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19
AND/OR Search Spaces for Graphical Models
, 2004
"... The paper introduces an AND/OR search space perspective for graphical models that include probabilistic networks (directed or undirected) and constraint networks. In contrast to the traditional (OR) search space view, the AND/OR search tree displays some of the independencies present in the gr ..."
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Cited by 102 (43 self)
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The paper introduces an AND/OR search space perspective for graphical models that include probabilistic networks (directed or undirected) and constraint networks. In contrast to the traditional (OR) search space view, the AND/OR search tree displays some of the independencies present in the graphical model explicitly and may sometime reduce the search space exponentially. Indeed, most
Compiling relational bayesian networks for exact inference
 International Journal of Approximate Reasoning
, 2004
"... 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 evalua ..."
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Cited by 54 (11 self)
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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.
Compiling constraint networks into AND/OR multivalued decision diagrams (AOMDDs)
 IN PROCEEDINGS OF THE TWELFTH INTERNATIONAL CONFERENCE ON PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING (CP’06
, 2006
"... Inspired by AND/OR search spaces for graphical models recently introduced, we propose to augment Ordered Decision Diagrams with AND nodes, in order to capture function decomposition structure. This yields AND/OR multivalued decision diagram (AOMDD) which compiles a constraint network into a canonic ..."
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Cited by 23 (6 self)
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Inspired by AND/OR search spaces for graphical models recently introduced, we propose to augment Ordered Decision Diagrams with AND nodes, in order to capture function decomposition structure. This yields AND/OR multivalued decision diagram (AOMDD) which compiles a constraint network into a canonical form that supports polynomial time queries such as solution counting, solution enumeration or equivalence of constraint networks. We provide a compilation algorithm based on Variable Elimination for assembling an AOMDD for a constraint network starting from the AOMDDs for its constraints. The algorithm uses the APPLY operator which combines two AOMDDs by a given operation. This guarantees the complexity upper bound for the compilation time and the size of the AOMDD to be exponential in the treewidth of the constraint graph, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs).
On probabilistic inference by weighted model counting
 Artificial Intelligence
"... A recent and effective approach to probabilistic inference calls for reducing the problem to one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the approach calls for encoding the probabilistic model, typically a Bayesian network, as a propositional knowledge base ..."
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Cited by 22 (0 self)
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A recent and effective approach to probabilistic inference calls for reducing the problem to one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the approach calls for encoding the probabilistic model, typically a Bayesian network, as a propositional knowledge base in conjunctive normal form (CNF) with weights associated to each model according to the network parameters. Given this CNF, computing the probability of some evidence becomes a matter of summing the weights of all CNF models consistent with the evidence. A number of variations on this approach have appeared in the literature recently, that vary across three orthogonal dimensions. The first dimension concerns the specific encoding used to convert a Bayesian network into a CNF. The second dimensions relates to whether weighted model counting is performed using a search algorithm on the CNF, or by compiling the CNF into a structure that renders WMC a polytime operation in the size of the compiled structure. The third dimension deals with the specific properties of network parameters (local structure) which are captured in the CNF encoding. In this paper, we discuss recent work in this area across the above three dimensions, and demonstrate empirically its practical importance in significantly expanding the reach of exact probabilistic inference. We restrict our discussion to exact inference and model counting, even though other proposals have been extended for approximate inference and approximate model counting.
Approximate Compilation of Constraints into Multivalued Decision Diagrams
"... Abstract. We present an incremental refinement algorithm for approximate compilation of constraint satisfaction models into multivalued decision diagrams (MDDs). The algorithm uses a vertex splitting operation that relies on the detection of equivalent paths in the MDD. Although the algorithm is qui ..."
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Cited by 13 (8 self)
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Abstract. We present an incremental refinement algorithm for approximate compilation of constraint satisfaction models into multivalued decision diagrams (MDDs). The algorithm uses a vertex splitting operation that relies on the detection of equivalent paths in the MDD. Although the algorithm is quite general, it can be adapted to exploit constraint structure by specializing the equivalence tests for partial assignments to particular constraints. We show how to modify the algorithm in a principled way to obtain an approximate MDD when the exact MDD is too large for practical purposes. This is done by replacing the equivalence test with a constraintspecific measure of distance. We demonstrate the value of the approach for approximate and exact MDD compilation and evaluate its benefits in one of the main MDD application domains, interactive configuration. 1
AND/OR multivalued decision diagrams (AOMDDs) for weighted graphical models
 In Proceedings of the Twenty Third Conference on Uncertainty in Artificial Intelligence (UAI’07
, 2007
"... Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment MultiValued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical model ..."
