Search Results - Computing methodologies Machine learning Machine learning approaches Stochastic games~

Distributed Multiuser Computation Offloading for Cloudlet-Based Mobile Cloud Computing: A Game-Theoretic Machine Learning Approach by Cao, Huijin, Cai, Jun

“…In this paper, we investigate the problem of multiuser computation offloading for cloudlet-based mobile cloud computing in a multichannel wireless contention environment…”
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Multi-Structural Games and Number of Quantifiers by Fagin, Ronald, Lenchner, Jonathan, Regan, Kenneth W., Vyas, Nikhil

“…We study multi-structural games, played on two sets {\mathcal{A}} and {\mathcal{B}} of structures. These games generalize Ehrenfeucht…”
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Markov decision process routing games by Calderone, Dan, Sastry, S. Shankar

“… This work can be thought of as a routing-game-based formulation of continuous population stochastic games…”
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A peer-to-peer simulation technique for instanced massively multiplayer games by Cecin, Fabio Reis, Geyer, Claudio Fernando Resin, Rabello, Solon, Barbosa, Jorge Luis Victoria

“…We propose a peer-to-peer event ordering and simulation technique aimed at networked real-time action games…”
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The Distinctive Challenges of Pervasive Games as Distributed Real-Time Applications by Benford, Steve

“…Pervasive games are an emerging form of distributed real-time application that extend computer gaming out into the real world…”
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Social Collaboration, Stochastic Strategies and Information Referrals by Nowostawski, Mariusz, Foukia, Noria

“…Referrals are used in multi-agent systems, network agents and peer-to-peer systems for the purpose of global or local information spreading to facilitate trust…”
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A Game Theoretic Approach to Active Distributed Data Mining by Zhang, Xiaofeng, Cheung, William K.

“…Learning-from-abstraction(LFA) is a recently proposed model-based distributed data mining approach which aims to the mining process both scalable and privacy preserving…”
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Designing the Market Game for a Commodity Trading Simulation by Cheng, Shih-Fen

“…In this paper, we propose to design a market game that (a) can be used in modeling and studying commodity trading scenarios, and (b…”
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How Relevant is Game Theory to Intelligent Agent Technology? by Shoham, Professor Yoav

“…At this point, restricted to rather specialized areas, and even there must be taken with a grain of salt. But at the same time you can't afford not to know it;…”
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Modeling as Craft by Fishwick, Paul

“… and engaging.Interactive computer games provide evengreater flexibility, while lacking in tactilefeedback and the sort of physical immersionwe expect from every day experience…”
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Gene mutation estimations via mutual information and Ewens sampling based CNN & machine learning algorithms by Dai, Wanyang

“…) and machine learning algorithms. More precisely, we develop a systematic methodology through constructing a CNN…”
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Path lookahead: a data flow view of PDES models by Meyer, Richard A., Bargrodia, Rajive L.

“…Architectural advances are making PDES more difficult. Processor speed improves much more quickly than interprocessor communication and memory. Lagging memory…”
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Solving two-armed Bernoulli bandit problems using a Bayesian learning automaton by Granmo, Ole-Christoffer

“…: the Bayesian learning automaton (BLA) family.Design methodology approach - Although computationally intractable in many cases, Bayesian methods provide a standard for optimal decision making…”
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Stability and Accuracy-Aware Learning for Task Offloading in UAV-MEC-Assisted Smart Farms by Khoramnejad, Fahime, Syed, Aisha, Kennedy, W. Sean, Erol-Kantarci, Melike

“…), and Mobile Edge Computing (MEC) technology. At the heart of our methodology is the utilization of multi-agent reinforcement learning (RL…”
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Best-Response Multiagent Learning in Non-Stationary Environments by Weinberg, Michael, Rosenschein, Jeffrey S.

“… Our learning algorithm works within the very general framework of n-player, general-sum stochastic games, and learns both the game structure and its associated optimal policy…”
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Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning by Mannion, Patrick, Devlin, Sam, Duggan, Jim, Howley, Enda

“…The majority of multi-agent reinforcement learning (MARL) implementations aim to optimize systems with respect to a single objective, despite the fact that many real-world problems are inherently multi-objective in nature…”
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Coordination in Large Multiagent Reinforcement Learning Problems by Kemmerich, T., Buning, H. K.

“… In this work, we deal with these two issues. Therefore, we consider a subclass of stochastic games called cooperative sequential stage games…”
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Synchronous self-learning Pareto strategy by Mozaffari, Ahmad

“… Until now, several deterministic and stochastic methods have been proposed to cope with such complex systems…”
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Synchronous self-learning Pareto strategy: An ensemble framework for vector and multi-criterion optimization by Mozaffari, Ahmad

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Simulation run length planning for stochastic loss models by Srikant, Rayadurgam, Whitt, Ward

“… It is natural to delete an initial portion of the simulation run to allow the system to approach steady state when it starts out empty…”
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