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EFFICIENT SOLVING OF TIMEDEPENDENT ANSWER SET PROGRAMS
"... Abstract. Answer set programs with time predicates are useful to model systems whose properties depend on time, like for example gene regulatory networks. A state of such a system at time point t then corresponds to the literals of an answer set that are grounded with time constant t. An important t ..."
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bound tmax and grounding and solving the program w.r.t. that upper bound leads to a suboptimal solving time when the estimate is too low or too high. In this paper we propose a more efficient algorithm for solving Markovian programs, which are timedependent programs for which the next state depends
Answering Queries Using Views: A Survey
, 2000
"... The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a w ..."
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Cited by 562 (32 self)
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The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a
Implementing data cubes efficiently
 In SIGMOD
, 1996
"... Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total ..."
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Cited by 548 (1 self)
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total sales. The values of many of these cells are dependent on the values of other cells in the data cube..A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 816 (39 self)
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in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a firstorder formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently
Large margin methods for structured and interdependent output variables
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
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Cited by 624 (12 self)
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the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order
Conflictdriven answer set solving
 in Proceedings IJCAI’07
, 2007
"... We introduce a new approach to computing answer sets of logic programs, based on concepts from constraint processing (CSP) and satisfiability checking (SAT). The idea is to view inferences in answer set programming (ASP) as unit propagation on nogoods. This provides us with a uniform constraintbased ..."
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Cited by 201 (48 self)
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We introduce a new approach to computing answer sets of logic programs, based on concepts from constraint processing (CSP) and satisfiability checking (SAT). The idea is to view inferences in answer set programming (ASP) as unit propagation on nogoods. This provides us with a uniform
ASSAT: Computing Answer Sets of a Logic Program by SAT Solvers
 Artificial Intelligence
, 2002
"... We propose a new translation from normal logic programs with constraints under the answer set semantics to propositional logic. Given a normal logic program, we show that by adding, for each loop in the program, a corresponding loop formula to the program’s completion, we obtain a onetoone corresp ..."
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Cited by 260 (7 self)
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toone correspondence between the answer sets of the program and the models of the resulting propositional theory. In the worst case, there may be an exponential number of loops in a logic program. To address this problem, we propose an approach that adds loop formulas a few at a time, selectively. Based
External Memory Algorithms and Data Structures
, 1998
"... Data sets in large applications are often too massive to fit completely inside the computer's internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottleneck. In this paper, we surve ..."
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Cited by 349 (23 self)
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and hierarchical memory. We discuss several important paradigms for how to solve batched and online problems efficiently in external memory. Programming tools and environments are available for simplifying the programming task. The TPIE system (Transparent Parallel I/O programming Environment) is both easy to use
Large scale multiple kernel learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... While classical kernelbased learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We s ..."
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Cited by 340 (20 self)
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show that it can be rewritten as a semiinfinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and oneclass classification. Experimental results
Application of theorem proving to problem solving
, 1969
"... This paper shows how an extension of the resolution proof procedure can be used to construct problem solutions. The extended proof procedure can solve problems involving state transformations. The paper explores several alternate problem representations and provides a discussion of solutions to samp ..."
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Cited by 264 (1 self)
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computer programs and can solve practical problems for a simple robot. Key Words: Theorem proving, resolution, problem solving, automatic programming, program writing, robots, state transformations, question answering. Automatic theorem proving by the resolution proof procedure § represents perhaps
Results 1  10
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