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Adaptive Caching in the YouTube Content Distribution Network: A Revealed Preference Game-Theoretic Learning Approach

William Hoiles, Omid Namvar Gharehshiran, Vikram Krishnamurthy, Ngoc-Dung Dao, Hang Zhang
2015 IEEE Transactions on Cognitive Communications and Networking  
The game is nonstationary as the preferences of users in each region evolve over time.  ...  The utility function tradesoff the placement cost for caching videos locally with the latency cost associated with delivering the video to the users from a neighboring server.  ...  Below, we formally defines the set of efficiently PAC-learnable demand functions [44] .  ... 
doi:10.1109/tccn.2015.2488649 fatcat:sgf2a3nrfnflzcaittvd3upfyy

Boosting Graph Search with Attention Network for Solving the General Orienteering Problem [article]

Zongtao Liu, Jing Xu, Jintao Su, Tao Xiao, Yang Yang
2021 arXiv   pre-print
stage of node selection, thus are hard to be applied in real-world.  ...  However, existing models can only support node coordinates as input, ignore the self-referential property of the studied routing problems, and lack the consideration about the low reliability in the initial  ...  Introduction The orienteering problem(OP) is an important routing problem that originates from the sports game of orienteering.  ... 
arXiv:2109.04730v1 fatcat:l2khvt7izfejflqibxi7uoj3jy

Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time [article]

Iddo Drori, Anant Kharkar, William R. Sickinger, Brandon Kates, Qiang Ma, Suwen Ge, Eden Dolev, Brenda Dietrich, David P. Williamson, Madeleine Udell
2020 arXiv   pre-print
In this work, we develop a new framework to solve any combinatorial optimization problem over graphs that can be formulated as a single player game defined by states, actions, and rewards, including minimum  ...  spanning tree, shortest paths, traveling salesman problem, and vehicle routing problem, without expert knowledge.  ...  The leaf nodes represent rewards or costs such as the sum of weights in MST or length of tour in TSP.  ... 
arXiv:2006.03750v2 fatcat:skeztpkbavhi5jde77rxru2uuy

Adversaries in Online Learning Revisited: with applications in Robust Optimization and Adversarial training [article]

Sebastian Pokutta, Huan Xu
2021 arXiv   pre-print
This meta-game allows for solving a large variety of robust optimization and multi-objective optimization problems and generalizes the approach of arXiv:1402.6361.  ...  While one of the classical setups in online learning deals with the "adversarial" setup, it appears that this concept is used less rigorously, causing confusion in applying results and insights from online  ...  We consider the setup of robust minimal cost routing in G with unrealiable edges: we want to find a route (without revisiting edge) of minimum cost from s to t in G under various scenarios where edges  ... 
arXiv:2101.11443v1 fatcat:f6iiukregnclrnylexkjup4fdq

Guest Editorial Optimization of Electric Vehicle Networks and Heterogeneous Networking in Future Smart Cities

Honghao Gao, Yudong Zhang
2021 IEEE transactions on intelligent transportation systems (Print)  
In an analysis of the route guidance effects, when facing congested road sections, the route guidance strategy of this study can effectively restrain the spread of congestion and achieve the timely amelioration  ...  surveillance in edge computing-enabled IoV for minimizing the response time of the services, achieving the load balance of the edge nodes and realizing privacy protection.  ... 
doi:10.1109/tits.2021.3056180 fatcat:do3jyqr535ha3l3u2meoonqb24

Personalized Route Recommendation with Neural Network Enhanced A* Search Algorithm

Jingyuan Wang, Ning Wu, Xin Zhao
2021 IEEE Transactions on Knowledge and Data Engineering  
The main idea of our solution is to automatically learn the cost functions in A * algorithms, which is the key of heuristic search algorithms. Our model consists of two main components.  ...  In this work, we study an important task in location-based services, namely Personalized Route Recommendation (PRR).  ...  Our solution is inspired by recent progress of deep learning in strategy-based games (e.g., Go and Atari) [17] , which incorporate learnable components in the heuristic search algorithms.  ... 
doi:10.1109/tkde.2021.3068479 fatcat:t7pf5eyi2jbyvmezdr6c5h5l34


Matteo Vasirani, Sascha Ossowski
2011 Applied Artificial Intelligence  
of intersections in order to minimize travel times.  ...  in order to improve their crossing times, and therefore, speed up the traffic flow through the intersection, and ii) how a set of reservation-based intersections can cooperatively act over an entire network  ...  For example, the Team Games Utility, TGU i (x) G(x), is trivially factored, but is poorly learnable.  ... 
doi:10.1080/08839514.2011.551318 fatcat:ezazylltxzg3rjrnjwsyqxrmtq

Spatial Cues in Small Screen Devices: Benefit Or Handicap? [chapter]

