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12      Bibliografia e altri documenti riguardanti la sodisfacibilita proposizionale

1.         Michael N. Barber. Finite­size scaling. In Phase Transitions and Critical Phenomena, Volume 8, pages 145-266. Academic Press, 1983

2.         P. Barth. A Davis­Putnam based enumeration algorithm for linear pseudoboolean  optimization. Research report mpi­i­95­2­003, Max Plack Institut fur Informatik,  Saarbrucken, 1995

3.         R. Battiti and M. Protasi. Reactive Search, a history based heuristic for MAX­SAT. Technical report, Dipartimento di Matematica, Univ. of Trento, Italy

4.         Roberto Bayardo and Robert Schrag. Using CSP look­back techniques to solve exceptionally hard SAT instances. In Proceedings of Second International  Conference on Principles and Practice of Constraint Programming (CP96), 1996

5.         R. J. Bayardo and D. P. Miranker. A Complexity Analysis of Space­Bounded Learning Algorithms for the Constraint Satisfaction Problem. In Proceedings 13th National Conference on Artificial Intelligence, 558­562, 1996

6.         Roberto Bayardo and Robert Schrag. Using CSP look­back techniques  to solve real­world SAT instances. In Proceedings of the 14th National Conference on AI. American Association for Artificial Intelligence, 1997

7.         R. Beigel and D. Eppstein. 3­coloring in time O(1.3446n): a No­MIS  algorithm. Technical report, ECCC, 1995. TR95­33

8.         R.E. Bryant. Symbolic Boolean manipulation with ordered binary­decision diagrams. ACM Computing Surveys, 24(3):293-318, 1992

9.         M. Buro and H. Buning. Report on a SAT competition. Technical Report, Dept. of Mathematics and Informatics, University of Paderborn, Germany, 1992

10.      M. Cadoli, A. Giovanardi, and M. Schaerf. Experimental analysis of the computational cost of evaluation quantified Boolean formulae. In Proceedings of the AI*IA­97, pages 207-218. Springer­Verlag, LNAI­1321, 1997

11.      P. Cheeseman, B. Kanefsky, and W.M. Taylor. Where the really hard problems are. In Proceedings of the 12th IJCAI, pages 331-337. International  Joint Conference on Artificial Intelligence, 1991

12.      V. Chvatal and B. Reed. Mick gets some (the odds are on his side). In Proceedings of the 33rd Annual Symposium on Foundations of Computer Science, pages 620-627. IEEE, 1992

13.      S.A. Cook. The complexity of theorem proving procedures. In Proceedings of the 3rd Annual ACM Symposium on the Theory of Computation, pages  151-158, 1971

14.      S.A. Cook and D.G. Mitchell. Finding hard instances of the satisfiability problem: A survey. In Satisfiability Problem: Theory and Applications.  Dimacs Series in Discrete Mathematics and Theoretical Computer Science, Volume 35, 1997

15.      J.M. Crawford and L.D. Auton. Experimental Results on the Cross­Over Point in Satisfiability Problemes. In Proceedings of AAAI 1993 Spring Symposium on AI and NP­Hard Problems, 1993

16.      J.M. Crawford and L.D. Auton. Experimental results on the crossover point in random 3­SAT. Artificial Intelligence, 81:31-57, 1996

17.      J.M. Crawford and A.D. Baker. Experimental Results on the Application of Satisfiability Algorithms to Scheduling Problems. In Proceedings of 12th National Conference on AI, pages 1092--1097. American Association for Artificial Intelligence, 1994

18.      Marcello D'Agostino. Are tableaux an improvement on truth­tables? Journal of Logic, Language and Information

19.      M. Davis, G. Logemann, and D. Loveland. A machine program for theorem proving. Comms. ACM, 5:394-397, 1962

20.      M. Davis and H. Putnam. A computing procedure for quantification theory. Journal of the Association for Computing Machinery, pages 201-215, 1960

21.      R. Dechter and I. Rish. Directional resolution: The Davis­Putnam procedure, revisited. In Proceedings of KR­94, pages 134-145, 1994. A longer  version is available from http://www.ics.uci.edu/irinar

