69 research outputs found
Using Restarts in Constraint Programming over Finite Domains - An Experimental Evaluation
The use of restart techniques in complete Satisfiability (SAT) algorithms has made
solving hard real world instances possible. Without restarts such algorithms could not
solve those instances, in practice. State of the art algorithms for SAT use restart
techniques, conflict clause recording (nogoods), heuristics based on activity variable in
conflict clauses, among others. Algorithms for SAT and Constraint problems share many
techniques; however, the use of restart techniques in constraint programming with finite
domains (CP(FD)) is not widely used as it is in SAT. We believe that the use of restarts in
CP(FD) algorithms could also be the key to efficiently solve hard combinatorial
problems.
In this PhD thesis we study restarts and associated techniques in CP(FD) solvers. In
particular, we propose to including in a CP(FD) solver restarts, nogoods and heuristics
based in nogoods as this should improve search algorithms, and, consequently, efficiently
solve hard combinatorial problems.
We thus intend to: a) implement restart techniques (successfully used in SAT) to
solve constraint problems with finite domains; b) implement nogoods (learning) and
heuristics based on nogoods, already in use in SAT and associated with restarts; and c)
evaluate the use of restarts and the interplay with the other implemented techniques.
We have conducted the study in the context of domain splitting backtrack search
algorithms with restarts. We have defined domain splitting nogoods that are extracted
from the last branch of the search algorithm before the restart. And, inspired by SAT
solvers, we were able to use information within those nogoods to successfully help the
variable selection heuristics. A frequent restart strategy is also necessary, since our
approach learns from restarts
Sensor networks and distributed CSP: communication, computation and complexity
We introduce SensorDCSP, a naturally distributed benchmark based on a real-world application that arises in the context of networked distributed systems. In order to study the performance of Distributed CSP (DisCSP) algorithms in a truly distributed setting, we use a discrete-event network simulator, which allows us to model the impact of different network traffic conditions on the performance of the algorithms. We consider two complete DisCSP algorithms: asynchronous backtracking (ABT) and asynchronous weak commitment search (AWC), and perform performance comparison for these algorithms on both satisfiable and unsatisfiable instances of SensorDCSP. We found that random delays (due to network traffic or in some cases actively introduced by the agents) combined with a dynamic decentralized restart strategy can improve the performance of DisCSP algorithms. In addition, we introduce GSensorDCSP, a plain-embedded version of SensorDCSP that is closely related to various real-life dynamic tracking systems. We perform both analytical and empirical study of this benchmark domain. In particular, this benchmark allows us to study the attractiveness of solution repairing for solving a sequence of DisCSPs that represent the dynamic tracking of a set of moving objects.This work was supported in part by AFOSR (F49620-01-1-0076, Intelligent Information Systems Institute and MURI F49620-01-1-0361), CICYT (TIC2001-1577-C03-03 and TIC2003-00950), DARPA (F30602-00-2- 0530), an NSF CAREER award (IIS-9734128), and an Alfred P. Sloan Research Fellowship. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the US Government
Constraints, Lazy Constraints, or Propagators in ASP Solving: An Empirical Analysis
Answer Set Programming (ASP) is a well-established declarative paradigm. One
of the successes of ASP is the availability of efficient systems.
State-of-the-art systems are based on the ground+solve approach. In some
applications this approach is infeasible because the grounding of one or few
constraints is expensive. In this paper, we systematically compare alternative
strategies to avoid the instantiation of problematic constraints, that are
based on custom extensions of the solver. Results on real and synthetic
benchmarks highlight some strengths and weaknesses of the different strategies.
(Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)Comment: Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1,
2017. 16 page
Restarts and Nogood Recording in Qualitative Constraint-based Reasoning
This paper introduces restart and nogood recording techniques in the domain of qualitative spatial and temporal reasoning. Nogoods and restarts can be applied orthogonally to usual methods for solving qualitative constraint satisfaction problems. In particular, we propose a more general definition of nogoods that allows for exploiting information about nogoods and tractable subclasses during backtracking search. First evaluations of the proposed techniques show promising results
Sequential and parallel solution-biased search for subgraph algorithms
Funding: This work was supported by the Engineering and Physical Sciences Research Council (grant numbers EP/P026842/1, EP/M508056/1, and EP/N007565).The current state of the art in subgraph isomorphism solving involves using degree as a value-ordering heuristic to direct backtracking search. Such a search makes a heavy commitment to the first branching choice, which is often incorrect. To mitigate this, we introduce and evaluate a new approach, which we call “solution-biased search”. By combining a slightly-random value-ordering heuristic, rapid restarts, and nogood recording, we design an algorithm which instead uses degree to direct the proportion of search effort spent in different subproblems. This increases performance by two orders of magnitude on satisfiable instances, whilst not affecting performance on unsatisfiable instances. This algorithm can also be parallelised in a very simple but effective way: across both satisfiable and unsatisfiable instances, we get a further speedup of over thirty from thirty-six cores, and over one hundred from ten distributed-memory hosts. Finally, we show that solution-biased search is also suitable for optimisation problems, by using it to improve two maximum common induced subgraph algorithms.Postprin
Proceedings of the 2022 XCSP3 Competition
This document represents the proceedings of the 2022 XCSP3 Competition. The
results of this competition of constraint solvers were presented at FLOC
(Federated Logic Conference) 2022 Olympic Games, held in Haifa, Israel from
31th July 2022 to 7th August, 2022.Comment: arXiv admin note: text overlap with arXiv:1901.0183
Conflict History Based Branching Heuristic for CSP Solving
International audienceAn important feature in designing algorithms to solve Constraint Satisfaction Problems (CSP) is the definition of a branching heuristic to explore efficiently the search space and exploit the problem structure. We propose Conflict-History Search (CHS), a new dynamic and adaptive branching heuristic for CSP solving. It is based on the search history by considering the temporality of search failures. To achieve that, we use the exponential recency weighted average to estimate the evolution of the hardness of constraints throughout the search. The experimental evaluation on XCSP3 instances shows that integrating CHS to solvers based on MAC obtains competitive results and can improve those obtained through other heuristics of the state of the art
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