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Automatic Problem Solving
Vortragsthemen & Literaturreferenzen
Die Tabelle auf der nächsten Seite gibt einen Überblick über die vorgesehenen Themen in der chronologischen Reihenfolge der Vorträge. Die Literaturhinweise sind als Einstiegspunkte in die jeweilige Thematik gedacht,
darüber hinausgehende eigene Recherche ist möglich und erwünscht. Die
Anzahl der Bearbeiter eines Themas sollte sich innerhalb der vorgegeben
Schranken bewegen. Bei Themen, die von zwei Studenten bearbeitet werden,
müssen beide einen gemeinsamen Vortrag halten. Wie ein Thema innerhalb
einer Zweiergruppe aufgeteilt wird, ist der Gruppe überlassen. Bei Themen,
deren Bearbeiterzahl mit 1–2 gekennzeichnet ist, bestimmt die letztendliche
Gruppenstärke den Tiefgang, mit dem auf das Thema eingegangen werden
muß. Die Berarbeitung der Themen 1 bis 9 ist obligatorisch. Erst wenn
diese Themen besetzt sind, beginnt die Vergabe der Themen 10 bis 17. Der
Modus der Vergabe von Themen wird in der ersten Veranstaltung am 19.10.
besprochen.
(Provisorische) Vortragsfolien müssen in der Woche, bevor ein Vortrag
gehalten werden soll, vorgelegt und mit einem jeweils verantwortlichen Betreuer besprochen werden. Zum Datum des Vortrags ist eine kurze Zusammenfassung als Handout (eine DIN-A4-Seite) zur Verfügung zu stellen. Am
Ende des Semesters ist eine Ausarbeitung von 5 bis 10 Seiten auf elektronischem Weg abzugeben.
Nr.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
Thema
Quellen
Bearbeiter Obligatorisch
SAT-Problem und DPLL
[DP60, DLL62, MMZ+ 01]
Komplexität und Phasenübergänge
[Coo71, GW94, ML96]
Clause Learning
[ZMMM01, Mit05]
Restarts
[GSCK00, GSK98, BMS00]
Heuristiken
[LA97, MMZ+ 01, GN02, Rya04]
Mini-SAT
[ES03]
Vollständige SAT-Solver
[LA97, BS96, MSS99, Zha97, MMZ+ 01, GN02, Rya04]
Unvollständige SAT-Solver
[SLM92, SKC94, HK05]
Parallele SAT-Solver
[JLU01, SBK01, CW03, FS02]
SAT-Competitions
[SAT, SBH05, BS04]
SATPlan/Blackbox
[KS92, KS96, KS99]
CCalc
[McC97, GLL+ 04]
Model Checking
[Mod, BCM+ 90, BCCZ99, VB01, CCG+ 02]
MinSAT/MaxSAT
[KKM94, Yan94]
Boolean Circuits
[JJN05]
Antwortmengen
[LZ04, LM04]
Quantified Boolean Formulas
[CKS81, Wil02]
1
1
1–2
1–2
1–2
1
2
1
1–2
1
1
1
1
1
1
1
1
X
X
X
X
X
X
X
X
X
References
[BCCZ99]
A. Biere, A. Cimatti, E.M. Clarke, and Y. Zhu. Symbolic model
checking without BDDs. In Proceedings of the Fifth International
Conference on Tools and Algorithms for the Analysis and Construction of Systems (TACAS’99), volume 1579 of Lecture Notes
in Computer Science, pages 193–207. Springer-Verlag, 1999. 2
[BCM+ 90] J. Burch, E. Clarke, K. McMillan, D. Dill, and L. Hwang. Symbolic model checking: 1020 states and beyond. In Proceedings of
the Fifth Annual IEEE Symposium on Logic in Computer Science, pages 1–33, 1990. 2
[BMS00]
L. Baptista and J. Marques-Silva. Using randomization and
learning to solve hard real-world instances of satisfiability. In
Proceedings of the 6th International Conference on Principles
and Practice of Constraint Programming (CP’00), pages 489–
494, 2000. 2
[BS96]
R. Bayardo and R. Schrag. Using csp look-back techniques to
solve exceptionally hard sat instances. In Proceedings of the
2nd International Conference on Principles and Practice of Constraint Programming (CP’96), pages 46–60, 1996. 2
[BS04]
D. Le Berre and L. Simon. Fifty-five solvers in vancouver: The
sat 2004 competition. In Proceedings of the 7th International
Conference on Theory and Applications of Satisfiability Testing (SAT’04), Lecture Notes in Artificial Intelligence, to appear
2004. 2
[CCG+ 02]