22.      O. Dubois, P. Andre, Y. Boufkhad, and J. Carlier. SAT versus UNSAT. In Proceedings of the Second DIMACS Challenge, 1993

23.      K. A. Dowsland. Simulated annealing. In Modern Heuristic Techniques for Combinatorial Problems,C.R. Reeves (E.), John Wiley & Sons, 20-69, 1988

24.      G.W. Ernst. A definition­driven theorem prover. In Proceedings of the 3rd IJCAI, pages 51-55. International Joint Conference on Artificial Intelligence, 1973

25.      J. Frank. Learning short term weights for GSAT. In Proceedings of the 14th IJCAI. International Joint Conference on Artificial Intelligence, 1995

26.      J. W. Freeman. Improvements to Propositional Satisfiability Search Algorithms. Ph.D. Dissertation, U. Pennsylvania Dept. of Computer and Information Science, 1995

27.      E. Friedgut. Sharp thresholds for graph properties and the k­SAT problem, 1998

28.      Frieze and S. Suen. Analysis of two simple heuristics on a random instance of k­SAT. Journal of Algorithms, 20:312-355, 1996

29.      D. Frost, I. Rish, and L. Vila. Summarizing CSP hardness with continuous probability distributions. In Proceedings of the 14th National Conferenceon AI, pages 327--333. American Association for Artificial Intelligence, 1997

30.      Masayuki Fujita, John Slaney and Frank Bennett. Automatic generation of some results in finite algebra. In Proceedings of the 13th IJCAI, pages 52-57. International Joint Conference on Artificial Intelligence, 1993

31.      M.R. Garey and D.S. Johnson. Computers and intractability: a guide to the theory of NP­completeness. W.H. Freeman, 1979

32.      I.P. Gent. On the stupid algorithm for satisfiability. Technical report, APES­03­1998, 1998, available from http://www.cs.strath.ac.uk/apes/reports/apes­03­1998.ps.gz

33.      I.P.Gent, E. MacIntyre, P. Prosser, and T. Walsh. The constrainedness of search. In Proceedings of AAAI­96, pages 246-252, 1996

34.      I.P. Gent, E. MacIntyre, P. Prosser, and T. Walsh. The scaling of search cost. In Proceedings of the 14th National Conference on AI, pages 315-320.  American Association for Artificial Intelligence, 1997

35.      Gent and T. Walsh. The Enigma of SAT Hill­climbing Procedures. Technical Report 605, Dept. of Artificial Intelligence, University of Edinburgh, 1992

36.      Gent and T. Walsh. Towards an Understanding of Hill­climbing Procedures for SAT. In Proceedings of the 11th National Conference on AI.American Association for Artificial Intelligence, 1993

37.      I.P. Gent and T. Walsh. An empirical analysis of search in GSAT. Journal of Artificial Intelligence Research, 1:23-57, 1993

38.      I.P. Gent and T. Walsh. Easy problems are sometimes hard. Artificial Intelligence, pages 335-345, 1994

39.      I.P.Gent and T. Walsh. The hardest random SAT problems. In Proceedings of KI­94, Saarbrucken, 1994

40.      I.P. Gent and T. Walsh. The SAT phase transition. In A G Cohn, editor, Proceedings of 11th ECAI, pages 105-109. John Wiley & Sons, 1994

41.      I.P. Gent and T. Walsh. Unsatisfied variables in local search. Research Paper 721, Dept. of Artificial Intelligence, University of Edinburgh, 1994. Presented at AISB­95

42.      I.P. Gent and T. Walsh. The satisfiability constraint gap. Artificial  Intelligence, 81(1-2), 1996

43.      I.P. Gent and T. Walsh. Beyond np: the QSAT phase transition. Technical report APES­05­1998, 1998, available from http://www.cs.strath.ac.uk/apes/reports/apes­05­1998.ps.gz

44.      I.P. Gent and T. Walsh. The Search for Satisfaction, Department of Computer Science, University of Strathclyde, Glasgow, 1999

45.      M. Ginsberg and D. McAllester. GSAT and Dynamic Backtracking, Principles of Knowledge Representation and Reasoning: Proceedings of the Fourth International Conference, 226­237, 1994

