27 research outputs found
Constraint-based sequence mining using constraint programming
The goal of constraint-based sequence mining is to find sequences of symbols
that are included in a large number of input sequences and that satisfy some
constraints specified by the user. Many constraints have been proposed in the
literature, but a general framework is still missing. We investigate the use of
constraint programming as general framework for this task. We first identify
four categories of constraints that are applicable to sequence mining. We then
propose two constraint programming formulations. The first formulation
introduces a new global constraint called exists-embedding. This formulation is
the most efficient but does not support one type of constraint. To support such
constraints, we develop a second formulation that is more general but incurs
more overhead. Both formulations can use the projected database technique used
in specialised algorithms. Experiments demonstrate the flexibility towards
constraint-based settings and compare the approach to existing methods.Comment: In Integration of AI and OR Techniques in Constraint Programming
(CPAIOR), 201
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
Rotation-based formulation for stable matching
We introduce new CP models for the many-to-many stable matching problem. We use the notion of rotation to give a novel encoding that is linear in the input size of the problem. We give extra filtering rules to maintain arc consistency in quadratic time. Our experimental study on hard instances of sex-equal and balanced stable matching shows the efficiency of one of our propositions as compared with the state-of-the-art constraint programming approach
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
Generating Random Logic Programs Using Constraint Programming
Testing algorithms across a wide range of problem instances is crucial to
ensure the validity of any claim about one algorithm's superiority over
another. However, when it comes to inference algorithms for probabilistic logic
programs, experimental evaluations are limited to only a few programs. Existing
methods to generate random logic programs are limited to propositional programs
and often impose stringent syntactic restrictions. We present a novel approach
to generating random logic programs and random probabilistic logic programs
using constraint programming, introducing a new constraint to control the
independence structure of the underlying probability distribution. We also
provide a combinatorial argument for the correctness of the model, show how the
model scales with parameter values, and use the model to compare probabilistic
inference algorithms across a range of synthetic problems. Our model allows
inference algorithm developers to evaluate and compare the algorithms across a
wide range of instances, providing a detailed picture of their (comparative)
strengths and weaknesses.Comment: This is an extended version of the paper published in CP 202
Confidence-based Reasoning in Stochastic Constraint Programming
In this work we introduce a novel approach, based on sampling, for finding
assignments that are likely to be solutions to stochastic constraint
satisfaction problems and constraint optimisation problems. Our approach
reduces the size of the original problem being analysed; by solving this
reduced problem, with a given confidence probability, we obtain assignments
that satisfy the chance constraints in the original model within prescribed
error tolerance thresholds. To achieve this, we blend concepts from stochastic
constraint programming and statistics. We discuss both exact and approximate
variants of our method. The framework we introduce can be immediately employed
in concert with existing approaches for solving stochastic constraint programs.
A thorough computational study on a number of stochastic combinatorial
optimisation problems demonstrates the effectiveness of our approach.Comment: 53 pages, working draf
sunny-as2: Enhancing SUNNY for Algorithm Selection
SUNNY is an Algorithm Selection (AS) technique originally tailored for
Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of
solvers, a subset of solvers to be run on a given CP problem. This approach has
proved to be effective for CP problems, and its parallel version won many gold
medals in the Open category of the MiniZinc Challenge -- the yearly
international competition for CP solvers. In 2015, the ASlib benchmarks were
released for comparing AS systems coming from disparate fields (e.g., ASP, QBF,
and SAT) and SUNNY was extended to deal with generic AS problems. This led to
the development of sunny-as2, an algorithm selector based on SUNNY for ASlib
scenarios. A preliminary version of sunny-as2 was submitted to the Open
Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the
best approach for the runtime minimization of decision problems. In this work,
we present the technical advancements of sunny-as2, including: (i)
wrapper-based feature selection; (ii) a training approach combining feature
selection and neighbourhood size configuration; (iii) the application of nested
cross-validation. We show how sunny-as2 performance varies depending on the
considered AS scenarios, and we discuss its strengths and weaknesses. Finally,
we also show how sunny-as2 improves on its preliminary version submitted to
OASC
A review of literature on parallel constraint solving
As multicore computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multicore computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation
Investigation of Matching Problems using Constraint Programming and Optimisation Methods
This thesis focuses on matching under ordinal preferences, i.e. problems where agents may be required to list other agents that they find acceptable in order of preference. In particular, we focus on two main cases: the popular matching and the kidney exchange problem. These problems are important in practice and in this thesis we develop novel algorithms and techniques to solve them as combinatorial optimisation problems. The first part of the thesis focuses on one-sided matching on a bipartite graph, specifically the popular matching. When the participants express their preferences in an ordinal order, one might want to guarantee that no two applicants are inclined to form a coalition in order to maximise their welfare, thus finding a stable matching is needed. Popularity is a concept that offers an attractive trade- off between these two notions. In particular, we examine the popular matching in the context of constraint programming using global constraints. We discuss the possibility to find a popular matching even for the instances that does not admit one.
The second part of the thesis focuses on non-bipartite graphs, i.e. the kidney exchange problem. Kidney transplant is the most effective treatment to cure end-stage renal disease, affecting one in every thousand European citizen. Motivated by the observation that the kidney exchange is inherently a stochastic online problem, first, we give a stochastic online method, which provides an expected value estimation that is correct within the limit of sampling errors. Second, we show that by taking into consideration a probabilistic model of future arrivals and drop-offs, we can get reduce sampling scenarios, and we can even construct a sampling-free probabilistic model, called the Abstract Exchange Graph (AEG). A final contribution of this thesis is related to finding robust solutions when uncertainty occurs. Uncertainty is inherent to most real world problems