A Search Control for Scheduling Problems

We propose a framework to control the search for solutions by relaxation, reduction, and decomposition of scheduling problems. Scheduling problems are regarded as Constraint Satisfaction Problems (CSPs) which are represented by sets of constraints. We introduce the notion of subquadrangle and show how to represent any constraint by intersections and unions of subquadrangles. This allows us to connect all three aspects of search control in the same framework, and to determine the maximum search time needed for a given problem. We derive a new tractable class of scheduling problems in this framework and show how to efficiently combine and generate scheduling heuristics. Finally, we present some experimental results. Key words: Constraint satisfaction problem, Graph theory, Reasoning, Search methods, Scheduling algorithms 1 Introduction One method lately considered to tackled scheduling problems consists of using constraint satisfaction techniques (Fox and Sadeh, 1990; Spragg and Smith, 1..

Poster: Attacking malware classifiers by crafting gradient-attacks that preserve functionality

Machine learning has proved to be a promising technology to determine whether a piece of software is malicious or benign. However, the accuracy of this approach comes sometimes at the expense of its robustness and probing these systems against adversarial examples is not always a priority. In this work, we present a gradient-based approach that can carefully generate valid executable malicious files that are classified as benign by state-of-the-art detectors. Initial results demonstrate that our approach is able to automatically find optimal adversarial examples in a more efficient way, which can provide a good support for building more robust models in the future

Lazy clause generation reengineered

Abstract. Lazy clause generation is a powerful hybrid approach to combinatorial optimization that combines features from SAT solving and finite domain (FD) propagation. In lazy clause generation finite domain propagators are considered as clause generators that create a SAT description of their behaviour for a SAT solver. The ability of the SAT solver to explain and record failure and perform conflict directed backjumping are then applicable to FD problems. The original implementation of lazy clause generation was constructed as a cut down finite domain propagation engine inside a SAT solver. In this paper we show how to engineer a lazy clause generation solver by embedding a SAT solver inside an FD solver. The resulting solver is flexible, efficient and easy to use. We give experiments illustrating the effect of different design choices in engineering the solver.

A New Constraint Solver for 3D Lattices and Its Application to the Protein Folding Problem

The paper describes the formalization and implementation of an efficient constraint programming framework operating on 3D crystal lattices. The framework is motivated and applied to address the problem of solving the abinitio protein structure prediction problem - i.e., predicting the 3D structure of a protein from its amino acid sequence. Experimental results demonstrate that our novel approach offers up to a 3 orders of magnitude of speedup compared to other constraint-based solutions proposed for the problem at hand

A SAT-based hybrid solver for optimal control of hybrid systems

Combinatorial optimization over continuous and integer variables was proposed recently as a useful tool for solving complex optimal control problems for linear hybrid dynamical systems formulated in discrete-time. Current approaches are based on mixed-integer linear or quadratic programming (MIP), which provides the solution after solving a sequence of relaxed standard linear (or quadratic) programs (LP, QP). An MIP formulation has the drawback of requiring conversion of the discrete/logic part of the hybrid problem into mixed-integer inequalities. Although this operation can be done automatically, most of the original discrete structure of the problem is lost during the conversion. Moreover, the efficiency of the MIP solver mainly relies upon the tightness of the continuous LP/QP relaxations. In this paper we attempt to overcome such difficulties by combining MIP and techniques for solving constraint satisfaction problems into a “hybrid” solver, taking advantage of SAT solvers for dealing efficiently with satisfiability of logic constraints. We detail how to model the hybrid dynamics so that the optimal control problem can be solved by the hybrid MIP+SAT solver, and show that the achieved performance is superior to the one achieved by commercial MIP solvers