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Cited by 11 (3 self)
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Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment MultiValued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR MultiValued Decision Diagram (AOMDD) which compiles a graphical model into a canonical form that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. equivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphical model. The first is searchbased, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. The second is inferencebased and uses a Bucket Elimination schedule to combine the AOMDDs of the input functions via the the APPLY operator. For both algorithms, the compilation time and the size of the AOMDD are, in the worst case, exponential in the treewidth of the graphical model, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs). We introduce the concept of semantic treewidth, which helps explain why the size of a decision diagram is often much smaller than the worst case bound. We provide an experimental evaluation that demonstrates the potential of AOMDDs. 1.
F.: Using more reasoning to improve #SAT solving
 Proc. AAAI’07, AAAI Press
, 2007
"... Many realworld problems, including inference in Bayes Nets, can be reduced to #SAT, the problem of counting the number of models of a propositional theory. This has motivated the need for efficient #SAT solvers. Currently, such solvers utilize a modified version of DPLL that employs decomposition a ..."
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Cited by 7 (0 self)
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Many realworld problems, including inference in Bayes Nets, can be reduced to #SAT, the problem of counting the number of models of a propositional theory. This has motivated the need for efficient #SAT solvers. Currently, such solvers utilize a modified version of DPLL that employs decomposition and caching, techniques that significantly increase the time it takes to process each node in the search space. In addition, the search space is significantly larger than when solving SAT since we must continue searching even after the first solution has been found. It has previously been demonstrated that the size of a DPLL search tree can be significantly reduced by doing more reasoning at each node. However, for SAT the reductions gained are often not worth the extra time required. In this paper we verify the hypothesis that for #SAT this balance changes. In particular, we show that additional reasoning can reduce the size of a #SAT solver’s search space, that this reduction cannot always be achieved by the already utilized technique of clause learning, and that this additional reasoning can be cost effective.
Knowledge compilation properties of treeofBDDs
 In Proc. of AAAI’07
, 2007
"... We present a CNF to TreeofBDDs (ToB) compiler with complexity at most exponential in the tree width. We then present algorithms for interesting queries on ToB. Although some of the presented query algorithms are in the worst case exponential in the tree width, our experiments show that ToB can ans ..."
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Cited by 5 (0 self)
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We present a CNF to TreeofBDDs (ToB) compiler with complexity at most exponential in the tree width. We then present algorithms for interesting queries on ToB. Although some of the presented query algorithms are in the worst case exponential in the tree width, our experiments show that ToB can answer nontrivial queries like clausal entailment in reasonable time for several realistic instances. While our ToBtool compiles all the used 91 instances, dDNNF compilation failed for 12 or 8 of them based on the decomposition heuristic used. Also, on the succeeded instances, a dDNNF is up to 1000 times larger than the matching ToB. The ToB compilations are often an order of magnitude faster than the dDNNF compilation. This makes ToB a quite interesting knowledge compilation form.
Fast dDNNF Compilation with sharpSAT
"... Knowledge compilation is a valuable tool for dealing with the computational intractability of propositional reasoning. In knowledge compilation, a representation in a source language is typically compiled into a target language in order to perform some reasoning task in polynomial time. One particul ..."
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Cited by 5 (2 self)
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Knowledge compilation is a valuable tool for dealing with the computational intractability of propositional reasoning. In knowledge compilation, a representation in a source language is typically compiled into a target language in order to perform some reasoning task in polynomial time. One particularly popular target language is Deterministic Decomposable Negation Normal Form (dDNNF). dDNNF supports efficient reasoning for tasks such as consistency checking and model counting, and as such it has proven a useful representation language for Bayesian inference, conformant planning, and diagnosis. In this paper, we exploit recent advances in #SAT solving in order to produce a new stateoftheart CNF → dDNNF compiler. We evaluate the properties and performance of our compiler relative to C2D, the de facto standard for compiling to dDNNF. Empirical results demonstrate that our compiler is generally one order of magnitude faster than C2D on typical benchmark problems while yielding a dDNNF representation of comparable size. 1