David G. Mitchell - Papers Etc.
Selected Publications, Notes, Talks, etc
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Declarative Programming of Search Problems with Built-in Arithmetic.
Eugenia Ternovska and David G. Mitchell.
IJCAI 2009
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Modelling Languages, Model Expansion, and MXG.
Invited Talk,
WOG Seminar on Logic and Computation, April 29, 2008.
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Expressiveness and Abstraction in Essence.
David G. Mitchell and Eugenia Ternovska.
Constraints, Volume 13, Issue 3, pages 343-384.
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An MX-based Front End for SAT.
Invited talk, NSF Workshop on the status and future of SAT, March 2008.
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Solving NP Search Problems with Model Expansion.
Faraz Hach, MSc. Thesis, 2007.
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A Method for Solving NP Search Based on Model Expansion and Grounding.
Raheleh Mohebali, MSc. Thesis, 2007.
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MXG: A Model Expansion Grounder and Solver.
Raheleh Mohebali, Faraz Hach and David Mitchell.
Accepted as an LPAR-2007 short paper.
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On The Expressive Power of Essence.
David Mitchell and Eugenia Ternovska.
ModRef-07
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Faster Phylogenetic Inference with MXG.
David G. Mitchell, Faraz Hach, Raheleh Mohebali.
Proc. of LPAR-2007: LNCS 4790, pp 423--437
Bibtex
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Minimum 2CNF Resolution Refutations in Polynomial Time.
Joshua Buresh-Oppenheim and David Mitchell.
In: Proc., SAT-2007
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The SAT Solver MXC, Version 0.5
David R. Bregman and David G. Mitchell.
Solver Description for the 2007 SAT Solver Competition.
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Model Expansion as a Framework for Modelling and Solving Search Problems.
David Mitchell, Eugenia Ternovska, Faraz Hach, Raheleh Mohebali.
SFU Computing Science Technical Report TR-2006-24.
Bibtex Entry
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Constructing Cladistic Camin-Sokal Phylogeny Trees Via Answer Set Programming.
Jonathan Kavanagh, David Mitchell, Eugenia Ternovska, Xiaohong Zhao, Jano Manuch, Arvind Gupta.
Proc. of LPAR-2006, LNCS 4246, pp 452-466.
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Modelling with FO(ID), Solving with SAT.
David Mitchell and Eugenia Ternovska.
Talk from the Workshop on Constraints and Verification, at the
Isaac Newton Institute, Cambridge, May 2006.
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Minimum Witnesses for Unsatisfiable 2-CNFs.
Joshua Buresh-Oppenheim and David Mitchell.
SAT-2006
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The Resolution Complexity of Random Graph $k$-colorability.
Paul Beame, Joseph Culberson, David Mitchell and Christopher Moore.
Discrete Applied Mathematics Volume 153, Issues 1-3 , 1 December 2005, Pages 25-47.
(Also ECCC Report TR04-012.)
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The SAT Solver MXC, Version 0.1
David R. Bregman and David G. Mitchell.
Solver Description for SAT Race 2006 Solver Competition.
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Constraint Programming with Unrestricted Quantification.
David G. Mitchell and Eugenia Ternovska,
First International Workshop on Quantification in Constraint Programming (CP-2005 Workshop)
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2-way vs. d-way Branching for CSP.
Joey Hwang and David G. Mitchell.
In: Principles and Practices of Constraint Programming - CP 2005,
Proceedings (Springer, LNCS-????).
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A Framework for Representing and Solving NP Search Problems.
David G. Mitchell and Eugenia Ternovska,
In: Proc., Twentieth National Conf. on Artificial Intelligence (AAAI-05),
Pittsburgh, PA, July 2005, AAAI Press/MIT Press, pages 430-435.
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A SAT Solver Primer.
David G. Mitchell,
EATCS Bulletin (The Logic in Computer Science Column),
Volume 85, February 2005, pages 112-133.
(Citations and Uses as Course Readings)
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A Theoretical Comparison of Resolution Systems for CSP Algorithms.
Joey Hwang MSc. Thesis, 2004.
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Efficient Algorithms for Clause Learning SAT Solvers.
Lawrence Ryan MSc. Thesis, 2004.
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Resolution and Constraint Satisfaction,
David G. Mitchell.
In: Principles and Practices of Constraint Programming - CP 2003,
Proceedings (Springer, LNCS-2833).
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Resolution Complexity of Random Constraints,
David G. Mitchell.
In: Principles and Practices of Constraint Programming - CP 2002,
Proceedings (Springer, LNCS-2470).
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Hard Problems for CSP Algorithms,
David G. Mitchell.
In: Proc., Fifteenth National Conf. on Artificial Intelligence,
Madison WI, July 1998, AAAI Press/MIT Press.
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Finding Hard Instances of the Satisfiability Problem: A Survey,
Stephen A. Cook and David G. Mitchell,
In: Satisfiability Problem: Theory and Applications, Du, Gu and Pardalos (Eds).
DIMACS Series in Discrete Mathamatics and Theoretical Computer Science, Volume 35, 1997, pages 1-17.
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Generating Hard Satisfiability Problems,
Bart Selman, David G. Mitchell and Hector J. Levesque,
Artificial Intelligence, Volume 81(1-2), March 1996, pages 17-29.
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Some Pitfalls for Experimenters with Random SAT,
David G. Mitchell and Hector J. Levesque,
Artificial Intelligence, Volume 81(1-2), March 1996, pages 111-125.
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A New Method for Solving Hard Satisfiability Problems,
Bart Selman, Hector J. Levesque and David G. Mitchell,
Proc., Tenth National Conf. on Artificial Intelligence
,San Jose, CA, July 1992, AAAI Press/MIT Press, pages 440-446.
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Hard and Easy Distributions of SAT Problems,
David G. Mitchell, Bart Selman and Hector J. Levesque,
Proc., Tenth National Conf. on Artificial Intelligence
,San Jose, CA, July 1992, AAAI Press/MIT Press, pages 459-465.
Theses
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The Resolution Complexity of Constraint Satisfaction,
Ph.D. Thesis, University of Toronto, 2002.
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An Empirical Study of Random SAT,
M.Sc. Thesis, Simon Fraser University, 1993.
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