A. Cimatti, E. Clarke, E. Giunchiglia, F. Giunchiglia, M. Pistore, M. Roveri, R. Sebastiani, and A. Tacchella. Nusmv 2: An
opensource tool for symbolic model checking. In Proceedings of
the 14th International Conference on Computer Aided Verification (CAV’02), pages 359–364. Springer-Verlag, 2002. 2
[CKS81]
A. Chandra, D. Kozen, and L. Stockmeyer. Alternation. Journal
of the ACM, 28(1):114–133, 1981. 2
[Coo71]
S. Cook. The complexity of theorem-proving procedures. In
Proceedings of the Third Annual ACM Symposium on Theory of
Computing, pages 151–158, 1971. 2
[CW03]
W. Chrabakh and R. Wolski. Gridsat: A chaff-based distributed
sat solver for the grid. In Proceedings of the ACM/IEEE SC2003
Conference on High Performance Networking and Computing,
page 37, 2003. 2
[DLL62]
M. Davis, G. Logemann, and D. Loveland. A machine program
for theorem-proving. Communications of the ACM, 5:394–397,
1962. 2
[DP60]
M. Davis and H. Putnam. A computing procedure for quantification theory. Journal of the ACM, 7:201–215, 1960. 2
[ES03]
N. Eén and N. Sörensson. An extensible sat-solver. In Proceedings of the 6th International Conference on Theory and Applications of Satisfiability Testing (SAT’03), pages 502–518, 2003.
2
[FS02]
S. Forman and A. Segre. Nagsat: A randomized, complete, parallel solver for 3-sat. In Proceedings of the 6th International
Conference on Theory and Applications of Satisfiability Testing
(SAT’01), 2002. 2
[GLL+ 04]
E. Giunchiglia, J. Lee, V. Lifschitz, N. McCain, and H. Turner.
Nonmonotonic causal theories. Artificial Intelligence, 153(12):49–104, 2004. 2
[GN02]
E. Goldberg and Y. Novikov. Berkmin: A fast and robust sat
solver. In Proceedings of the 5th Conference on Design, Automation and Test in Europe (DATE’02), pages 142–149, 2002. 2
[GSCK00]
C. Gomes, B. Selman, N. Crato, and H. Kautz. Heavy-tailed
phenomena in satisfiability and constraint satisfaction problems.
Journal of Automated Reasoning, 24(1-2):67–100, 2000. 2
[GSK98]
C. Gomes, B. Selman, and H. Kautz. Boosting combinatorial
search through randomization. In Proceedings of the 15th National Conference on Artificial Intelligence (AAAI’98), pages
431–437, 1998. 2
[GW94]
I. Gent and T. Walsh. The sat phase transition. In Proceedings of the 11th European Conference on Artificial Intelligence
(ECAI’94), pages 105–109, 1994. 2
[HK05]
E. Hirsch and A. Kojevnikov. Unitwalk: A new sat solver that
uses local search guided by unit clause elimination. Annals of
Mathematics and Artificial Intelligence, 43(1):91–111, 2005. 2
[JJN05]
M. Järvisalo, T. Junttila, and I. Niemelä. Unrestricted vs restricted cut in a tableau method for boolean circuits. Annals of
Mathematics and Artificial Intelligence, 44(4):373–399, 2005. 2
[JLU01]
B. Jurkowiak, C. M. Li, and G. Utard. Parallelizing satz using
dynamic workload balancing. In Proceedings of the 5th International Conference on Theory and Applications of Satisfiability
Testing (SAT’01), 2001. 2
[KKM94]
R. Kohli, R. Krishnamurti, and P. Mirchandani. The minimum
satisfiability problem. SIAM Journal on Discrete Mathematics,
7(2):275–283, 1994. 2
[KS92]
H. Kautz and B. Selman. Planning as satisfiability. In Proceedings of the 10th European Conference on Artificial Intelligence
(ECAI’92), pages 359–363, 1992. 2
[KS96]
H. Kautz and B. Selman. Pushing the envelope: Planning,
propositional logic and stochastic search. In Proceedings of the
13th National Conference on Artificial Intelligence (AAAI’96),
pages 1194–1201, 1996. 2
[KS99]
H. Kautz and B. Selman. Unifying sat-based and graph-based
planning. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI’99), pages 318–325, 1999.
