By Gerard_J. Holzmann (auth.), Ed Brinksma, Kim Guldstrand Larsen (eds.)
This quantity includes the complaints of the convention on computing device Aided V- i?cation (CAV 2002), held in Copenhagen, Denmark on July 27-31, 2002. CAV 2002 was once the 14th in a chain of meetings devoted to the development of the speculation and perform of computer-assisted formal research equipment for software program and platforms. The convention covers the spectrum from theoretical - sults to concrete functions, with an emphasis on useful veri?cation instruments, together with algorithms and strategies wanted for his or her implementation. The c- ference has characteristically drawn contributions from researchers in addition to prac- tioners in either academia and undefined. This yr we bought ninety four general paper submissions out of which 35 have been chosen. each one submission bought a standard of four referee reports. moreover, the CAV software contained eleven device shows chosen from sixteen submissions. for every instrument presentation, a demo used to be given on the convention. the big variety of instrument submissions and shows testi?es to the liveliness of the ?eld and its utilized ?avor.
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Additional info for Computer Aided Verification: 14th International Conference, CAV 2002 Copenhagen, Denmark, July 27–31, 2002 Proceedings
This section focuses on the main lessons learnt in this process. 1 The Branching Heuristics Branching occurs in the function decide_next_branch() in Fig. 2. When no more deduction is possible, the function will choose one variable from all the free variables and assign it to a value. The importance of choosing good branching variables is well known - different branching heuristics may produce drastically different sized search trees for the same basic algorithm, thus significantly affect the efficiency of the solver.
W. D. Thesis, Department of Computer and Information Science: University of Pennsylvania, 1995.  A. V. Gelder and Y. K. Tsuji, "Satisfiability Testing with more Reasoning and Less guessing," in Cliques, Coloring and Satisfiability: Second DIMACS Implementation Challenge, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, M. : American Mathematical Society, 1995.  O. Dubois, P. Andre, Y. Boufkhad, and J. s. UNSAT," in Cliques, Coloring and Satisfiability: Second DIMACS Implementation Challenge, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, D.
Therefore, its efficiency is directly related to the implementation of the BCP engine. 1 BCP Mechanisms In a SAT solver, the BCP engine’s function is to detect unit clauses and conflicting clauses after a variable assignment. The BCP engine is the most important part of a SAT solver and usually dictates the data structure and organization of the solver. A simple and intuitive implementation for BCP is to keep counters for each clause. This scheme is attributed to Crawford and Auton  by .