Martina Ziefle
2009 Lecture Notes in Computer Science  
features on the route.  ...  It is concluded that navigation aids reduce disorientation in small devices, especially those which support users to build up a spatial representation of the menu.  ...  By this, the number and type of keys used, the functions selected, and the individual navigation routes taken through the menu could be reconstructed in detail.  ... 
doi:10.1007/978-3-642-03655-2_70 fatcat:n3wrkvcplrbnxnvyjgdcmsspy4

Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods [article]

Theocharis Kravaris, Christos Spatharis, Alevizos Bastas, George A. Vouros, Konstantinos Blekas, Gennady Andrienko, Natalia Andrienko, Jose Manuel Cordero Garcia
2019 arXiv   pre-print
In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic  ...  Specifically, we formalize the problem as a multiagent Markov Decision Process (MA-MDP) and we show that it can be considered as a Markov game in which interacting agents need to reach an equilibrium:  ...  from the SESAR Joint Undertaking under grant agreement No 699299 under European Union Horizon 2020 research and innovation programme; It has been partially funded by National Matching Funds 2017-2018 of  ... 
arXiv:1912.06860v1 fatcat:y3zgms5i7jfrjd5isvpun2dn5m

Reinforcement Learning for Combinatorial Optimization: A Survey [article]

Nina Mazyavkina and Sergey Sviridov and Sergei Ivanov and Evgeny Burnaev
2020 arXiv   pre-print
In this survey, we explore the recent advancements of applying RL frameworks to hard combinatorial problems.  ...  Reinforcement learning (RL) proposes a good alternative to automate the search of these heuristics by training an agent in a supervised or self-supervised manner.  ...  Let be a set of elements and ∶ ↦ ℝ be a cost function.  ... 
arXiv:2003.03600v3 fatcat:ofc6gzf2fzhchjawbxfiin3354

A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem

Zhihao Xing, Shikui Tu
2020 IEEE Access  
Instead of making decisions directly based on the output of graph neural networks, we combine the graph neural network with Monte Carlo Tree Search to provide a more reliable policy as the output of the  ...  Without much heuristic designing, our approach outperforms recent state-of-the-art learning-based methods on the TSP.  ...  the objective of the TSP is computed based on the edge cost, i.e., the distance between two vertices.  ... 
doi:10.1109/access.2020.3000236 fatcat:dpholycgrnejff2732kkcxkmha

Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing [article]

Hangyu Mao, Zhibo Gong, Zhen Xiao
2020 arXiv   pre-print
In this paper, we study reward design problem in cooperative MARL based on packet routing environments. Firstly, we show that the above two reward signals are prone to produce suboptimal policies.  ...  Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents.  ...  If we also reward the agent for achieving subgoals such as taking its opponent's pieces, the agent might find a way to achieve them even at the cost of losing the game.  ... 
arXiv:2003.03433v1 fatcat:mefv5bvdfnbzhlnenalmw5t4pq

Subadditive Load Balancing [article]

Kiyohito Nagano, Akihiro Kishimoto
2019 arXiv   pre-print
Set function optimization is essential in AI and machine learning. We focus on a subadditive set function that generalizes submodularity, and examine the subadditivity of non-submodular functions.  ...  In addition, we give a lower bound computation technique for the problem. We apply these methods to the multi-robot routing problem for an empirical performance evaluation.  ...  A.1.1 Subadditivity of minimum spanning tree function In the field of game theory, the subadditivity of the minimum spanning tree function is recognized in relation to the minimum spanning tree game (Bird  ... 
arXiv:1908.09135v1 fatcat:abef45umcreipdodnwaudppbie

Learning and Efficiency in Games with Dynamic Population [article]

Thodoris Lykouris, Vasilis Syrgkanis, Eva Tardos
2020 arXiv   pre-print
We analyze the efficiency of repeated games in dynamically changing environments, motivated by application domains such as Internet ad-auctions and packet routing.  ...  Game theory classically considers Nash equilibria of one-shot games, while in practice many games are played repeatedly, and in such games players often use algorithmic tools to learn to play in the given  ...  Finally, part of the work was conducted when the authors were visiting the Simons Institute.  ... 
arXiv:1505.00391v4 fatcat:5ftj54r7ejcfredldfu4vfih5a

Semi-automatic zooming for mobile map navigation

Sven Kratz, Ivo Brodien, Michael Rohs
2010 Proceedings of the 12th international conference on Human computer interaction with mobile devices and services - MobileHCI '10  
SAZ gives the user the ability to manually control the zoom level of an SDAZ interface, while retaining the automatic zooming characteristics of that interface at times when the user is not explicitly  ...  In this paper we present a novel interface for mobile map navigation based on Semi-Automatic Zooming (SAZ).  ...  The follow route task was designed to evaluate the panning functionality whereas in the landmark finder task participants had to make additional use of the zooming functionality [3] .  ... 
doi:10.1145/1851600.1851615 dblp:conf/mhci/KratzBR10 fatcat:v7pruw6dtrg3ndyup7jaxm56lm
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