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Resolution versus Search: Two Strategies for SAT. Brad Dunbar Shamik Roy Chowdhury. Propositional Satisfiability Problems. Propositional satisfiability Algorithms with good average performance has been focus of extensive research.
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Resolution versus Search:Two Strategies for SAT Brad Dunbar Shamik Roy Chowdhury
Propositional Satisfiability Problems • Propositional satisfiability Algorithms with good average performance has been focus of extensive research. • Davis Putnam Algorithm for deciding propositional satisfiability Directional Resolution. • Worst Case Time /Space complexity of DR : • O( n.exp(w*) ) where • n : number of variables • W* : induced width
What makes DR a good algorithm: • Decides satisfiability and finds solution ( model ). • Given input theory and a variable ordering Knowledge Compilation Algorithm : • Generation equivalent theory ( directional extension ) • Each model can be found in linear time. • All models can be found in the time linear in the number of models. • Performs better on structured algorithms. • k-tree embeddings having induced width. • w* < n ( generally ) • DR ( worst case bound) < DP ( worst case bound )
An Example Resolution : Resolution over A • Node : Each propositional variable. • Edge : Between variables of the same clause. • Resolution over clauses ( a V Q ) and ( b V ~Q ) => a V b ( Resolvent ). • Resolution over A ( adj. Fig. ) => (B V C V E ) … introduces edge C – E.
Execution of Directional Resolution (DR):Knowledge Compilation & model generation
Complexity of Directional Resolution(DR) Algorithm: Change of E(Q) with ordering
Dependence of complexity on Induced Width • Theorem 4: • Given Theory(Q) and an ordering of its variables (o). • Directional Resolution(DR) time complexity along ‘o’ • Size of at most • where is the induced width of interaction graph.
Ordering Heuristics : Which Ordering gives Minimum Induced Width ? • Finding an ordering which yields smallest induced width is NP-HARD. • Ordering Heuristics : • Polynomial Time Greedy Algorithm. • Computation/Generation of min-width ordering.
Diversity • Upper bound on the number of resolution operation. • Based on fact : Proposition resolved only when it appears both positively and negatively in different clauses. • Div(o) – largest diversity of its variables relative to ‘o’. • Div(of a theory) – minimum diversity among all orderings • bounds number of clauses generated in each bucket. • Eg: If ordering (o) has 0 diversity, then algorithm DR adds no clauses to the theory regardless of its induced width .
Ordering Heuristics : Algorithm to generate ordering giving minimum Diversity • Finding an ordering which yields minimum- induced diversity is NP-HARD. • Ordering Heuristics : • Polynomial Time Greedy Algorithm. • Computation/Generation of min-diversity ordering.
Conclusions • DP Performs much better on random uniform k-cnfs • DR Performs much better on k-cnf chains and (k,m) trees • A hybrid model can perform better than DR and DP for certain cases
References • Rish and Dechter (Irina Rish and RinaDechter. "Resolution versus Search: Two Strategies for SAR." Journal of Automated Reasoning, 24, 215—259, 2000.) • (Davis, M. and Putnam, H. (1960). "A computing procedure for quantification theory." Journal of the ACM, 7(3): 201--215.) • (Davis, M., Logemann, G., and Loveland, D. (1962). "A machine program for theorem proving." Communications of the ACM, 5(7): 394--397)