2,066 research outputs found

    An exploration strategy for non-stationary opponents

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    The success or failure of any learning algorithm is partially due to the exploration strategy it exerts. However, most exploration strategies assume that the environment is stationary and non-strategic. In this work we shed light on how to design exploration strategies in non-stationary and adversarial environments. Our proposed adversarial drift exploration (DE) is able to efficiently explore the state space while keeping track of regions of the environment that have changed. This proposed exploration is general enough to be applied in single agent non-stationary environments as well as in multiagent settings where the opponent changes its strategy in time. We use a two agent strategic interaction setting to test this new type of exploration, where the opponent switches between different behavioral patterns to emulate a non-deterministic, stochastic and adversarial environment. The agent’s objective is to learn a model of the opponent’s strategy to act optimally. Our contribution is twofold. First, we present DE as a strategy for switch detection. Second, we propose a new algorithm called R-max# for learning and planning against non-stationary opponent. To handle such opponents, R-max# reasons and acts in terms of two objectives: (1) to maximize utilities in the short term while learning and (2) eventually explore opponent behavioral changes. We provide theoretical results showing that R-max# is guaranteed to detect the opponent’s switch and learn a new model in terms of finite sample complexity. R-max# makes efficient use of exploration experiences, which results in rapid adaptation and efficient DE, to deal with the non-stationary nature of the opponent. We show experimentally how using DE outperforms the state of the art algorithms that were explicitly designed for modeling opponents (in terms average rewards) in two complimentary domains

    Analysis of Climate Policy Targets under Uncertainty

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    Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).Although policymaking in response to the climate change is essentially a challenge of risk management, most studies of the relation of emissions targets to desired climate outcomes are either deterministic or subject to a limited representation of the underlying uncertainties. Monte Carlo simulation, applied to the MIT Integrated Global System Model (an integrated economic and earth system model of intermediate complexity), is used to analyze the uncertain outcomes that flow from a set of century-scale emissions targets developed originally for a study by the U.S. Climate Change Science Program. Results are shown for atmospheric concentrations, radiative forcing, sea ice cover and temperature change, along with estimates of the odds of achieving particular target levels, and for the global costs of the associated mitigation policy. Comparison with other studies of climate targets are presented as evidence of the value, in understanding the climate challenge, of more complete analysis of uncertainties in human emissions and climate system response.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

    Deep Colorization for Facial Gender Recognition

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    Radio frequency traffic classification over WLAN

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    Network traffic classification is the process of analyzing traffic flows and associating them to different categories of network applications. Network traffic classification represents an essential task in the whole chain of network security. Some of the most important and widely spread applications of traffic classification are the ability to classify encrypted traffic, the identification of malicious traffic flows, and the enforcement of security policies on the use of different applications. Passively monitoring a network utilizing low-cost and low-complexity wireless local area network (WLAN) devices is desirable. Mobile devices can be used or existing office desktops can be temporarily utilized when their computational load is low. This reduces the burden on existing network hardware. The aim of this paper is to investigate traffic classification techniques for wireless communications. To aid with intrusion detection, the key goal is to passively monitor and classify different traffic types over WLAN to ensure that network security policies are adhered to. The classification of encrypted WLAN data poses some unique challenges not normally encountered in wired traffic. WLAN traffic is analyzed for features that are then used as an input to six different machine learning (ML) algorithms for traffic classification. One of these algorithms (a Gaussian mixture model incorporating a universal background model) has not been applied to wired or wireless network classification before. The authors also propose a ML algorithm that makes use of the well-known vector quantization algorithm in conjunction with a decision tree—referred to as a TRee Adaptive Parallel Vector Quantiser. This algorithm has a number of advantages over the other ML algorithms tested and is suited to wireless traffic classification. An average F-score (harmonic mean of precision and recall) > 0.84 was achieved when training and testing on the same day across six distinct traffic types
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