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Optimal dynamic information provision

GAMES AND ECONOMIC BEHAVIOR 2017年 104卷 329-349页

作者： Renault, Jerome Solan, Eilon Vieille, Nicolas Univ Toulouse 1 TSE GREMAQ 21 Allee Brienne F-31000 Toulouse France Tel Aviv Univ Sch Math Sci IL-6997800 Tel Aviv Israel HEC Paris Dept Econ & Decis Sci 1 Rue Liberat F-78351 Jouy En Josas France

We study a dynamic model of information provision. A state of nature evolves according to a Markov chain. An advisor with commitment power decides how much information to provide to an uninformed decision maker, so as to influence his short-term decisions. We deal with a stylized class of situations, in which the decision maker has a risky action and a safe action, and the payoff to the advisor only depends on the action chosen by the decision maker. The greedy disclosure policy is the policy which, at each stage, minimizes the amount of information being disclosed in that stage, under the constraint that it maximizes the current payoff of the advisor. We prove that the greedy policy is optimal in many cases - but not always. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Dynamic information provision Optimal strategy greedy algorithm Commitment

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A Reinforcement Learning Approach to the View Planning Problem 30

A Reinforcement Learning Approach to the View Planning Probl...

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30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kaba, Mustafa Devrim Uzunbas, Mustafa Gokhan Lim, Ser Nam Gen Elect Global Res Ctr 1 Res Circle Niskayuna NY 12309 USA

ISBN: (纸本)9781538604571

We present a Reinforcement Learning (RL) solution to the view planning problem (VPP), which generates a sequence of view points that are capable of sensing all accessible area of a given object represented as a 3D model. In doing so, the goal is to minimize the number of view points, making the VPP a class of set covering optimization problem (SCOP). The SCOP is NP-hard, and the inapproximability results tell us that the greedy algorithm provides the best approximation that runs in polynomial time. In order to find a solution that is better than the greedy algorithm, (i) we introduce a novel score function by exploiting the geometry of the 3D model, (ii) we device an intuitive approach to VPP using this score function, and (iii) we cast VPP as a Markovian Decision Process (MDP), and solve the MDP in RL framework using well-known RL algorithms. In particular, we use SARSA, Watkins-Q and TD with function approximation to solve the MDP. We compare the results of our method with the baseline greedy algorithm in an extensive set of test objects, and show that we can out-perform the baseline in almost all cases.

关键词： VP-P protocol greedy algorithm Markov chain learning (artificial intelligence) PHLPP1 gene function approximation optimal approximation three-dimensional displays Polynomial time Tell View Nondeterministic polynomial-time hard

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Interference-Free LED Allocation for the Fisheye Lens based Visible Light Communications

Interference-Free LED Allocation for the Fisheye Lens based ...

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IEEE International Conference on Communications (ICC)

作者： Jang, Rui Wang, Zhaocheng Zhu, Xudong Wang, Qi Tsinghua Univ TNList Dept Elect Engn Beijing 100084 Peoples R China Univ Southampton Elect & Comp Sci Southampton SO17 1BJ Hants England

ISBN: (纸本)9781467389990

Due to the limited modulation bandwidth of light emitting diodes (LEDs), the imaging optical multiple-input multiple-output (MIMO) technology is applied in visible light communications (VLC) for high data rate transmission. As an imaging lens with a wide angle/field-of-view (FOV) is favorable in the imaging optical MIMO VLC system, a fisheye lens can be utilized to concentrate the light. To avoid inter-user interference among indoor users and satisfy their target bit error rate (BER) requirements with the minimum number of LEDs simultaneously, an interference-free LED allocation scheme for the fisheye lens based imaging optical MIMO VLC system is investigated in this paper. Since the formulated problem is NP-hard, a location-based greedy algorithm is proposed, where the LEDs are allocated to the users sequentially based on their distances to the center of the LED array. Simulation results verify that there exists no interference among all users while the target BER requirements for all users are satisfied with our proposed algorithm.

关键词： Visible light communication optical MIMO fisheye lens inter-user interference greedy algorithm

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CQM: Coverage-constrained Quality Maximization in Crowdsourcing Test 17

CQM: Coverage-constrained Quality Maximization in Crowdsourc...

