufman. <BR>(The ftp copy is missing two figures.) </LI><P>Reprinted in D. S. Weld &amp; J. de Kleer (Eds.), <I>Readings in QualitativeReasoning about Physical Systems,</I> Los Altos, CA: Morgan Kaufmann, 1990.</P><P>[Superceded by <!WA52><!WA52><!WA52><!WA52><!WA52><!WA52><!WA52><!WA52><!WA52><!WA52><!WA52><!WA52><A HREF="http://www.cs.utexas.edu/users/qr/QR-book.html">QR book</A>, chapter 12.] </P><LI>Jeff Rickel and Bruce Porter. 1994. <B><!WA53><!WA53><!WA53><!WA53><!WA53><!WA53><!WA53><!WA53><!WA53><!WA53><!WA53><!WA53><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Rickel+Porter-AAAI94.ps.Z">Automatedmodeling for answering prediction questions: selecting the time scale andsystem boundary.</A></B> In <I>Proceedings of the National Conference onArtificial Intelligence (AAAI-94),</I> AAAI/MIT Press, 1994. </LI><LI>Jeff W. Rickel. 1995. <B><!WA54><!WA54><!WA54><!WA54><!WA54><!WA54><!WA54><!WA54><!WA54><!WA54><!WA54><!WA54><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Rickel-PhD-95.ps.Z">Automatedmodeling of complex systems to answer prediction questions.</A></B> Doctoraldissertation, Department of Computer Sciences, The University of Texasat Austin. </LI></UL><P><HR></P><H3><A NAME="Phase-Space"></A>Qualitative Phase Space </H3><UL><LI>W. W. Lee &amp; B. J. Kuipers. 1988. <B><!WA55><!WA55><!WA55><!WA55><!WA55><!WA55><!WA55><!WA55><!WA55><!WA55><!WA55><!WA55><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Lee+Kuipers-AAAI93.ps.Z">Non-intersectionof trajectories in qualitative phase space: a global constraint for qualitativesimulation.</A></B> In <I>Proceedings of the National Conference on ArtificialIntelligence (AAAI-88).</I> Los Altos, CA: Morgan Kaufmann, 1988. </LI><P>Reprinted in D. S. Weld &amp; J. de Kleer (Eds.), <I>Readings in QualitativeReasoning about Physical Systems,</I> Los Altos, CA: Morgan Kaufmann, 1990.</P><P>[Superceded by <!WA56><!WA56><!WA56><!WA56><!WA56><!WA56><!WA56><!WA56><!WA56><!WA56><!WA56><!WA56><A HREF="http://www.cs.utexas.edu/users/qr/QR-book.html">QR book</A>, chapter 11.] </P><LI>W. W. Lee &amp; B. Kuipers. 1993. <B><!WA57><!WA57><!WA57><!WA57><!WA57><!WA57><!WA57><!WA57><!WA57><!WA57><!WA57><!WA57><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Lee+Kuipers-AAAI93.ps.Z">Aqualitative method to construct phase portraits.</A></B> <I>Proceedingsof the National Conference on Artificial Intelligence (AAAI-93),</I> AAAI/MITPress, 1993.</LI><P>[Superceded by <!WA58><!WA58><!WA58><!WA58><!WA58><!WA58><!WA58><!WA58><!WA58><!WA58><!WA58><!WA58><A HREF="http://www.cs.utexas.edu/users/qr/QR-book.html">QR book</A>, chapter 11.] </P><P>(<!WA59><!WA59><!WA59><!WA59><!WA59><!WA59><!WA59><!WA59><!WA59><!WA59><!WA59><!WA59><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Lee+Kuipers-AAAI93-fig1.ps.Z">Figure 1</A>, <!WA60><!WA60><!WA60><!WA60><!WA60><!WA60><!WA60><!WA60><!WA60><!WA60><!WA60><!WA60><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Lee+Kuipers-AAAI93-fig2a.ps.Z">Figure2a</A>, and <!WA61><!WA61><!WA61><!WA61><!WA61><!WA61><!WA61><!WA61><!WA61><!WA61><!WA61><!WA61><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Lee+Kuipers-AAAI93-fig2b.ps.Z">Figure2b</A> were not incorporated into the PostScript file for this paper, sothey appear in auxiliary files.) </P></UL><P><HR></P><H3><A NAME="#Compare"></A>Comparative Analysis </H3><UL><LI>C. Chiu &amp; B. J. Kuipers. 1992. <B><!WA62><!WA62><!WA62><!WA62><!WA62><!WA62><!WA62><!WA62><!WA62><!WA62><!WA62><!WA62><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Chiu+Kuipers-raqp-92.ps.Z">Comparativeanalysis and qualitative integral representations.