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.

We study computational problems for two popular parliamentary voting procedures: the amendment procedure and the successive procedure. While finding successful manipulations or agenda controls is tractable for both procedures, our real-world experimental results indicate that most elections cannot be manipulated by a few voters and agenda control is typically impossible. If the voter preferences are incomplete, then finding possible winners is NP-hard for both procedures. Whereas finding necessary winners is coNP-hard for the amendment procedure, it is polynomial-time solvable for the successive one.

However, humans are proved to question: What is the connection between the arguments proposed behave differently, mixing rational and emotional by the participants of a debate and their emotional attitudes to guide their actions, and it has been status? Such question breaks down into the following subquestions: claimed that there exists a strong connection between (1) is the polarity of arguments and the relations the argumentation process and the emotions among them correlated with the polarity of the detected emotions?, felt by people involved in such process. In this paper, and (2) what is the relation between the kind and the we assess this claim by means of an experiment: amount of arguments proposed in a debate, and the mental during several debates people's argumentation engagement detected among the participants of the debate? in plain English is connected and compared to the emotions automatically detected from the participants. To answer these questions, we propose an empirical evaluation Our results show a correspondence between of the connection between argumentation and emotions.

We study the problem of how to optimize a cloud service provider's pricing policy so as to better compete with other providers. Different from previous work, we take both the evolution of the market and the competition between multiple cloud providers into consideration while optimizing the pricing strategy for the provider. Inspired by the real situations in today's cloud market, we consider a situation in which there is only one provider who actively optimizes his/her pricing policy, while other providers adopt a follow-up policy to match his/her price cut. To compute optimal pricing policy under the above settings, we decompose the optimization problem into two steps: (1) When the market finally becomes saturated, we use Q-learning, a method of reinforcement learning, to derive an optimal pricing policy for the stationary market; (2) Based on the optimal policy for the stationary market, we use backward induction to derive an optimal pricing policy for the situation of competition in an evolutionary market. Numerical simulations demonstrate the effectiveness of our proposed approach.

However, as pointed out by [Moran et al., 2013], such We consider a setting where an agent-based planner assumptions simply do not hold in reality. The environment instructs teams of human emergency responders to is typically prone to significant uncertainties and humans may perform tasks in the real world. Due to uncertainty reject plans suggested by a software agent if they are tired or in the environment and the inability of the planner prefer to work with specific partners. Now, a naïve solution to consider all human preferences and all attributes to this would involve re-planning every time a rejection is of the real-world, humans may reject plans received. However, this may instead result in a high computational computed by the agent. A naïve solution that replans cost (as a whole new plan needs to be computed for given a rejection is inefficient and does not the whole team), may generate a plan that is still not acceptable, guarantee the new plan will be acceptable. Hence, and, following multiple rejection/replanning cycles (as we propose a new model re-planning problem using all individual team members need to accept the new plan), a Multi-agent Markov Decision Process that may lead the teams to suboptimal solutions.

Despite some success of Perfect Information Monte Carlo Sampling (PIMC) in imperfect information games in the past, it has been eclipsed by other approaches in recent years. Standard PIMC has well-known shortcomings in the accuracy of its decisions, but has the advantage of being simple, fast, robust and scalable, making it well-suited for imperfect information games with large state-spaces. We propose Presumed Value PIMC resolving the problem of overestimation of opponent's knowledge of hidden information in future game states. The resulting AI agent was tested against human experts in Schnapsen, a Central European 2-player trick-taking card game, and performs above human expert-level.

We investigate the problem of revenue optimal mechanism design [Myerson, 1981] under the context of the partial rationality model, where buyers randomize between two modes: rational and irrational. When a buyer is irrational (can be thought of as lazy), he acts according to certain fixed strategies, such as bidding his true valuation. The seller cannot observe the buyer’s valuation, or his rationality mode, but treat them as random variables from known distributions. The seller’s goal is to design a single-shot auction that maximizes her expected revenue. A minor generalization as it may seem, our findings are in sharp contrast to Myerson’s theory on the standard rational bidder case. In particular, we show that, even for the simplest setting with one buyer, direct value revelation loses generality. However, we do show that, in terms of revenue, the optimal value-revelation and type-revelation mechanisms are equivalent. In addition, the posted-price mechanism is no longer optimal. In fact, the more complicated the mechanism, the higher the revenue. For the case where there are multiple bidders with IID uniform valuations, we show that when the irrational buyers are truthful, first price auction yields more revenue than second price auction.