46.      F. Giunchiglia and R. Sebastiani. Building decision procedures for modal logics from propositional decision procedures ­ the case study of modal k. In Proceedings of the 13th International Conference on Automated Deduction  (CADE­13). Springer Verlag, 1996

47.      F. Giunchiglia and R. Sebastiani. A SAT­based decision procedure for alc. In Proceedings of the 5th International Conference on Principles of Knowledge. Representation and Reasoning ­ KR'96, 1996

48.      F. Giunchiglia and R. Sebastiani. A new method for testing decision procedures in modal logics. In Proceedings of the 14th International Conference on Automated Deduction (CADE­14). Springer Verlag, 1997

49.      P.Y. Gloess. U­Log - a Unified Object Logic, Revue d'intelligence artificielle, Vol. 5, No. 3, pp. 33­-66, 1991

50.      F. Glover. Tabu Search - Part I. ORSA Journal on Computing, 1(3):190-206, 1989

51.      F. Glover. Future path for integer programming and links to artificial intelligence. Computers & Ops. Res. , 5:533-549, 1993

52.      F. Glover and M. Laguna. Tabu search. In Modern Heuristic Techniques for Combinatorial Problems, C.R. Reeves (E.), John Wiley & Sons, 70-150, 1996.

53.      Goerdt. A threshold for unsatisfiability. In I. Havel and V. Koubek editors, Mathematical Foundations of Computer Science, Lecture Notes in Computer Science, pages 264-274. Springer Verlag, 1992

54.      Gomes, B. Selman, and H. Kautz. Boosting combinatorial search through randomization. In Proceedings of 15th National Conference on Artificial Intelligence, pages 431-437. AAAI Press - The MIT Press, 1998

55.      J. Gu. Efficient local search for very large­scale satisfiability problems. SIGART Bulletin, 3(1):8-12, 1992

56.      J. Gu. Global Optimisation for satisfiability (SAT) problem. IEEE Transactions on Data and Knowledge Engineering, 6(3):361-381,1994

57.      J. Hansen and B. Jaumard. Algorithms for the maximum satisfiability problem. Computing, 44, 279-303, 1990

58.      P.Hayes. The naive physics manifesto. In D. Michie, editor, Expert Systems in the Microelectronic Age. Edinburgh University Press, 1979

59.      A. Hertz and D. De Werra. Using tabu search techniques for Graph Coloring. Computing, pages 345-351, 1987

60.      T. Hogg. Quantum computing and phase transitions in combinatorial search. Journal of Artificial Intelligence Research, 4:91-128, 1996

61.      J. N. Hooker. Resolution vs. cutting plane solution of inference problems: some computational experience. Operations Research Letters, 7(1):1-7, 1988

62.      J. N. Hooker and C. Fedjki. Branch­and­cut solution of inference problems in propositional logic. Annals of Mathematics and Artificial Intelligence, 1:123--139, 1990

63.      J.N. Hooker. Branch­and­cut solution of inference problems in propositional logic. Annals of Mathematics and Artificial Intelligence, 1:123-139, 1990

64.      J.N. Hooker. Solving the incremental satisfiability problem. Journal of Logic Programming, 15:177-186, 1993

65.      J.N. Hooker and V. Vinay. Branching Rules for Satisfiability. Journal of Automated Reasoning, 15:359-383, 1995

66.      H. Hoos. Stochastic Local Search ­ Methods, Models, Applications. PhD thesis, TU Darmstadt, 1998. Available from  http://www.cs.ubc.ca/spider/hoos/publ­ai.html

67.      H. Hoos and T. Stutzle. Evaluating Las Vegas algorithms ­ pitfalls and remedies. In Proceedings of 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI­98), 1998. Available from  http://www.sis.pitt.edu/~dsl/uai98.html

68.      R. E. Jeroslow and J. Wang. Solving satisfiability propositional problems. Annals of Mathematics and Artificial Intelligence, 1:167-187, 1990

