Core-guided MAXSAT algorithms dominate other methods in solving industrial MAXSAT problems. In this work, we propose a new efficient algorithm that is guided by correction sets and cores. At every iteration, the algorithm obtains a correction set or a core, which is then used to rewrite the formula using incremental and succinct transformations. We theoretically show that correction sets and cores have complementary strengths and empirically demonstrate that their combination leads to an efficient MAXSAT solver that outperforms state-of-the-art WPMS solvers on the 2014 Evaluation on industrial instances.

Solvers for the Maximum satisfiability (MaxSAT) problem find an increasing number of applications today. We focus on improving MaxHS — one of the most successful recent MaxSAT algorithms — via SAT-based preprocessing. We show that employing SAT-based preprocessing via the so-called labelled CNF (LCNF) framework before calling MaxHS can in some cases greatly degrade the performance of the solver. As a remedy, we propose a lifting of MaxHS that works directly on LCNFs, allowing for a tighter integration of SAT-based preprocessing and MaxHS. Our empirical results on standard crafted and industrial weighted partial MaxSAT Evaluation benchmarks show overall improvements over the original MaxHS algorithm both with and without SAT-based preprocessing.

In Constraint Programming (CP), a portfolio solver uses a variety of different solvers for solving a given Constraint Satisfaction / Optimization Problem. In this paper we introduce sunny-cp2: the first parallel CP portfolio solver that enables a dynamic, cooperative, and simultaneous execution of its solvers in a multicore setting. It incorporates state-of-the-art solvers, providing also a usable and configurable framework. Empirical results are very promising. sunny-cp2 can even outperform the performance of the oracle solver which always selects the best solver of the portfolio for a given problem.

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.

Online portfolio selection (PS) has been extensively studied in artificial intelligence and machine learning communities in recent years. An important practical issue of online PS is transaction cost, which is unavoidable and nontrivial in real financial trading markets. Most existing strategies, such as universal portfolio (UP) based strategies, often rebalance their target portfolio vectors at every investment period, and thus the total transaction cost increases rapidly and the final cumulative wealth degrades severely. To overcome the limitation, in this paper we investigate new investment strategies that rebalances its portfolio only at some selected instants. Specifically, we design a novel on-line PS strategy named semi-universal portfolio (SUP) strategy under transaction cost, which attempts to avoid rebalancing when the transaction cost outweighs the benefit of trading. We show that the proposed SUP strategy is universal and has an upper bound on the regret. We present an efficient implementation of the strategy based on non-uniform random walks and online factor graph algorithms. Empirical simulation on real historical markets show that SUP can overcome the drawback of existing UP based transaction cost aware algorithms and achieve significantly better performance. Furthermore, SUP has a polynomial complexity in the number of stocks and thus is efficient and scalable in practice.

Therefore, we have to rely on an AI algorithm Many collective human activities have been shown to fill in the blank spots. More precisely, we provide a relational to exhibit universal patterns. However, the possibility view on Label Propagation (LP) [Zhu et al., 2003; of regularities underlying researcher migration Bengio et al., 2006] and introduce a novel way to significantly in computer science (CS) has barely been explored speed it up based on equitable partitions. We call the resulting at global scale. To a large extend, this is due algorithm Compressed Label Propagation (CLP) because to official and commercial records being restricted, the original LPgraph is "lifted" or rather "compressed" before incompatible between countries, and especially not running vanilla LP on the smaller graph. Running CLP registered across researchers. We overcome these results in the first translational dataset for more than a million limitations by building our own, transnational, computer scientists on which we then learn statistical migration large-scale dataset inferred from publicly available models explaining the results in sociologically plausible information on the Web. Essentially, we use Label ways. To verify the quality of our inferred geo-tags and Propagation (LP) to infer missing geo-tags of statistical models, we additionally run CLP on an orders-ofmagnitude author-paper-pairs retrieved from online bibliographies.