The Experts below are selected from a list of 45 Experts worldwide ranked by ideXlab platform
Georg Gottlob - One of the best experts on this subject based on the ideXlab platform.
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On minimal Constraint Networks
Artificial Intelligence, 2012Co-Authors: Georg GottlobAbstract:In a minimal Binary Constraint Network, every tuple of a Constraint relation can be extended to a solution. The tractability or intractability of computing a solution to such a minimal Network was a long standing open question. Dechter conjectured this computation problem to be NP-hard. We prove this conjecture. We also prove a conjecture by Dechter and Pearl stating that for k>=2 it is NP-hard to decide whether a single Constraint can be decomposed into an equivalent k-ary Constraint Network. We show that this holds even in case of bi-valued Constraints where k>=3, which proves another conjecture of Dechter and Pearl. Finally, we establish the tractability frontier for this problem with respect to the domain cardinality and the parameter k.
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On minimal Constraint Networks
Artificial Intelligence, 2012Co-Authors: Georg GottlobAbstract:In a minimal Binary Constraint Network, every tuple of a Constraint relation can be extended to a solution. The tractability or intractability of computing a solution to such a minimal Network was a long standing open question. Dechter conjectured this computation problem to be NP-hard. We prove this conjecture. We also prove a conjecture by Dechter and Pearl stating that for k\geq2 it is NP-hard to decide whether a single Constraint can be decomposed into an equivalent k-ary Constraint Network. We show that this holds even in case of bi-valued Constraints where k\geq3, which proves another conjecture of Dechter and Pearl. Finally, we establish the tractability frontier for this problem with respect to the domain cardinality and the parameter k
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CP - On minimal Constraint Networks
Principles and Practice of Constraint Programming – CP 2011, 2011Co-Authors: Georg GottlobAbstract:In a minimal Binary Constraint Network, every tuple of a Constraint relation can be extended to a solution. It was conjectured that computing a solution to such a Network is NP hard. We prove this conjecture. We also prove a conjecture by Dechter and Pearl stating that for k ≥ 2 it is NP-hard to decide whether a Constraint Network can be decomposed into an equivalent k-ary Constraint Network, and study related questions.
James M. Conrad - One of the best experts on this subject based on the ideXlab platform.
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ISMIS - Static Parallel Arc Consistency in Constraint Satisfaction
Lecture Notes in Computer Science, 1991Co-Authors: James M. Conrad, Dennis Bahler, James BowenAbstract:Constraint satisfaction problems (CSPs) are ubiquitous in artificial intelligence; versions arise in areas such as vision, design, Boolean satisfiability, cryptarithmetic, and database retrieval. Most researchers have solved CSPs on sequential computers; relatively few have addressed the use of parallel computers for these problems. Among those who have investigated parallel approaches, several authors have solved CSPs using parallel tree search algorithms, while others have pre-processed Constraint Networks using parallel consistency algorithms. No one, however, has measured the specific work performed by the individual processors using arc consistency techniques to pre-process a Constraint Network. In this paper we introduce two Static Parallel Arc Consistency algorithms (SPAC-1 and SPAC-2), which ensure arc consistency of a finite domain Binary Constraint Network, and which are designed for any general-purpose parallel processing computer. Through simulation, we measure work performed by each processor and compare it with work performed by existing sequential and parallel algorithms. Results show that our parallel arc consistency algorithm can be used to pre-process a Constraint Network with good speedup and utilization.
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IPPS - Scalable parallel arc consistency algorithms for shared memory computers
Proceedings Sixth International Parallel Processing Symposium, 1Co-Authors: James M. Conrad, Dharma P. Agrawal, D.r. BahlerAbstract:The paper introduces three scalable static parallel arc consistency algorithms (SPAC-1, SPAC-2 and SPAC-3) designed for any general-purpose shared memory multiple instruction-stream, multiple data-stream (MIMD) computer. The algorithms are intended for Constraint satisfaction problems in AI applications. Arc consistency is ensured of a finite domain Binary Constraint Network. Through actual machine experimentation the paper measures work performed by the SPAC algorithms and compares it with work performed by existing sequential algorithms, AC-1 and AC-3. Results shows that the parallel arc consistency algorithms can be effectively used to pre-process a Constraint Network. >
James Bowen - One of the best experts on this subject based on the ideXlab platform.
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ISMIS - Static Parallel Arc Consistency in Constraint Satisfaction
Lecture Notes in Computer Science, 1991Co-Authors: James M. Conrad, Dennis Bahler, James BowenAbstract:Constraint satisfaction problems (CSPs) are ubiquitous in artificial intelligence; versions arise in areas such as vision, design, Boolean satisfiability, cryptarithmetic, and database retrieval. Most researchers have solved CSPs on sequential computers; relatively few have addressed the use of parallel computers for these problems. Among those who have investigated parallel approaches, several authors have solved CSPs using parallel tree search algorithms, while others have pre-processed Constraint Networks using parallel consistency algorithms. No one, however, has measured the specific work performed by the individual processors using arc consistency techniques to pre-process a Constraint Network. In this paper we introduce two Static Parallel Arc Consistency algorithms (SPAC-1 and SPAC-2), which ensure arc consistency of a finite domain Binary Constraint Network, and which are designed for any general-purpose parallel processing computer. Through simulation, we measure work performed by each processor and compare it with work performed by existing sequential and parallel algorithms. Results show that our parallel arc consistency algorithm can be used to pre-process a Constraint Network with good speedup and utilization.
D.r. Bahler - One of the best experts on this subject based on the ideXlab platform.
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IPPS - Scalable parallel arc consistency algorithms for shared memory computers
Proceedings Sixth International Parallel Processing Symposium, 1Co-Authors: James M. Conrad, Dharma P. Agrawal, D.r. BahlerAbstract:The paper introduces three scalable static parallel arc consistency algorithms (SPAC-1, SPAC-2 and SPAC-3) designed for any general-purpose shared memory multiple instruction-stream, multiple data-stream (MIMD) computer. The algorithms are intended for Constraint satisfaction problems in AI applications. Arc consistency is ensured of a finite domain Binary Constraint Network. Through actual machine experimentation the paper measures work performed by the SPAC algorithms and compares it with work performed by existing sequential algorithms, AC-1 and AC-3. Results shows that the parallel arc consistency algorithms can be effectively used to pre-process a Constraint Network. >
Dennis Bahler - One of the best experts on this subject based on the ideXlab platform.
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ISMIS - Static Parallel Arc Consistency in Constraint Satisfaction
Lecture Notes in Computer Science, 1991Co-Authors: James M. Conrad, Dennis Bahler, James BowenAbstract:Constraint satisfaction problems (CSPs) are ubiquitous in artificial intelligence; versions arise in areas such as vision, design, Boolean satisfiability, cryptarithmetic, and database retrieval. Most researchers have solved CSPs on sequential computers; relatively few have addressed the use of parallel computers for these problems. Among those who have investigated parallel approaches, several authors have solved CSPs using parallel tree search algorithms, while others have pre-processed Constraint Networks using parallel consistency algorithms. No one, however, has measured the specific work performed by the individual processors using arc consistency techniques to pre-process a Constraint Network. In this paper we introduce two Static Parallel Arc Consistency algorithms (SPAC-1 and SPAC-2), which ensure arc consistency of a finite domain Binary Constraint Network, and which are designed for any general-purpose parallel processing computer. Through simulation, we measure work performed by each processor and compare it with work performed by existing sequential and parallel algorithms. Results show that our parallel arc consistency algorithm can be used to pre-process a Constraint Network with good speedup and utilization.