13,700 research outputs found
Proteus: A Hierarchical Portfolio of Solvers and Transformations
In recent years, portfolio approaches to solving SAT problems and CSPs have
become increasingly common. There are also a number of different encodings for
representing CSPs as SAT instances. In this paper, we leverage advances in both
SAT and CSP solving to present a novel hierarchical portfolio-based approach to
CSP solving, which we call Proteus, that does not rely purely on CSP solvers.
Instead, it may decide that it is best to encode a CSP problem instance into
SAT, selecting an appropriate encoding and a corresponding SAT solver. Our
experimental evaluation used an instance of Proteus that involved four CSP
solvers, three SAT encodings, and six SAT solvers, evaluated on the most
challenging problem instances from the CSP solver competitions, involving
global and intensional constraints. We show that significant performance
improvements can be achieved by Proteus obtained by exploiting alternative
view-points and solvers for combinatorial problem-solving.Comment: 11th International Conference on Integration of AI and OR Techniques
in Constraint Programming for Combinatorial Optimization Problems. The final
publication is available at link.springer.co
Optimization as an analysis tool for human complex decision making
We present a problem class of mixed-integer nonlinear programs (MINLPs) with nonconvex continuous relaxations which stem from economic test scenarios that are used in the analysis of human complex problem solving. In a round-based scenario participants hold an executive function. A posteriori a performance indicator is calculated and correlated to personal measures such as intelligence, working memory, or emotion regulation. Altogether, we investigate 2088 optimization problems that differ in size and initial conditions, based on real-world experimental data from 12 rounds of 174 participants. The goals are twofold. First, from the optimal solutions we gain additional insight into a complex system, which facilitates the analysis of a participant’s performance in the test. Second, we propose a methodology to automatize this process by providing a new criterion based on the solution of a series of optimization problems. By providing a mathematical optimization model and this methodology, we disprove the assumption that the “fruit fly of complex problem solving,” the Tailorshop scenario that has been used for dozens of published studies, is not mathematically accessible—although it turns out to be extremely challenging even for advanced state-of-the-art global optimization algorithms and we were not able to solve all instances to global optimality in reasonable time in this study. The publicly available computational tool Tobago [TOBAGO web site https://sourceforge.net/projects/tobago] can be used to automatically generate problem instances of various complexity, contains interfaces to AMPL and GAMS, and is hence ideally suited as a testbed for different kinds of algorithms and solvers. Computational practice is reported with respect to the influence of integer variables, problem dimension, and local versus global optimization with different optimization codes
Building a Truly Distributed Constraint Solver with JADE
Real life problems such as scheduling meeting between people at different
locations can be modelled as distributed Constraint Satisfaction Problems
(CSPs). Suitable and satisfactory solutions can then be found using constraint
satisfaction algorithms which can be exhaustive (backtracking) or otherwise
(local search). However, most research in this area tested their algorithms by
simulation on a single PC with a single program entry point. The main
contribution of our work is the design and implementation of a truly
distributed constraint solver based on a local search algorithm using Java
Agent DEvelopment framework (JADE) to enable communication between agents on
different machines. Particularly, we discuss design and implementation issues
related to truly distributed constraint solver which might not be critical when
simulated on a single machine. Evaluation results indicate that our truly
distributed constraint solver works well within the observed limitations when
tested with various distributed CSPs. Our application can also incorporate any
constraint solving algorithm with little modifications.Comment: 7 page
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