Cluster-based ASP solving with claspar

Authors:
Martin Gebser;Roland Kaminski;Benjamin Kaufmann;Torsten Schaub;Bettina Schnor
Affiliations:
Institut für Informatik, Universität Potsdam;Institut für Informatik, Universität Potsdam;Institut für Informatik, Universität Potsdam;Institut für Informatik, Universität Potsdam;Institut für Informatik, Universität Potsdam
Venue:
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Year:
2011

Citing 7
Cited 3

PSATO: a distributed propositional prover and its application to quasigroup problems

Journal of Symbolic Computation - Special issue on parallel symbolic computation
Using MPI-2: Advanced Features of the Message Passing Interface

Using MPI-2: Advanced Features of the Message Passing Interface
A Simple Distributed Conflict-Driven Answer Set Solver

LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
The Second Answer Set Programming Competition

LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Conflict-driven answer set solving

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Predicting learnt clauses quality in modern SAT solvers

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Conflict-driven answer set enumeration

LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning

Potassco: The Potsdam Answer Set Solving Collection

AI Communications - Answer Set Programming
Conflict-driven answer set solving: From theory to practice

Artificial Intelligence
Multi-threaded asp solving with clasp

Theory and Practice of Logic Programming

Quantified Score

Hi-index	0.00

Visualization

Abstract

We report on three recent advances in the distributed ASP solver claspar. First, we describe its flexible architecture supporting various search strategies, including competitive search using a portfolio of solver configurations. Second, we describe claspar's distributed learning capacities that allow for sharing learned nogoods among solver instances. Finally, we discuss claspar's approach to distributed optimization.