Probabilistic analysis of two heuristics for the 3-satisfiability problem
SIAM Journal on Computing
Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
On the hardness of approximate reasoning
Artificial Intelligence
Generating hard satisfiability problems
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
A threshold for unsatisfiability
Journal of Computer and System Sciences
Beyond NP: the QSAT phase transition
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Initial experiments in stochastic satisfiability
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Setting 2 variables at a time yields a new lower bound for random 3-SAT (extended abstract)
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Lower bounds for random 3-SAT via differential equations
Theoretical Computer Science - Phase transitions in combinatorial problems
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
Coupon Collectors, q-Binomial Coefficients and the Unsatisfiability Threshold
ICTCS '01 Proceedings of the 7th Italian Conference on Theoretical Computer Science
Counting Models Using Connected Components
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Experimental Analysis of the Computational Cost of Evaluating Quantified Boolean Formulae
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
On some central problems in computational complexity.
On some central problems in computational complexity.
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
The good old Davis-Putnam procedure helps counting models
Journal of Artificial Intelligence Research
P systems with elementary active membranes: beyond NP and coNP
CMC'10 Proceedings of the 11th international conference on Membrane computing
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The complexity class PP consists of all decision problems solvable by polynomial-time probabilistic Turing machines. It is well known that PP is a highly intractable complexity class and that PP-complete problems are in all likelihood harder than NP-complete problems. We investigate the existence of phase transitions for a family of PP-complete Boolean satisfiability problems under the fixed clauses-to-variables ratio model. A typical member of this family is the decision problem # 3SAT(=2^n^/^2): given a 3CNF-formula, is it satisfied by at least the square-root of the total number of possible truth assignments? We provide evidence to the effect that there is a critical ratio r"3","2 at which the asymptotic probability of # 3SAT(=2^n^/^2) undergoes a phase transition from 1 to 0. We obtain upper and lower bounds for r"3","2 by showing that 0.9227==2^n^/^2) using a natural modification of the Davis-Putnam-Logemann-Loveland (DPLL) procedure. Our experimental results suggest that r"3","2~2.5. Moreover, the average number of recursive calls of this modified DPLL procedure reaches a peak around 2.5 as well.