We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available Primula tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose Primula–generated propositional instances have thousands of variables, and whose jointrees have clusters with hundreds of variables.

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Abstract. Searching for optimal solutions to a problem using lower bounds obtained from a relaxation is a common idea in Heuristic Search and Planning. In SAT and CSPs, however, explicit relaxations are seldom used. In this work, we consider the use of explicit relaxations for solving MinCostSAT, the problem of finding a minimum cost satisfying assignment. We start with the observation that while a number of SAT and CSP tasks have a complexity that is exponential in the treewidth, such models can be relaxed into weaker models of bounded treewidth by a simple form of variable renaming. The relaxed models can then be compiled in polynomial time and space, so that their solutions can be used effectively for pruning the search in the original problem. We have implemented a MinCostSAT solver using this idea on top of two off-the-shelf tools, a d-DNNF compiler that deals with the relaxation, and a SAT solver that deals with the search. The results over the entire suite of 559 problems from the 2006 Weighted Max-SAT Competition are encouraging: SR(w), the new solver, solves 56 % of the problems when the bound on the relaxation treewidth is set to w = 8, while Toolbar, the winner, solves 73 % of the problems, Lazy, the runner up, 55%, and MinCostChaff, a recent MinCostSAT solver, 26%. The relation between the proposed relaxation method and existing structural solution methods such as cutset decomposition and derivatives such as mini-buckets is also discussed. 1

In this paper, we investigate the extent to which knowledge compilation can be used to circumvent the complexity of skeptical inference from a stratified belief base (SBB). We first analyze the compilability of skeptical inference from an SBB, under various requirements concerning both the selection policy under consideration, the possibility to make the stratification vary at the on-line query answering stage and the expected complexity of inference from the compiled form. Not surprisingly, the results are mainly negative. However, since they concern the worst case situation only, they do not prevent a compilation-based approach from being practically useful for some families of instances. While many approaches to compile an SBB can be designed, we are primarily interested in those which take advantage of existing knowledge compilation techniques for classical inference. Specifically, we present a general framework for compiling SBBs into so-called C-normal SBBs, where C is any tractable class for clausal entailment which is the target class of a compilation function. Another major advantage of the proposed approach lies in the flexibility of the C-normal belief bases obtained, which means that changing the stratification does not require to re-compile the SBB. For several families of compiled SBBs and several selection policies, the complexity of skeptical inference is identified. Some tractable restrictions are exhibited for each policy. Finally, some empirical results are presented.

Abstract. We introduce a new branch-and-bound Max-SAT solver, Clone, which employs a novel approach for computing lower bounds. This approach allows Clone to search in a reduced space. Moreover, Clone is equipped with novel techniques for learning from soft conflicts. Experimental results show that Clone performs competitively with the leading Max-SAT solver in the broadest category of this year’s Max-SAT evaluation and outperforms last year’s leading solvers. 1

Several propositional fragments have been considered so far as target languages for knowledge compilation and used for improving computational tasks from major AI areas (like inference, diagnosis and planning); among them are the (quite influential) ordered binary decision diagrams, prime implicates, prime implicants, ”formulae ” in decomposable negation normal form. On the other hand, the validity problem QBF for Quantified Boolean Formulae (QBF) has been acknowledged for the past few years as an important issue for AI, and many solvers have been designed for this purpose. In this paper, the complexity of restrictions of QBF obtained by imposing the matrix of the input QBF to belong to such propositional fragments is identified. Both tractability and intractability results (PSPACE-completeness) are obtained.

many-sorted propositional logic

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Abstract. Given the large number of Semantic Web Services that can be created from online sources by using existing annotation tools, expressive formalisms and efficient and scalable approaches to solve the service selection problem are required to make these services widely available to the users. In this paper, we propose a framework that is grounded on logic and the Local-As-View approach for representing instances of the service selection problem. In our approach, Web services are semantically described using LAV mappings in terms of generic concepts from an ontology, user requests correspond to conjunctive queries on the generic concepts and, in addition, the user may specify a set of preferences that are used to rank the possible solutions to the given request. The LAV formulation allows us to cast the service selection problem as a query rewriting problem that must consider the relationships among the concepts in the ontology and the ranks induced by the preferences. Then, building on related work, we devise an encoding of the resulting query rewriting problem as a logical theory whose models are in correspondence with the solutions of the user request, and in presence of preferences, whose best models are in correspondence with the best-ranked solutions. Thus, by exploiting known properties of modern SAT solvers, we provide an efficient and scalable solution to the service selection problem. The approach provides the basis to represent a large number of real-world situations and interesting user requests. 1

We analyze, in this work, the performance of a recently introduced weighted Max-SAT solver, Clone, in the Max-SAT evaluation 2007. Clone utilizes a novel bound computation based on formula compilation that allows it to search in a reduced search space. We study how additional techniques from the SAT and Max-SAT literature affect the performance of Clone on problems from the evaluation. We then perform further investigations on factors that may affect the performance of leading Max-SAT solvers. We empirically identify two properties of weighted Max-SAT problems that can be used to adjust the difficulty level of the problems with respect to the considered solvers. Keywords: Max-SAT, constraint relaxation, lower bound computation