69.      D.S. Johnson. Optimization algorithms for combinatorial problems. J. of Comp. and Sys. Sci., 9:256-279, 1974

70.      D.S. Johnson, C.R. Aragon, L.A. McGeoch and C. Schevon.Optimization by simulated annealing: an experimental evaluation; part II, Graph Coloring and number partioning. Operations Research, 39(3):378-406, 1991

71.      Y. Jiang, H. Kautz, and B. Selman. Solving problems with hard and soft constraints using a stochastic algorithm for MAX­SAT. In First International  Joint Workshop on Artificial Intelligence and Operations Research, 1995

72.      K.A. De Jong and W.M. Spears. Using Genetic Algorithms to Solve NP-Complete Problems. In Third International Conference on Genetic Algorithms. Morgan Kaufmann, 1989

73.      A.P. Kamath, N.K. Karmarkar, K.G. Ramakrishnan and M.G.C. Resende.A continuous approach to inductive inference. Mathematical Programming, 57, 215-238, 1991

74.      A.P. Kamath, R. Motwani, K. Palem, and P. Spirakis. Tail bounds for occupancy and the satisfiability threshold conjecture. Randomized Structure  and Algorithms, 7:59-80, 1995

75.      A.P. Kamath, N.K. Karmarkar, K.G. Ramakrishnan, and M.G.C. Resende. An interior point approach to Boolean vector function synthesis. In Proceedings of the 36th MSCAS, 1993

76.      K. Kask and R. Dechter. GSAT and local consistency. In Proceedings of the 14th IJCAI, pages 616-622. International Joint Conference on Artificial Intelligence, 1995

77.      H. Kautz and B. Selman. Planning as Satisfiability. In Proceedings of the 10th ECAI, pages 359-363. European Conference on Artificial Intelligence, 1992

78.      H. Kautz and B. Selman. Pushing the envelope: planning, propositional logic, and stochastic search. In Proceedings of the 13th National Conference on AI, pages 1194-1201. American Association for Artificial Intelligence, 1996

79.      H. Kautz and B. Selman. BLACKBOX: A new approach to the application of theorem proving to problem solving. In Working notes of the Workshop on Planning as Combinatorial Search, 1998. Held in conjunction with AIPS98, Pittsburgh, PA, 1998

80.      H. Kautz and B. Selman. The role of domain­specific knowledge in the planning as satisfiability framework. In Proceedings of AIPS­98, Pittsburgh,  PA, 1998

81.      S. Kim, H. Zhang. ModGen: Theorem proving by model generation, Proceedings of the 12th National Conference on Artificial Intelligence (AAAI­94), pp. 162-­167, 1994

82.      S. Kirkpatrick, C.D. Gelatt Jr., and M.P.Vecchi. Optimisation by Simulated Annealing. Science, 220:671-680, 1983

83.      S. Kirkpatrick and B. Selman. Criticial comportamento in the satisfiability of random boolean expressions. Science, 264:1297-1301, 1994

84.      L.M. Kirousis, E. Kranakis, and D. Krizanc. Approximating the unsatisfiability threshold of random formulas. In Proceedings of the 4th Annual European Symposium on Algorithms (ESA'96), pages 27-38, 1996

85.      E. Koutsoupias and C.H. Papadimitriou. On the greedy algorithm for satisfiability. Information Processing Letters, 43:53-55, 1992

86.      T. Larrabee. Test pattern generation using Boolean satisfiability. IEEE Transactions on Computer­Aided Design, pages 4-15, 1992

87.      C.M. Li, Exploiting yet more the power of unit clause propagation to solve 3­SAT problem, Proceedings of the ECAI'96 Workshop on Advances in propositional deduction, Budapest, Hungary, pp. 11­16, August 1996

88.      C.M. Li and Anbulagan. Heuristics based on unit propagation for satisfiability problems. In Proceedings of the 15th IJCAI, pages 366-371. International Joint Conference on Artificial Intelligence, 1997

89.      C.M. Li and Anbulagan, Look-ahead versus look-back for satisfiability problems, in preceedings of third international conference on Principles and Practice of Constraint Programming - CP97, Springer-Verlag, LNCS 1330, Page 342-356, Autriche, 1997.