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IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)

作者： Xie, Miao Wang, Qing Cui, Qiang Yang, Guowei Li, Mingshu CSI Euler Dept Huawei Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Chinese Acad Sci Inst Software Natl Engn Res Ctr Fundamental Software Beijing Peoples R China Texas State Univ Dept Comp Sci San Marcos TX USA

ISBN: (纸本)9781538615898

Mobile app testing is challenging since each test needs to be executed in a variety of operating contexts including heterogeneous devices, various wireless networks, and different locations. Crowdsourcing enables a mobile app testing to be distributed as a crowdsourced task to leverage the crowd in a community. However, a high test quality and expected test context coverage are difficult to achieve in crowdsourcing test. Upon distributing a test task, mobile app providers can neither know who will participate and submit a high-qualified test report nor predict whether all expected test contexts can be covered during the test. To address this problem, we put forward a novel research problem called Coverage-constrained Quality Maximization (CQM) for crowdsourcing test. Given a mobile app test task, our objective is to discover and recommend a set of potential workers from available crowd workers such that they can accomplish the task achieving expected test context coverage and the possible highest test quality. We prove that the CQM problem is NP-Complete and then introduce two efficient greedy algorithms. Based on a real dataset of the largest Chinese crowdsourcing test platform, our evaluation shows that the proposed algorithms are effective and efficient, and can be potentially used as online services in practice.

关键词： Quality Maximization Crowdsourcing Crowdsource Mobile Applications Information Services Nondeterministic polynomial-time hard greedy algorithm experimental duties

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Social-Aware Collaborative Caching for D2D Content Sharing 17

Social-Aware Collaborative Caching for D2D Content Sharing

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IEEE 17th International Conference on Communication Technology (ICCT)

作者： Zhang, Can Wu, Dan Ao, Liang Cai, Yueming PLA Army Engn Univ Nanjing 210007 Jiangsu Peoples R China

ISBN: (纸本)9781509039449

With the rapid growth of wireless content demands, Device-to-Device (D2D) content sharing technology is proposed to effectively alleviate the pressure of base stations and improve the quality of service of users. However, due to the limited storage capacity of devices, the various content demands are difficult to be satisfied. Hence, caching schemes are needed. In particular, the collaborative caching, which can increase the utilization ratio of the storage capacity, attracts much attention. Moreover, we introduce the social popularity to improve the availability of preset contents, and then, we propose the social-related download rate by combining the physical and social information. Guided by this, we model the social-aware collaborative caching problem in a D2D content sharing scenario by maximizing the sum of social-related download rate over the constraint of limited storage capacity. Due to its intractability, it is computationally reduced to the maximization of a monotone submodular function, subject to a partition matroid constraint. Subsequently, the social-aware collaborative caching algorithm based on greedy algorithm is designed to achieve a suboptimal solution within a factor 1/2 approximation guarantee and polynomial-complexity.

关键词： D2D content sharing collaborative caching social-aware submodular function greedy algorithm

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Resource Management for Maximum Spectrum Utilization in D2D Cellular Communication

Resource Management for Maximum Spectrum Utilization in D2D ...

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Conference on Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： Sona, Deepika Rani Bagadi, Kala Praveen VIT Univ Sch Elect Engn Vellore 14 Tamil Nadu India

ISBN: (纸本)9781509056828

Given the steadily growing importance of D2D communication, a resource allocation scheme aimed at maximizing the spectrum utilization by minimizing transmission length of D2D link is proposed. The proposed scheme illustrates the existence of RBs in time and frequency domain and performs power control within set Cellular to Device (C2D) and Device to Cellular (D2C) Constraints. Mixed integer programming (MIP) which is an NP complete problem is suggested to formulate interference issues. Proposed column generation method is the solution for NP problem which is achieved by reducing it to a pricing problem that focus to solve restricted master problem before proceeding for the next step. The pricing issue is iteratively solved by a combination of a distance based and greedy heuristic algorithm. The numerical results represented optimum minimum distance between suggested cellular user and device to device link user.

关键词： Device-to-Device Communication C2D and D2C interference greedy algorithm Distance-based algorithm

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Prolonging Lifetime in Wireless Sensor Networks with Interference Constraints 1

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12th International Conference on Green, Pervasive, and Cloud Computing (GPC)

作者： Carrabs, Francesco Cerulli, Raffaele D'Ambrosio, Ciriaco Raiconi, Andrea Univ Salerno Dept Math Via Giovanni Paolo 2 132 I-84084 Fisciano SA Italy

ISBN: (数字)9783319571867

ISBN: (纸本)9783319571867;9783319571850

In this work, we consider a scenario in which we have to monitor some locations of interest in a geographical area by means of a wireless sensor network. Our aim is to keep the network operational for as long as possible, while preventing certain sensors from being active simultaneously, since they would interfere with one another causing data loss, need for retransmissions and overall affecting the throughput and efficiency of the network. We propose an exact approach based on column generation, as well as a heuristic algorithm to solve its separation problem. Computational tests prove our approach to be effective, and that the introduction of our heuristic in the Column Generation framework allows significant gains in terms of required computational effort.

