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.

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.

Exploiting parallelism is a key strategy for speeding up computation. However, on hard combinatorial problems, such a strategy has been surprisingly challenging due to the intricate variable interactions.We introduce a novel way in which parallelism can be used to exploit hidden structure of hard combinatorial problems. Our approach complements divide-and-conquer and portfolio approaches. We evaluate our approach on the minimum set basis problem: a core combinatorial problem with a range of applications in optimization, machine learning, and system security. We also highlight a novel sustainability related application, concerning the discovery of new materials for renewable energy sources such as improved fuel cell catalysts. In our approach, a large number of smaller sub-problems are identified and solved concurrently. We then aggregate the information from those solutions, and use this information to initialize the search of a global, complete solver. We show that this strategy leads to a substantial speed-up over a sequential approach, since the aggregated sub-problem solution information often provides key structural insights to the complete solver. Our approach also greatly outperforms state-of-the-art incomplete solvers in terms of solution quality. Our work opens up a novel angle for using parallelism to solve hard combinatorial problems.

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.

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.

Pricing-based mechanisms have been widely studied and developed for resource allocation in multi-agent systems. One of the main goals in such studies is to avoid envy between the agents, i.e., guarantee fair allocation. However, even the simplest combinatorial cases of this problem is not well understood. Here, we try to fill these gaps and design polynomial revenue maximizing pricing mechanisms to allocate homogeneous resources among buyers in envy-free manner. In particular, we consider envy-free outcomes in which all buyers' utilities are maximized. We also consider pair envy-free outcomes in which all buyers prefer their allocations to the allocations obtained by other agents. For both notions of envy-freeness, we consider item and bundle pricing schemes. Our results clearly demonstrate the limitations and advantages in terms of revenue between these two different notions of envy-freeness.

We consider three important, non-strategyproof assignment mechanisms: Probabilistic Serial and two variants of the Boston mechanism. Under each of these mechanisms, we study the agent’s manipulation problem of determining a best response, i.e., a report that maximizes the agent’s expected utility. In particular, we consider local manipulation strategies, which are simple heuristics based on local, greedy search. We make three main contributions. First, we present results from a behavioral experiment (conducted on Amazon Mechanical Turk) which demonstrate that human manipulation strategies can largely be explained by local manipulation strategies. Second, we prove that local manipulation strategies may fail to solve the manipulation problem optimally. Third, we show via large-scale simulations that despite this non-optimality, these strategies are very effective on average. Our results demonstrate that while the manipulation problem may be hard in general, even cognitively or computationally bounded (human) agents can find near-optimal solutions almost all the time via simple local search strategies.

While a variety of persuasion agents have been created and applied in different domains such as marketing, military training and health industry, there is a lack of a model which can provide a unified framework for different persuasion strategies. Specifically, persuasion is not adaptable to the individuals' personal states in different situations. Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents. MAP is a semi-connected network model which enables an agent to adapt its persuasion strategies through feedback. We have implemented and evaluated a MAP-based virtual nurse agent who takes care and recommends healthy lifestyle habits to the elderly. Our experimental results show that the MAP-based agent is able to change the others' attitudes and behaviors intentionally, interpret individual differences between users, and adapt to user's behavior for effective persuasion.

Costly preference elicitation has also been studied The last two decades have seen a growing interest in the setting of auctions; notably by Conen and Sandholm in the development of automated agents that are [2001] and Parkes [2005]. These works are primarily able to negotiate on the user's behalf. When representing aimed at designing mechanisms that can avoid unnecessary a user in a negotiation, it is essential for the elicitation. Costly preference elicitation may alternatively be agent to understand the user's preferences, without cast as a problem in which agents have to allocate costly computational exposing them to elicitation fatigue. To this end, we resources to compute their valuation [Larson and propose a new model in which a negotiating agent Sandholm, 2001], but this work focuses on interactions between may incrementally elicit the user's preference during different strategies.

Professor Higgins Professor of Natural Sciences at the School of Engineering and Natural Selman is recognized for expanding our understanding of problem Sciences, Harvard University. Professor Grosz is recognized for her pioneering complexity and developing new algorithms for efficient inference. Previous recipients have been Bernard outstanding young scientists in artificial intelligence. It is currently supported by income Grosz (2001), Alan Bundy (2003), Raj Reddy (2005), Ronald J. Brachman from IJCAI funds. Past recipients of this honor have been Terry (2007), Luigia Carlucci Aiello (2009), Raymond C. Perrault (2011), and Winograd (1971), Patrick Winston (1973), Chuck Rieger (1975), Douglas Wolfgang Wahlster (2013).