90.      R.J. Lipton. Using DNA to solve NP­complete problems. Science, 268:542-545, 1995

91.      M. Mazure, L. Sais and E. Gregoire. Detecting Logical Inconsistencies. In Proc. of the Fourth International Symposium on Artificial Intelligence and Mathematics, 116­121, 1996

92.      M. Mazure, L. Sais, and E. Gregoire. Tabu search for SAT. In Proceedings of 14th National Conference on Artificial Intelligence, pages 281-285. American  Association for Artificial Intelligence, AAAI Press/The MIT Press, 1997

93.      McAllester, B. Selman, and H. Kautz. Evidence for invariants in local search. In Proceedings of the 14th National Conference on AI, pages 321-327. American Association for Artificial Intelligence, 1997

94.      W. W. McCune. Otter user’s guide 0.91. Maths and CS Division, Argonne National Laboratory, Argonne, Illinois, 1988

95.      P. Meseguer and T. Walsh. Interleaved and discrepancy based search. In Proceedings of the 13th ECAI. European Conference on Artificial Intelligence, Wiley, 1998

96.      S. Minton, M.D. Johnston, A.B. Philips, and P. Laird. Solving large­scale constraint satisfaction and scheduling problems using a heuristic repair method. In Proceedings of AAAI'90, pages 17-24, 1990

97.      S. Minton, M.D. Johnston, A.B. Philips, and P. Laird. Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problems. Artificial Intelligence, 52:161-205, 1992

98.      D. Mitchell, B. Selman, and H. Levesque. Hard and Easy Distributions of SAT Problems. In Proceedings of the 10th National Conference on AI, pages 459-465. American Association for Artificial Intelligence, 1992

99.      P. Morris. The Breakout method for escaping from local minima. In Proceedings of the 11th National Conference on AI. American Association for Artificial Intelligence, 1993

100.   Jens Otten. On the Advantage of a Non­Clausal Davis­Putnam Procedure. Technical Report AIDA-97-01, FG Intellektik, FB Informatik, TH Darmstadt, January 1997

101.   C.H. Papadimitriou and K. Steiglitz. Combinatorial optimization. Englewood Cliffs, NJ: Prentice­Hall Inc, 1982

102.   C.H. Papadimitriou. On selecting a satisfying truth assigment. In Proceedings of the Conference on the Foundations of Computer Science, pages  163-169, 1991

103.   C.H. Papadimitriou. Computational Complexity. Addison­Wesley, 1994

104.   P. Pandurang Nayak and B.C. Williams. Fast context switching in real­time propositional reasoning. In Proceedings of the 14th National Conference on AI. American Association for Artificial Intelligence, 1997

105.   A.J. Parkes and J.P. Walser. Tuning local search for satisfiability testing. In Proceedings of AAAI­96, pages 356-362, 1996

106.   D. Pretolani. Satisfiability and hypergraphs, Ph.D. Thesis, Dipartimento di Informatica, Universita di Pisa, 1993

107.   P. Prosser. Hybrid Algorithms for the Constraint Satisfaction Problem. Computational Intelligence 9(3):268­299, 1993

108.   Paul Walton Purdom Jr. and Cynthia A. Brown. The pure literal rule and polynomial average time. SIAM Journal of Computing, 14(4):943--953,  November 1985

109.   R. Reiter and A. Mackworth. A logical framework for depiction and image interpretation. Artificial Intelligence, 42(2):125-155, 1989

110.   R. Rodosek. A new approach on solving 3­Satisfiability. In Proceedings of the 3rd International Conference on Artificial Intelligence and Symbolic Mathematical Computation, pages 197-212. Springer, LNCS 1138, 1996

111.   R. Schrag and J. M. Crawford. Implicates and Prime Implicates in Random 3SAT. Artificial Intelligence 81(1­2), 199­222, 1996

112.   B. Selman and H. Kautz. Domain­independent extensions to GSAT: Solving large structured satisfiability problems. In Proceedings of the 13th IJCAI. International Joint Conference on Artificial Intelligence, 1993

