OR-constrained (ORC) graphical user interface layouts unify conventional constraint-based layouts with flow layouts, which enables the definition of flexible layouts that adapt to screens with different sizes, orientations, or aspect ratios with only a single layout specification. Unfortunately, solving ORC layouts with current solvers is time-consuming and the needed time increases exponentially with the number of widgets and constraints. To address this challenge, we propose ORCSolver, a novel solving technique for adaptive ORC layouts, based on a branch-and-bound approach with heuristic preprocessing. We demonstrate that ORCSolver simplifies ORC specifications at runtime and our approach can solve ORC layout specifications efficiently at near-interactive rates.

The aim of this work is to provide a family of qualitative theories for spatial change in general, and for motion of spatial scenes in particular. To achieve this, we consider a spatio-temporalisation MTALC(Dx), of the well-known ALC(D) family of Description Logics (DLs) with a concrete domain: the MTALC(Dx) concepts are interpreted over infinite k-ary Sigma-trees, with the nodes standing for time points, and Sigma including, additionally to its uses in classical k-ary Sigma-trees, the description of the snapshot of an n-object spatial scene of interest; the roles split into m+n immediate-successor (accessibility) relations, which are serial, irreflexive and antisymmetric, and of which m are general, not necessarily functional, the other n functional; the concrete domain Dx is generated by an RCC8-like spatial Relation Algebra (RA) x, and is used to guide the change by imposing spatial constraints on objects of the "followed" spatial scene, eventually at different time points of the input trees. In order to capture the expressiveness of most modal temporal logics encountered in the literature, we introduce weakly cyclic Terminological Boxes (TBoxes) of MTALC(Dx), whose axioms capture the decreasing property of modal temporal operators. We show the important result that satisfiability of an MTALC(Dx) concept with respect to a weakly cyclic TBox can be reduced to the emptiness problem of a Buchi weak alternating automaton augmented with spatial constraints. In another work, complementary to this one, also submitted to this conference, we thoroughly investigate Buchi automata augmented with spatial constraints, and provide, in particular, a translation of an alternating into a nondeterministic, and an effective decision procedure for the emptiness problem of the latter.

TCSPs (Temporal Constraint Satisfaction Problems), as defined in [Dechter et al., 1991], get rid of unary constraints by binarising them after having added an "origin of the world" variable. The constraints are therefore exclusively binary; additionally, a TCSP verifies the property that it is node-consistent and arc-consistent. Path-consistency, the next higher local consistency, solves the consistency problem of a convex TCSP, referred to in [Dechter et al., 1991] as an STP (Simple Temporal Problem); more than that, the output of path-consistency applied to an n+1-variable STP is a minimal and strongly n+1-consistent STP. Weaker versions of path-consistency, aimed at avoiding what is referred to in [Schwalb and Dechter, 1997] as the "fragmentation problem", are used as filtering procedures in recursive backtracking algorithms for the consistency problem of a general TCSP. In this work, we look at the constraints between the "origin of the world" variable and the other variables, as the (binarised) domains of these other variables. With this in mind, we define a notion of arc-consistency for TCSPs, which we will refer to as binarised-domains Arc-Consistency, or bdArc-Consistency for short. We provide an algorithm achieving bdArc-Consistency for a TCSP, which we will refer to as bdAC3, for it is an adaptation of Mackworth's [1977] well-known arc-consistency algorithm AC3. We show that bdArc-Consistency computes the minimal (binarised) domains of an STP. We then show how to use the result in a general TCSP solver, in a TCSP-based job shop scheduler, and in generalising the well-known Dijkstra's one-to-all shortest paths algorithm.

Cost Function Networks (CFN) are a formalism in Constraint Programming to model combinatorial satisfaction or optimization problems. By associating a function to each constraint type to evaluate the quality of an assignment, it extends the expressivity of regular CSP/COP formalisms but at a price of making harder the problem modeling. Indeed, in addition to regular variables/domains/constraints sets, one must provide a set of cost functions that are not always easy to define. Here we propose a method to automatically learn a cost function of a constraint, given a function deciding if assignments are valid or not. This is to the best of our knowledge the first attempt to automatically learn cost functions. Our method aims to learn cost functions in a supervised fashion, trying to reproduce the Hamming distance, by using a variation of neural networks we named Interpretable Compositional Networks, allowing us to get explainable results, unlike regular artificial neural networks. We experiment it on 5 different constraints to show its versatility. Experiments show that functions learned on small dimensions scale on high dimensions, outputting a perfect or near-perfect Hamming distance for most constraints. Our system can be used to automatically generate cost functions and then having the expressivity of CFN with the same modeling effort than for CSP/COP.

Also, many supplychain & logistics challenges are time, cost and resource constraint based, making ML an ideal technology to solve.

In (a), the default settings are used (bitruncated order-3 heptagonal tessellation, numbers of adjacent mines are color-coded; some of the mines that the players is sure of are marked red). In (b) we play on an order-3 heptagonal tessellation, and numbers are shown. Minesweeper is a popular game included with many computer systems; it also exists in the puzzle form. In the puzzle form, every cell in a square grid either contains a number or is empty.

In this work we present a new method of black-box optimization and constraint satisfaction. Existing algorithms that have attempted to solve this problem are unable to consider multiple modes, and are not able to adapt to changes in environment dynamics. To address these issues, we developed a modified Cross-Entropy Method (CEM) that uses a masked auto-regressive neural network for modeling uniform distributions over the solution space. We train the model using maximum entropy policy gradient methods from Reinforcement Learning. Our algorithm is able to express complicated solution spaces, thus allowing it to track a variety of different solution regions. We empirically compare our algorithm with variations of CEM, including one with a Gaussian prior with fixed variance, and demonstrate better performance in terms of: number of diverse solutions, better mode discovery in multi-modal problems, and better sample efficiency in certain cases.

This paper proposes constraint propagation relaxation (CPR), a probabilistic approach to classical constraint propagation that provides another view on the whole parametric family of survey propagation algorithms SP(ρ), ranging from belief propagation (ρ 0) to (pure) survey propagation(ρ 1). Papers published at the Neural Information Processing Systems Conference.

We propose a novel inference framework for finding maximal cliques in a weighted graph that satisfy hard constraints. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., sets of nodes that cannot belong to the same solution. The proposed inference is based on a novel particle filter algorithm with state permeations. We apply the inference framework to a challenging problem of learning part-based, deformable object models. Two core problems in the learning framework, matching of image patches and finding salient parts, are formulated as two instances of the problem of finding maximal cliques with hard constraints.

Several algorithms for solving constraint satisfaction problems are based on survey propagation, a variational inference scheme used to obtain approximate marginal probability estimates for variable assignments. These marginals correspond to how frequently each variable is set to true among satisfying assignments, and are used to inform branching decisions during search; however, marginal estimates obtained via survey propagation are approximate and can be self-contradictory. We introduce a more general branching strategy based on streamlining constraints, which sidestep hard assignments to variables. We show that streamlined solvers consistently outperform decimation-based solvers on random k-SAT instances for several problem sizes, shrinking the gap between empirical performance and theoretical limits of satisfiability by 16.3% on average for k 3, 4, 5, 6. Papers published at the Neural Information Processing Systems Conference.