We increase the usefulness of Chu and Stuckey's work by automating it, that is, by developing a method to (a) automatically The exploitation of dominance relations in constraint identify symmetries for a given problem, and (b) optimization problems can lead to dramatic automatically construct the associated dominance breaking reductions in search space. We propose an automatic constraints. Note that these dominance breaking constraints method to detect some of the dominance relations are not symmetry breaking constraints, as the key element manually identified by Chu and Stuckey for is for f(σ(θ)) to be better. Further, the symmetries need to optimization problems, and to construct the associated be detected for the inherent satisfaction problem -- that is, dominance breaking constraints. Experimental the problem without the objective function -- or, otherwise, results show that the method is able to find several f(σ(θ)) will be equal to f(θ), not better.

Presolving is a preprocessing step performed by optimisation solvers to improve performance. However, these solvers cannot easily exploit high-level model structure as available in modelling languages such as MiniZinc or Essence. We present an integrated approach that performs presolving as a separate pass during the compilation from high-level optimisation models to solver-level programs. The compiler produces a representation of the model that is suitable for presolving by retaining some of the high-level structure. It then uses information learned during presolving to generate the final solver-level representation. Our approach introduces the novel concept of variable paths that identify variables which are common across multiple compilation passes, increasing the amount of shared information. We show that this approach can lead to both faster compilation and more efficient solver-level programs.

Specifically, we present a top-down algorithm cn2mddg for compiling finite-domain CNs into multivalued decomposable We present and evaluate a top-down algorithm for decision graphs. The input of cn2mddg is a CN compiling finite-domain constraint networks (CNs) represented in the XCSP 2.1 format [Roussel and Lecoutre, into the language MDDG of multivalued decomposable 2009]. The output of our compilation algorithm is a representation decision graphs. Though it includes Decision-of the solutions of the CN in the language MDDG DNNF as a proper subset, MDDG offers the same key of multivalued decomposable decision graphs. MDDG is precisely tractable queries and transformations as Decision-the extension to non-Boolean domains of the language DNNF, which makes it useful for many applications. DDG [Fargier and Marquis, 2006] also known as Decision-Intensive experiments showed that our compiler DNNF [Oztok and Darwiche, 2014]: it is based on decomposable cn2mddg succeeds in compiling CNs which -nodes and (multivalued) decision nodes. Similarly are out of the reach of standard approaches based to Decision-DNNF, the MDDG language offers a number of on a translation of the input network to CNF, followed tractable queries, including (possibly weighted) solution finding by a compilation to Decision-DNNF. Furthermore, and counting, solution enumeration (solutions can be enumerated the sizes of the resulting compiled representations with polynomial delay), and optimization w.r.t. a linear turn out to be much smaller (sometimes by objective function. It also offers tractable transformations, several orders of magnitude).

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems — that are in general NP-hard — efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on a selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques based on features of the decomposition rather than on the actual problem instance. We report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

At each node of the search tree, Branch and Bound solvers for Max-SAT compute the lower bound (LB) by estimating the number of disjoint inconsistent subsets (IS) of the formula. IS are detected thanks to unit propagation (UP) then transformed by max-resolution to ensure that they are counted only once. However, it has been observed experimentally that the max-resolution transformations impact the capability of UP to detect further IS. Consequently, few transformations are learned and the LB computation is redundant. In this paper, we study the effect of the transformations on the UP mechanism. We introduce the notion of UP-resiliency of a transformation, which quantifies its impact on UP. It provides, from a theoretical point of view, an explanation to the empirical efficiency of the learning scheme developed in the last ten years. The experimental results we present give evidences of UP-resiliency relevance and insights on the behavior of the learning mechanism.

Standard results on and algorithms for repeated games assume that defections are instantly observable. In reality, it may take some time for the knowledge that a defection has occurred to propagate through the social network. How does this affect the structure of equilibria and algorithms for computing them? In this paper, we consider games with cooperation and defection. We prove that there exists a unique maximal set of forever-cooperating agents in equilibrium and give an efficient algorithm for computing it. We then evaluate this algorithm on random graphs and find experimentally that there appears to be a phase transition between cooperation everywhere and defection everywhere, based on the value of cooperation and the discount factor. Finally, we provide a condition for when the equilibrium found is credible, in the sense that agents are in fact motivated to punish deviating agents. We find that this condition always holds in our experiments, provided the graphs are sufficiently large.

We explore the mechanism design problem for lung exchange and its implementation in practice. We prove that determining whether there exists a non-trivial solution of the lung exchange problem is NP-complete. We propose a mechanism that is individually rational, strategy-proof and maximizes exchange size. To implement this mechanism in practice, we propose an algorithm based on Integer Linear Program and another based on search. Both of our algorithms for this mechanism yield excellent performances in simulated data sets.

Recent work has shown that the quality of work produced in a crowdsourcing working session can be influenced by the presence of performance-contingent financial incentives, such as bonuses for exceptional performance, in the session. We take an algorithmic approach to decide when to offer bonuses in a working session to improve the overall utility that a requester derives from the session. Specifically, we propose and train an input-output hidden Markov model to learn the impact of bonuses on work quality and then use this model to dynamically decide whether to offer a bonus on each task in a working session to maximize a requester’s utility. Experiments on Amazon Mechanical Turk show that our approach leads to higher utility for the requester than fixed and random bonus schemes do. Simulations on synthesized data sets further demonstrate the robustness of our approach against different worker population and worker behavior in improving requester utility.

Exponential Random Graphs Models (ERGM) are common, simple statistical models for social network and other network structures. Unfortunately, inference and learning with them is hard even for small networks because their partition functions are intractable for precise computation. In this paper, we introduce a new quadratic time deterministic approximation to these partition functions. Our main insight enabling this advance is that subgraph statistics is sufficient to derive a lower bound for partition functions given that the model is not dominated by a few graphs. The proposed method differs from existing methods in its ways of exploiting asymptotic properties of subgraph statistics. Compared to the current Monte Carlo simulation based methods, the new method is scalable, stable, and precise enough for inference tasks.

Argumentation mining aims to automatically identify structured argument data from unstructured natural language text. This challenging, multi-faceted task is recently gaining a growing attention, especially due to its many potential applications. One particularly important aspect of argumentation mining is claim identification. Most of the current approaches are engineered to address specific domains. However, argumentative sentences are often characterized by common rhetorical structures, independently of the domain. We thus propose a method that exploits structured parsing information to detect claims without resorting to contextual information, and yet achieve a performance comparable to that of state-of-the-art methods that heavily rely on the context.