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Performing bayesian inference by weighted model counting
 In Proceedings of the National Conference on Artificial Intelligence (AAAI
, 2005
"... Over the past decade general satisfiability testing algorithms have proven to be surprisingly effective at solving a wide variety of constraint satisfaction problem, such as planning and scheduling (Kautz and Selman 2003). Solving such NPcomplete tasks by “compilation to SAT ” has turned out to be a ..."
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Over the past decade general satisfiability testing algorithms have proven to be surprisingly effective at solving a wide variety of constraint satisfaction problem, such as planning and scheduling (Kautz and Selman 2003). Solving such NPcomplete tasks by “compilation to SAT ” has turned out to be an approach that is of both practical and theoretical interest. Recently, (Sang et al. 2004) have shown that state of the art SAT algorithms can be efficiently extended to the harder task of counting the number of models (satisfying assignments) of a formula, by employing a technique called component caching. This paper begins to investigate the question of whether “compilation to modelcounting ” could be a practical technique for solving realworld #Pcomplete problems, in particular Bayesian inference. We describe an efficient translation from Bayesian networks to weighted model counting, extend the best modelcounting algorithms to weighted model counting, develop an efficient method for computing all marginals in a single counting pass, and evaluate the approach on computationally challenging reasoning problems.
Complete local search for propositional satisfiability
 In proceedings of AAAI
, 2004
"... Algorithms based on following local gradient information are surprisingly effective for certain classes of constraint satisfaction problems. Unfortunately, previous local search algorithms are notoriously incomplete: They are not guaranteed to find a feasible solution if one exists and they cannot b ..."
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Algorithms based on following local gradient information are surprisingly effective for certain classes of constraint satisfaction problems. Unfortunately, previous local search algorithms are notoriously incomplete: They are not guaranteed to find a feasible solution if one exists and they cannot be used to determine unsatisfiability. We present an algorithmic framework for complete local search and discuss in detail an instantiation for the propositional satisfiability problem (SAT). The fundamental idea is to use constraint learning in combination with a novel objective function that converges during search to a surface without local minima. Although the algorithm has worstcase exponential space complexity, we present empirical results on challenging SAT competition benchmarks that suggest that our implementation can perform as well as stateoftheart solvers based on more mature techniques. Our framework suggests a range of possible algorithms lying between treebased search and local search.
Towards IndustrialLike Random SAT Instances ∗
"... We focus on the random generation of SAT instances that have computational properties that are similar to realworld instances. It is known that industrial instances, even with a great number of variables, can be solved by a clever solver in a reasonable amount of time. This is not possible, in gene ..."
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We focus on the random generation of SAT instances that have computational properties that are similar to realworld instances. It is known that industrial instances, even with a great number of variables, can be solved by a clever solver in a reasonable amount of time. This is not possible, in general, with classical randomly generated instances. We provide different generation models of SAT instances, extending the uniform and regular 3CNF models. They are based on the use of nonuniform probability distributions to select variables. Our last model also uses a mechanism to produce clauses of different lengths as in industrial instances. We show the existence of the phase transition phenomena for our models and we study the hardness of the generated instances as a function of the parameters of the probability distributions. We prove that, with these parameters we can adjust the difficulty of the problems in the phase transition point. We measure hardness in terms of the performance of different solvers. We show how these models will allow us to generate random instances similar to industrial instances, of interest for testing purposes. 1
Towards solving manyvalued MaxSAT
 In Proceedings, 36th International Symposium on MultipleValued Logics (ISMVL
, 2006
"... We define the MaxSAT problem for manyvalued CNF formulas, called manyvalued MaxSAT, and establish its complexity class. We then describe a basic branch and bound algorithm for solving manyvalued MaxSAT, and an exact manyvalued MaxSAT solver we have implemented. Finally, we report the experimenta ..."