关键词： Wireless sensor network Column generation Maximum lifetime Interference constraints greedy algorithm

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Online and Random-order Load Balancing Simultaneously 17

Online and Random-order Load Balancing Simultaneously

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28th Annual ACM-SIAM Symposium on Discrete algorithms (SODA)

作者： Molinaro, Marco Pontificia Univ Catolica Rio de Janeiro Comp Sci Dept Rio de Janeiro Brazil

ISBN: (纸本)9781510836358

We consider the problem of online load balancing under l(p)-norms: sequential jobs need to be assigned to one of the machines and the goal is to minimize the l(p)-norm of the machine loads. This generalizes the classical problem of scheduling for makespan minimization (case l(infinity)) and has been thoroughly studied. However, despite the recent push for beyond worst-case analyses, no such results are known for this problem. In this paper we provide algorithms with simultaneous guarantees for the worst-case model as well as for the random-order(i.e. secretary) model, where an arbitrary set of jobs comes in random order. First, we show that the greedy algorithm (with restart), known to have optimal O(p) worst-case guarantee, also has a (typically) improved random-order guarantee. However, the behavior of this algorithm in the random-order model degrades with p. We then propose algorithm SIMULTANEOUSLB that has simultaneously optimal guarantees (within constants) in both worst-case and random order models. In particular, the random-order guarantee of SIMULTANEOUSLB improves as p increases. One of the main components is a new algorithm with improved regret for Online Linear Optimization (OLO) over the non-negative vectors in the l(p) ball. Interestingly, this OLO algorithm is also used to prove a purely probabilistic inequality that controls the correlations arising in the random-order model, a common source of difficulty for the analysis. Another important component used in both SIMULTANEOUSLB and our OLO algorithm is a smoothing of the l(p)-norm that may be of independent interest. This smoothness property allows us to see algorithm SIMULTANEOUSLB as essentially a greedy one in the worst-case model and as a primal-dual one in the random-order model, which is instrumental for its simultaneous guarantees.

关键词： algorithms Load balancing greedy algorithm machine jobs Restarting Reduced order model low-order model smoothing

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Mean Squared Error vs. Frame Potential for Unsupervised Variable Selection

Mean Squared Error vs. Frame Potential for Unsupervised Vari...

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International Conference on Life System Modeling and Simulation (LSMS) / International Conference on Intelligent Computing for Sustainable Energy and Environment (ICSEE)

作者： Zocco, Federico McLoone, Sean Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland

ISBN: (纸本)9789811063732;9789811063725

Forward Selection Component Analysis (FSCA) provides a pragmatic solution to the NP-hard unsupervised variable selection problem, but is not guaranteed to be optimal due to the multi-modal nature of the mean squared error (MSE) selection metric used. Frame potential (FP) is a metric that has recently been shown to yield near-optimal greedy sensor selection performance for linear inverse problems. This paper explores if FP offers similar benefits in the unsupervised variable selection context. In addition, the backward elimination counterpart of FSCA is introduced for the first time (BECA) and compared with forward and backward FP based variable selection on a number of simulated and real world datasets. It is concluded that FP does not improve on FSCA and that while BECA yields comparable results to FSCA it is not a competitive alternative due to its much higher computational complexity.

关键词： Unsupervised dimensionality reduction Variable selection Frame potential greedy algorithm

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Efficient Selection of Influential Nodes for Viral Marketing in Social Networks

Efficient Selection of Influential Nodes for Viral Marketing...

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IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)

作者： Menta, Venkata Pushpak Teja Singh, Parikshit Kishor Aurigo Software Technol Bengaluru India Indian Inst Informat Technol Kota Dept Elect & Commun Engn MNIT Campus Jaipur Rajasthan India

ISBN: (纸本)9781509049974

There has been an exponential growth in social networking and online shopping with the internet revolution in the recent past. Viral marketing, exploiting social networks to promote various products, has proved successful in influencing the public compared to other media. One well known task in this area is to choose the best nodes that maximize the overall influence propagated in the social network. t-Influence Maximization problem has been addressed in this paper which can be defined as maximizing the overall influence in a social network by selecting seeds for the given t products with their seed requirements. Constraints are set on number of products to be recommended for any single user (to avoid spamming) and total number of seeds to be selected for a particular product (budget constraint). The technique we have used, a greedy algorithm to the aforementioned t-influence maximization problem. It not only allocates seeds with maximum total influence, but also ensures that any one product doesn't dictate the overall influence and a fair selection is done. An efficient algorithm is designed for calculating the approximate influence of the selected nodes which is important in practical situations. Effectiveness and scalability of the algorithm is analyzed and verified using simulations on real-life facebook data.

关键词： viral marketing influence maximization social networks independent cascade model greedy algorithm Oracle algorithm spam limit prediction

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