We consider ordinal approximation algorithms for a broad class of utility maximization problems for multi-agent systems. In these problems, agents have utilities for connecting to each other, and the goal is to compute a maximum-utility solution subject to a set of constraints. We represent these as a class of graph optimization problems, including matching, spanning tree problems, TSP, maximum weight planar subgraph, and many others. We study these problems in the ordinal setting: latent numerical utilities exist, but we only have access to ordinal preference information, i.e., every agent specifies an ordering over the other agents by preference. We prove that for the large class of graph problems we identify, ordinal information is enough to compute solutions which are close to optimal, thus demonstrating there is no need to know the underlying numerical utilities. For example, for problems in this class with bounded degree b a simple ordinal greedy algorithm always produces a (b + 1)-approximation; we also quantify how the quality of ordinal approximation depends on the sparsity of the resulting graphs. In particular, our results imply that ordinal information is enough to obtain a 2-approximation for Maximum Spanning Tree; a 4-approximation for Max Weight Planar Subgraph; a 2-approximation for Max-TSP; and a 2- approximation for various Matching problems.

Treatable infectious diseases are a critical challenge for public health. Outreach campaigns can encourage undiagnosed patients to seek treatment but must be carefully targeted to make the most efficient use of limited resources. We present an algorithm to optimally allocate limited outreach resources among demographic groups in the population. The algorithm uses a novel multiagent model of disease spread which both captures the underlying population dynamics and is amenable to optimization. Our algorithm extends, with provable guarantees, to a stochastic setting where we have only a distribution over parameters such as the contact pattern between agents. We evaluate our algorithm on two instances where this distribution is inferred from real world data: tuberculosis in India and gonorrhea in the United States. Our algorithm produces a policy which is predicted to avert an average of least 8,000 person-years of tuberculosis and 20,000 person-years of gonorrhea annually compared to current policy.

In traditional models for word-of-mouth recommendations and viral marketing, the objective function has generally been based on reaching as many people as possible. However, a number of studies have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure, reaching people who evaluate it negatively. This can lead to an effect in which the over-promotion of a product can produce negative reputational effects, by reaching a part of the audience that is not receptive to it. How should one make use of social influence when there is a risk of overexposure? In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy. We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselines.

Pirate syndicates capturing tankers to siphon oil, causing an estimated cost of $5 billion a year, has become a serious security issue for maritime traffic. In response to the threat, coast guards and navies deploy patrol boats to protect international oil trade. However, given the vast area of the sea and the highly time and space dependent behaviors of both players, it remains a significant challenge to find efficient ways to deploy patrol resources. In this paper, we address the research challenges and provide four key contributions. First, we construct a Stackelberg model of the oil-siphoning problem based on incident reports of actual attacks; Second, we propose a compact formulation and a constraint generation algorithm, which tackle the exponentially growth of the defender’s and attacker’s strategy spaces, respectively, to compute efficient strategies of security agencies; Third, to further improve the scalability, we propose an abstraction method, which exploits the intrinsic similarity of defender’s strategy space, to solve extremely large-scale games; Finally, we evaluate our approaches through extensive simulations and a detailed case study with real ship traffic data. The results demonstrate that our approach achieves a dramatic improvement of scalability with modest influence on the solution quality and can scale up to realistic-sized problems.

Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour. A typical work-flow includes the manual annotation (also called coding) of social signals in multi-modal corpora of considerable size. For the involved annotators this defines an exhausting and time-consuming task. In the article at hand we present a novel method and also provide the tools to speed up the coding procedure. To this end, we suggest and evaluate the use of Cooperative Machine Learning (CML) techniques to reduce manual labelling efforts by combining the power of computational capabilities and human intelligence. The proposed CML strategy starts with a small number of labelled instances and concentrates on predicting local parts first. Afterwards, a session-independent classification model is created to finish the remaining parts of the database. Confidence values are computed to guide the manual inspection and correction of the predictions. To bring the proposed approach into application we introduce NOVA - an open-source tool for collaborative and machine-aided annotations. In particular, it gives labellers immediate access to CML strategies and directly provides visual feedback on the results. Our experiments show that the proposed method has the potential to significantly reduce human labelling efforts.

Blockchain systems and ubiquitous computing are changing the way we do business and lead our lives. Its easy to compare blockchain with multi-agents systems communication a technology, which provides a way for multiple interacting intelligent agents to communicate with each other and with environment. Blockchain is a mystery story or provides the foundation for cryptocurrencies like Bitcoin. Some time I get confused with my school time memories data structure; ehe way blockchain is represented as a singly linked list. Each block has a hash of the previous block which can be thought of as a pointer to previous block.

Because the groups are SMARTER when thinking TOGETHER than the individuals would be on their own.

We consider a team of reinforcement learning agents that concurrently learn to operate in a common environment. We identify three properties - adaptivity, commitment, and diversity - which are necessary for efficient coordinated exploration and demonstrate that straightforward extensions to single-agent optimistic and posterior sampling approaches fail to satisfy them. As an alternative, we propose seed sampling, which extends posterior sampling in a manner that meets these requirements. Simulation results investigate how per-agent regret decreases as the number of agents grows, establishing substantial advantages of seed sampling over alternative exploration schemes.

Competitive sports are ultimately numbers games. Whether it's a gymnast racking up points on a balance beam, a tennis player acing her opponent, or a football team scoring on a last second Hail Mary, all matches are won and lost by numbers. There are upsets, comebacks, and situations when the losing team still seems to outperform the other -- but, even then, victory distills into digits. As such, it's obvious that many sports lend themselves nicely to the type of mathematical analyses that let keen-eyed statisticians predict outcomes -- maybe even exact scores -- just by crunching a bunch of numbers. After all, that's the basis of sports betting, and it's helped baseball managers craft winning teams on a tight budget just by considering little more than batting average, runs batted in, and stolen bases.