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We define the MaxSAT problem for manyvalued CNF formulas, called manyvalued MaxSAT, and establish its complexity class. We then describe a basic branch and bound algorithm for solving manyvalued MaxSAT, and an exact manyvalued MaxSAT solver we have implemented. Finally, we report the experimental investigation we have performed to compare our solver with Boolean MaxSAT solvers on graph coloring instances. The results obtained indicate that manyvalued CNF formulas can become a competitive formalism for representing and solving combinatorial optimization problems. 1
On the Structure of Industrial SAT Instances ⋆
"... Abstract. During this decade, it has been observed that many realworld graphs, like the web and some social and metabolic networks, have a scalefree structure. These graphs are characterized by a big variability in the arity of nodes, that seems to follow a powerlaw distribution. This came as a bi ..."
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Abstract. During this decade, it has been observed that many realworld graphs, like the web and some social and metabolic networks, have a scalefree structure. These graphs are characterized by a big variability in the arity of nodes, that seems to follow a powerlaw distribution. This came as a big surprise to researchers steeped in the tradition of classical random networks. SAT instances can also be seen as (bipartite) graphs. In this paper we study many families of industrial SAT instances used in SAT competitions, and show that most of them also present this scalefree structure. On the contrary, random SAT instances, viewed as graphs, are closer to the classical random graph model, where arity of nodes follows a Poisson distribution with small variability. This would explain their distinct nature. We also analyze what happens when we instantiate a fraction of the variables, at random or using some heuristics, and how the scalefree structure is modified by these instantiations. Finally, we study how the structure is modified during the execution of a SAT solver, concluding that the scalefree structure is preserved. 1
TLSim and EVC: a termlevel symbolic simulator and an efficient decision procedure for the logic of equality with uninterpreted functions and memories
 Int. J. Embedded Systems
, 2005
"... ..."
CSP Search Algorithms with Responsibility Sets and Kernels
, 2005
"... A CSP lookahead search algorithm, like FC or MAC, explores a search tree during its run. Every node of the search tree can be associated with a CSP created by the refined domains of unassigned variables. If the algorithm detects that the CSP associated with a node is insoluble, the node becomes a ..."
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A CSP lookahead search algorithm, like FC or MAC, explores a search tree during its run. Every node of the search tree can be associated with a CSP created by the refined domains of unassigned variables. If the algorithm detects that the CSP associated with a node is insoluble, the node becomes a deadend. A strategy of pruning ”by analogy ” states that the current node of the search tree can be discarded if the CSP associated with it is ”more constrained ” than a CSP associated with some deadend node. In this paper we present a method of pruning based on the above strategy. The information about the CSPs associated with deadend nodes is kept in the structures called responsibility set and kernel. The method that uses these structures for pruning is termed Responsibility set, Kernel, Propagation RKP. The resulting combined algorithms are FCRKP and MACRKP. Under certain restrictions, FCRKP is shown theoretically to simulate FCCBJ. Experimental evaluation is presented demonstrating that MACRKP outperforms MACCBJ on random CSPs and on random graph coloring problems. .
Logic Programming for Healthcare Informatics: an Informal Survey
"... • Terrance Swift has financial interest in mdlogix, Inc which is mentioned in this talk • Terrance Swift has financial interest in XSB, Inc which is not mentioned in this talk – The programming system XSB Prolog is opensource and freely available. It is not controlled in any way by XSB, Inc, althoug ..."
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• Terrance Swift has financial interest in mdlogix, Inc which is mentioned in this talk • Terrance Swift has financial interest in XSB, Inc which is not mentioned in this talk – The programming system XSB Prolog is opensource and freely available. It is not controlled in any way by XSB, Inc, although they have generously contributed to its development 2 Learning Objectives • to survey how formal logic and logic programming have been used for knowledge representation and reasoning for a variety of medical applications • to sketch the current state of logic programming systems, focusing on opensource systems • to indicate how some current research directions in logic programming may be relevant to areas such as adaptive workflow management for healthcare or for dynamic decision support.... a little learning is a dangerous thing, and so is writing your learning objectives before your talk:) 3