</A></B> In Boi Faltingsand Peter Struss (Eds.), <I>Recent Advances in Qualitative Physics,</I>MIT Press, 1992. </LI></UL><P><HR></P><H2><A NAME="Semi-Quant"></A>Semi-Quantitative Reasoning </H2><P><HR></P><H3><A NAME="Q2"></A>Q2 </H3><UL><LI>B. J. Kuipers &amp; D. Berleant. 1988. <B>Using incomplete quantitativeknowledge in qualitative reasoning.</B> In <I>Proceedings of the NationalConference on Artificial Intelligence (AAAI-88).</I> Los Altos, CA: MorganKaufman, 1988. </LI><P>[Superceded by <!WA63><!WA63><!WA63><!WA63><!WA63><!WA63><!WA63><!WA63><!WA63><!WA63><!WA63><!WA63><A HREF="http://www.cs.utexas.edu/users/qr/QR-book.html">QR book</A>, chapter 9.] </P></UL><P><HR></P><H3><A NAME="Q3"></A>Q3 </H3><UL><LI>J. Daniel Berleant. 1991. <B><!WA64><!WA64><!WA64><!WA64><!WA64><!WA64><!WA64><!WA64><!WA64><!WA64><!WA64><!WA64><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Berleant-PhD-91.ps.Z">Theuse of partial quantitative knowledge with qualitative reasoning.</A></B>University of Texas at Austin, Artificial Intelligence Laboratory, TechnicalReport AI 91-163. (Doctoral dissertation, Department of Computer Sciences.)</LI>  <p><LI>D. Berleant &amp; B. Kuipers. 1992. <B><!WA65><!WA65><!WA65><!WA65><!WA65><!WA65><!WA65><!WA65><!WA65><!WA65><!WA65><!WA65><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Berleant+Kuipers-raqp-92.ps.Z">Qualitative-numericsimulation with Q3.</A></B> In Boi Faltings and Peter Struss (Eds.), <I>RecentAdvances in Qualitative Physics,</I> MIT Press, 1992. <BR>(The ftp file is missing several figures.) </LI>  <p></UL><P><HR></P><H3><A NAME="NSIM"></A>NSIM and SQSIM </H3><UL><LI>H. Kay &amp; B. Kuipers. 1993. <B><!WA66><!WA66><!WA66><!WA66><!WA66><!WA66><!WA66><!WA66><!WA66><!WA66><!WA66><!WA66><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Kay+Kuipers-AAAI93.ps.Z">Numericalbehavior envelopes for qualitative simulation.</A></B> <I>Proceedings ofthe National Conference on Artificial Intelligence (AAAI-93),</I> AAAI/MITPress, 1993. </LI>  <p><LI>H. Kay &amp; L. H. Ungar. 1993. <B><!WA67><!WA67><!WA67><!WA67><!WA67><!WA67><!WA67><!WA67><!WA67><!WA67><!WA67><!WA67><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Kay+Ungar-QR93.ps.Z">Derivingmonotonic function envelopes from observations.</A></B> In <I>Working Papersof the Seventh International Workshop on Qualitative Reasoning about PhysicalSystems (QR'93),</I> Orcas Island, Washington. </LI>  <p><LI>Herbert Kay. 1996. <B><!WA68><!WA68><!WA68><!WA68><!WA68><!WA68><!WA68><!WA68><!WA68><!WA68><!WA68><!WA68><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Kay-tr-ai96-247.ps.Z">SQsim:a simulator for imprecise ODE models</A></B>. University of Texas ArtificialIntelligence Laboratory TR AI96-247, March 1996. <BR><!WA69><!WA69><!WA69><!WA69><!WA69><!WA69><!WA69><!WA69><!WA69><!WA69><!WA69><!WA69><A HREF="http://www.cs.utexas.edu/users/qr/abstracts-qr.html#SQSIM">[Abstract]</A> </LI>  <p><li>  Herbert Kay.  1996.  <!WA70><!WA70><!WA70><!WA70><!WA70><!WA70><!WA70><!WA70><!WA70><!WA70><!WA70><!WA70><a href="file://ftp.cs.utexas.edu/pub/qsim/papers/Kay-PhD-96.ps.Z"><B>Refining Imprecise Models and Their Behaviors</B></a>.Doctoral dissertation, Department of Computer Sciences, The University of Texasat Austin, December 1996. <BR><!WA71><!WA71><!WA71><!WA71><!WA71><!WA71><!WA71><!WA71><!WA71><!WA71><!WA71><!WA71><A HREF="http://www.cs.utexas.edu/users/qr/abstracts.html#Kay">[Abstract]</A> </LI>  <p></UL><P><HR></P><H2><A NAME="TL"></A>QSIM and Temporal Logic Model-Checking </H2><P>This body of work treats the behavior graph output by QSIM as a temporalmodel, and applies a model-checking algorithm to prove statements in temporallogic. </P><UL><LI>B. J. Kuipers and B. Shults. 