Search

Social dilemmas are situations of inter-dependent decision making in which individual rationality can lead to outcomes with poor social qualities. The ubiquity of social dilemmas in social, biological, and computational systems has generated substantial research across these diverse disciplines into the study of mechanisms for avoiding deficient outcomes by promoting and maintaining mutual cooperation. Much of this research is focused on studying how individuals faced with a dilemma can learn to cooperate by adapting their behaviours according to their past experience. In particular, three types of learning approaches have been studied: evolutionary game-theoretic learning, reinforcement learning, and best-response learning. This article is a comprehensive integrated survey of these learning approaches in the context of dilemma games. We formally introduce dilemma games and their inherent challenges. We then outline the three learning approaches and, for each approach, provide a survey of the solutions proposed for dilemma resolution. Finally, we provide a comparative summary and discuss directions in which further research is needed. [ABSTRACT FROM AUTHOR]

We consider an outsourcing problem where a software agent procures multiple services from providers with uncertain reliabilities to complete a computational task before a strict deadline. The service consumer's goal is to design an outsourcing strategy (defining which services to procure and when) so as to maximize a specific objective function. This objective function can be different based on the consumer's nature; a socially-focused consumer often aims to maximize social welfare, while a self-interested consumer often aims to maximize its own utility. However, in both cases, the objective function depends on the providers' execution costs, which are privately held by the self-interested providers and hence may be misreported to influence the consumer's decisions. For such settings, we develop a unified approach to design truthful procurement auctions that can be used by both socially-focused and, separately, self-interested consumers. This approach benefits from our proposed weighted threshold payment scheme which pays the provably minimum amount to make an auction with a monotone outsourcing strategy incentive compatible. This payment scheme can handle contingent outsourcing plans, where additional procurement happens gradually over time and only if the success probability of the already hired providers drops below a time-dependent threshold. Using a weighted threshold payment scheme, we design two procurement auctions that maximize, as well as two low-complexity heuristic-based auctions that approximately maximize, the consumer's expected utility and expected social welfare, respectively. We demonstrate the effectiveness and strength of our proposed auctions through both game-theoretical and empirical analysis. [ABSTRACT FROM AUTHOR]

Recently, intelligent scheduling approaches using surrogate models have been proposed to efficiently allocate volatile tasks in heterogeneous fog environments. Advances like deterministic surrogate models, deep neural networks (DNN) and gradient-based optimization allow low energy consumption and response times to be reached. However, deterministic surrogate models, which estimate objective values for optimization, do not consider the uncertainties in the distribution of the Quality of Service (QoS) objective function that can lead to high Service Level Agreement (SLA) violation rates. Moreover, the brittle nature of DNN training and the limited exploration with low agility in gradient-based optimization prevent such models from reaching minimal energy or response times. To overcome these difficulties, we present a novel scheduler that we call GOSH for Gradient Based Optimization using Second Order derivatives and Heteroscedastic Deep Surrogate Models. GOSH uses a second-order gradient based optimization approach to obtain better QoS and reduce the number of iterations to converge to a scheduling decision, subsequently lowering the scheduling time. Instead of a vanilla DNN, GOSH uses a Natural Parameter Network (NPN) to approximate objective scores. Further, a Lower Confidence Bound (LCB) optimization approach allows GOSH to find an optimal trade-off between greedy minimization of the mean latency and uncertainty reduction by employing error-based exploration. Thus, GOSH and its co-simulation based extension GOSH*, can adapt quickly and reach better objective scores than baseline methods. We show that GOSH* reaches better objective scores than GOSH, but it is suitable only for high resource availability settings, whereas GOSH is apt for limited resource settings. Real system experiments for both GOSH and GOSH* show significant improvements against the state-of-the-art in terms of energy consumption, response time and SLA violations by up to 18, 27 and 82 percent, respectively. [ABSTRACT FROM AUTHOR]

Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements. Recently proposed scheduling methods leverage the low response times of edge computing platforms to optimize application Quality of Service (QoS). However, scheduling workflow applications in mobile edge-cloud systems is challenging due to computational heterogeneity, changing latencies of mobile devices and the volatile nature of workload resource requirements. To overcome these difficulties, it is essential, but at the same time challenging, to develop a long-sighted optimization scheme that efficiently models the QoS objectives. In this work, we propose MCDS: Monte Carlo Learning using Deep Surrogate Models to efficiently schedule workflow applications in mobile edge-cloud computing systems. MCDS is an Artificial Intelligence (AI) based scheduling approach that uses a tree-based search strategy and a deep neural network-based surrogate model to estimate the long-term QoS impact of immediate actions for robust optimization of scheduling decisions. Experiments on physical and simulated edge-cloud testbeds show that MCDS can improve over the state-of-the-art methods in terms of energy consumption, response time, SLA violations and cost by at least 6.13, 4.56, 45.09 and 30.71 percent respectively. [ABSTRACT FROM AUTHOR]

Heart disease kills more people around the world than any other disease, and it is one of the leading causes of death in the UK, triggering up to 74,000 deaths per year. An essential part in the prevention of deaths by heart disease and thus heart disease itself is the analysis of biomedical markers to determine the risk of a person developing heart disease. Lots of research has been conducted to assess the accuracy of detecting heart disease by analyzing biomedical markers. However, no previous study has attempted to identify the biomedical markers which are most important in this identification. To solve this problem, we proposed a machine learning-based intelligent heart disease prediction system called BioLearner for the determination of vital biomedical markers. This study aims to improve upon the accuracy of predicting heart disease and identify the most essential biological markers. This is done with the intention of composing a set of markers that impacts the development of heart disease the most. Multiple factors determine whether or not a person develops heart disease. These factors are thought to include age, history of chest pain (of different types), fasting blood sugar of different types, heart rate, smoking, and other essential factors. The dataset is analyzed, and the different aspects are compared. Various machine learning models such as K Nearest Neighbours, Neural Networks, Support Vector Machine (SVM) are trained and used to determine the accuracy of our prediction for future heart disease development. BioLearner is able to predict the risk of heart disease with an accuracy of 95%, much higher than the baseline methods. [ABSTRACT FROM AUTHOR]