113.   B. Selman and H. Kautz. An empirical study of greedy local search for satisfiability testing. In Proceedings of the 11th National Conference on AI, pages 46-51. American Association for Artificial Intelligence, 1993

114.   B. Selman, H. Kautz, and B. Cohen. Noise Strategies for Improving Local Search. In Proceedings of the 12th National Conference on AI, pages 337- 343. American Association for Artificial Intelligence, 1994

115.   B. Selman and S. Kirkpatrick. Critical behaviour in the computational cost of satisfiability testing. Artificial Intelligence, 81:273-295, 1996

116.   B. Selman, H. Levesque, and D. Mitchell. A New Method for Solving Hard Satisfiability Problems. In Proceedings of the 10th National Conference on AI, pages 440-446. American Association for Artificial Intelligence, 1992

117.   J.P. Silva., K.A. Sakallah. Conflict Analysis in Search Algorithms for propositional  satisfiability, Proceedings of the International Conference on Tools with Artificial Intelligence, November 1996

118.   J. Slaney, M. Fujita, and M. Stickel. Automated reasoning and exhaustive search: quasigroup existence problems. Computers and Mathematics with Apllications, 29:115-132, 1995

119.   Raymond M Smullyan. First Order Logic. Springer­Verlag, Berlin, 1968

120.   W.M. Spears. A nn algorithm for boolean satisfiability problems. In Proceedings of the 1996 International Conference on Neural Networks, pages 1121-1126, 1996

121.   W.M. Spears. Simulated annealing for hard satisfiability problems. In D.S. Johnson and M.A. Trick editors, In Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, pages 533-558. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 26, American Mathematical Society, 1996

122.   P. R. Stephan, R. K.Brayton, A. L. Sangiovanni­Vincentelli. Combinational Test Generation Using satisfiability, Memorandum No. UCB/ERL M92/112, EECS Department, University of California at Berkeley, October 1992

123.   T.E. Uribe and M.E. Stickel. Ordered binary decision diagrams and the Davis­Putnam procedure. In Proceedings of the First International Conference on Constraints in Computational Logics, Munich, Germany, pages  34-49, 1994

124.   van Gelder and Y.K. Tsuji. Satisfiability testing with more reasoning and less guessing. In D.S. Johnson and M.A. Trick editors, In Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 26, American Mathematical Society, 1996

125.   J.P. Walser. Solving linear pseudo­boolean constraints with local search. In Proceedings of the 14th National Conference on AI, pages 269--274. American Association for Artificial Intelligence, 1997

126.   B.C. Willians and P. Pandurang Nayak. A model­based approach to reactive self­configuring systems. In Proceedings of the 13th National Conference on AI. American Association for Artificial Intelligence, 1996

127.   N. Yugami. Theoretical analysis of Davis­Putnam procedure and propositional satisfiability. In Proceedings of IJCAI­95, pages 282-288, 1995

128.   H. Zhang. SATO: An efficient propositional prover. In Proceedings of 14th Conference on Automated Deduction, pages 272-275, 1997

129.   H. Zhang. Specifying Latin squares in propositional logic. In R. Veroff, editor, Automated Reasoning and Its Applications, Essays in honor of Larry Wos, chapter 6. MIT Press, 1997

130.   H. Zhang, M.P. Bonacina, and J. Hsiang. Psato: A distributed propositional prover and its application to quasigroup problems. Journal of Symbolic Computation, 11:1-18, 1996

131.   H. Zhang and Stickel M. Implementing the Davis­Putnam Algorithm by Tries. Technical report, University of Iowa, 1994

132.   H. Zhang and M.E. Stickel. An efficient algorithm for unit propagation. In  Working Notes of the Fourth International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, Florida, 1996

133.   J. Zhang and H. Zhang. SEM: a System for Enumerating Models. In Proceedings of IJCAI­95, volume 1, pages 298-303. International Joint Conference on Artificial Intelligence, 1995

134.   J. Zhang and H. Zhang. Combining local search and backtracking techniques for constraint Satisfaction. In Proceedings of 13th National Conference on Artificial Intelligence, pages 369-374. American Association for Artificial Intelligence, AAAI Press/The MIT Press, 1996


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