1994. <B><!WA72><!WA72><!WA72><!WA72><!WA72><!WA72><!WA72><!WA72><!WA72><!WA72><!WA72><!WA72><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Kuipers+Shults-KR94.ps.Z">Reasoningin logic about continuous systems.</A></B> In J. Doyle, E. Sandewall, andP. Torasso, editors, <I>Principles of Knowledge Representation and Reasoning:Proceedings of the Fourth International Conference (KR94)</I>, Morgan Kaufmann,San Mateo, CA. <BR><!WA73><!WA73><!WA73><!WA73><!WA73><!WA73><!WA73><!WA73><!WA73><!WA73><!WA73><!WA73><A HREF="http://www.cs.utexas.edu/users/qr/abstracts-tl.html">[Abstract]</A><p><LI>Benjamin Shults. 1996. <B><!WA74><!WA74><!WA74><!WA74><!WA74><!WA74><!WA74><!WA74><!WA74><!WA74><!WA74><!WA74><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Shults-tr-AI96-245.ps.Z">Towarda reformalization of QSIM</A></B>. University of Texas Artificial IntelligenceLaboratory TR AI96-245, January 1996. <BR><!WA75><!WA75><!WA75><!WA75><!WA75><!WA75><!WA75><!WA75><!WA75><!WA75><!WA75><!WA75><A HREF="http://www.cs.utexas.edu/users/qr/abstracts-tl.html">[Abstract]</A><p><LI>Benjamin Shults and Benjamin Kuipers. 1997. <B><!WA76><!WA76><!WA76><!WA76><!WA76><!WA76><!WA76><!WA76><!WA76><!WA76><!WA76><!WA76><AHREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Shults+Kuipers-aij-97.ps.Z">Proving properties of continuous systems: qualitative simulation andtemporal logic</A></B>.  <cite>Artificial Intelligence Journal</cite>, 1997.  <br><!WA77><!WA77><!WA77><!WA77><!WA77><!WA77><!WA77><!WA77><!WA77><!WA77><!WA77><!WA77><A HREF="http://www.cs.utexas.edu/users/qr/abstracts-tl.html">[Abstract]</A><p></UL><P><HR></P><H2><A NAME="Models"></A>Building Qualitative Models <HR></H2><H3><A NAME="CC"></A>CC </H3><UL><LI>David W. Franke and Daniel Dvorak. 1989. <B>Component-connection models.</B>Model-Based Reasoning Workshop, IJCAI-89, Detroit, Michigan, August 1989.</LI><P>[Superceded by <!WA78><!WA78><!WA78><!WA78><!WA78><!WA78><!WA78><!WA78><!WA78><!WA78><!WA78><!WA78><A HREF="http://www.cs.utexas.edu/users/qr/QR-book.html">QR book</A>, chapter 13.] </P></UL><P><HR></P><H3><A NAME="QPC"></A>QPC </H3><UL><LI>J. M. Crawford, A. Farquhar, B. J. Kuipers. 1990. <B><!WA79><!WA79><!WA79><!WA79><!WA79><!WA79><!WA79><!WA79><!WA79><!WA79><!WA79><!WA79><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Crawford+Farquhar+Kuipers-AAAI90.ps.Z">QPC:a compiler from physical models into qualitative differential equations.</A></B><I>Proceedings of the National Conference on Artificial Intelligence (AAAI-90),</I>AAAI/MIT Press, 1990. </LI><P>Revised version in Boi Faltings and Peter Struss (Eds.), <I>Recent Advancesin Qualitative Physics</I>, MIT Press, 1992. </P><P>[Superceded by <!WA80><!WA80><!WA80><!WA80><!WA80><!WA80><!WA80><!WA80><!WA80><!WA80><!WA80><!WA80><A HREF="http://www.cs.utexas.edu/users/qr/QR-book.html">QR book</A>, chapter 14.] </P><LI>Adam Farquhar. 1993. <B><!WA81><!WA81><!WA81><!WA81><!WA81><!WA81><!WA81><!WA81><!WA81><!WA81><!WA81><!WA81><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Farquhar-PhD-93.ps.Z">Automatedmodeling of physical systems in the presence of incomplete knowledge.</A></B>University of Texas at Austin, Artificial Intelligence Laboratory, TechnicalReport AI 93-207. (Doctoral dissertation, Department of Computer Sciences.)</LI><LI>Adam Farquhar. 1994. <B><!WA82><!WA82><!WA82><!WA82><!WA82><!WA82><!WA82><!WA82><!WA82><!WA82><!WA82><!WA82><A HREF="file://ftp.cs.utexas.edu/pub/qsim/papers/Farquhar-AAAI94.ps.Z">Aqualitative physics compiler.</A></B> In <I>Proceedings of the NationalConference on Artificial Intelligence (AAAI-94),</I> AAAI/MIT Press, 1994.</LI>

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