2
[LA97]
C. Li and Anbulagan. Heuristics based on unit propagation for
satisfiability problems. In Proceedings of the 15th International
Joint Conference on Artificial Intelligence (IJCAI’97), pages
366–371, 1997. 2
[LM04]
Y. Lierler and M. Maratea. Cmodels-2: Sat-based answer sets
solver enhanced to non-tight programs. In Proceedings of the 7th
International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’04), pages 346–350, 2004. 2
[LZ04]
F. Lin and Y. Zhao. Assat: computing answer sets of a logic
program by sat solvers. Artificial Intelligence, 157(1-2):115–137,
2004. 2
[McC97]
N. McCain. Causality in commonsense reasoning about actions.
PhD thesis, University of Texas, 1997. 2
[Mit05]
D. Mitchell. A sat solver primer. Bulletin of the European Association for Theoretical Computer Science, 85:112–133, 2005.
2
[ML96]
D. Mitchell and H. Levesque. Some pitfalls for experimenters
with random sat. Artificial Intelligence, 81(1-2):111–125, 1996.
2
[MMZ+ 01] M. Moskewicz, C. Madigan, Y. Zhao, L. Zhang, and S. Malik.
Chaff: Engineering an efficient sat solver. In Proceedings of the
38th Conference on Design Automation (DAC’01), pages 530–
535, 2001. 2
[Mod]
http://www.cs.cmu.edu/~modelcheck/pubs.htm. 2
[MSS99]
J. Marques-Silva and K. Sakallah. Grasp: A search algorithm for
propositional satisfiability. IEEE Transactions on Computers,
48(5):506–521, 1999. 2
[Rya04]
L. Ryan. Efficient algorithms for clause-learning sat solvers.
Master’s thesis, Simon Fraser University, 2004. 2
[SAT]
http://www.satcompetition.org/. 2
[SBH05]
L. Simon, D. Le Berre, and E. Hirsch. The sat 2002 competition.
Annals of Mathematics and Artificial Intelligence, 43(1):307–
342, 2005. 2
[SBK01]
C. Sinz, W. Blochinger, and W. Kuchlin. Pasat - parallel satchecking with lemma exchange: Implementation and applications. In Proceedings of the 5th International Conference on
Theory and Applications of Satisfiability Testing (SAT’01), 2001.
2
[SKC94]
B. Selman, H. Kautz, and B. Cohen. Noise strategies for improving local search. In Proceedings of the 12th National Conference
on Artificial Intelligence (AAAI’94), pages 337–343, 1994. 2
[SLM92]
B. Selman, H. Levesque, and D. Mitchell. A new method for
solving hard satisfiability problems. In Proceedings of the 10th
National Conference on Artificial Intelligence (AAAI’92), pages
440–446, 1992. 2
[VB01]
M. Velev and R. Bryant. Effective use of boolean satisfiability
procedures in the formal verification of superscalar and vliw microprocessors. In Proceedings of the 38th Conference on Design
Automation (DAC’01), pages 226–231, 2001. 2
[Wil02]
R. Williams. Algorithms for quantified boolean formulas. In Proceedings of the 13th Annual ACM-SIAM Symposium on Discrete
algorithms (SODA ’02), pages 299–307, 2002. 2
[Yan94]
M. Yannakakis. On the approximation of maximum satisfiability.
In Selected Papers from the Third Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’94), pages 475–502, 1994.
2
[Zha97]
H. Zhang. Sato: an efficient propositional prover. In Proceedings
of the 14th International Conference on Automated Deduction
(CADE’97), pages 272–275, 1997. 2
[ZMMM01] L. Zhang, C. Madigan, M. Moskewicz, and S. Malik. Efficient
conflict driven learning in a boolean satisfiability solver. In Proceedings of the IEEE/ACM International Conference on Computer Aided Design (ICCAD’01), pages 279–285, 2001. 2