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  • Coordinate-wise Median: Not Bad, Not Bad, Pretty Good
    arXiv.cs.GT Pub Date : 2020-07-02
    Sumit Goel; Wade Hann-Caruthers

    We consider the facility location problem in two dimensions. In particular, we consider a setting where agents have Euclidean preferences, defined by their ideal points, for a facility to be located in $\mathbb{R}^2$. For the utilitarian objective and an odd number of agents, we show that the coordinate-wise median mechanism (CM) has a worst-case approximation ratio (WAR) of $\sqrt{2}\frac{\sqrt{n^2+1}}{n+1}$

    更新日期:2020-07-03
  • Dynamic Bidding Strategies with Multivariate Feedback Control for Multiple Goals in Display Advertising
    arXiv.cs.GT Pub Date : 2020-06-01
    Michael Tashman; Jiayi Xie; John Hoffman; Lee Winikor; Rouzbeh Gerami

    Real-Time Bidding (RTB) display advertising is a method for purchasing display advertising inventory in auctions that occur within milliseconds. The performance of RTB campaigns is generally measured with a series of Key Performance Indicators (KPIs) - measurements used to ensure that the campaign is cost-effective and that it is purchasing valuable inventory. While an RTB campaign should ideally meet

    更新日期:2020-07-02
  • Robust communication on networks
    arXiv.cs.GT Pub Date : 2020-07-01
    Marie Laclau; Ludovic Renou; Xavier Venel

    We consider sender-receiver games, where the sender and the receiver are two distinct nodes in a communication network. Communication between the sender and the receiver is thus indirect. We ask when it is possible to robustly implement the equilibrium outcomes of the direct communication game as equilibrium outcomes of indirect communication games on the network. Robust implementation requires that:

    更新日期:2020-07-02
  • A Multi-Agent Reinforcement Learning Approach for Dynamic Information Flow Tracking Games for Advanced Persistent Threats
    arXiv.cs.GT Pub Date : 2020-06-30
    Dinuka Sahabandu; Shana Moothedath; Joey Allen; Linda Bushnell; Wenke Lee; Radha Poovendran

    Advanced Persistent Threats (APTs) are stealthy attacks that threaten the security and privacy of sensitive information. Interactions of APTs with victim system introduce information flows that are recorded in the system logs. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism for detecting APTs. DIFT taints information flows originating at system entities that are susceptible

    更新日期:2020-07-02
  • Small Nash Equilibrium Certificates in Very Large Games
    arXiv.cs.GT Pub Date : 2020-06-29
    Brian Hu Zhang; Tuomas Sandholm

    In many game settings, the game is not explicitly given but is only accessible by playing it. While there have been impressive demonstrations in such settings, prior techniques have not offered safety guarantees, that is, guarantees on the game-theoretic exploitability of the computed strategies. In this paper we introduce an approach that shows that it is possible to provide exploitability guarantees

    更新日期:2020-07-01
  • The Equilibrium Existence Duality: Equilibrium with Indivisibilities & Income Effects
    arXiv.cs.GT Pub Date : 2020-06-30
    Elizabeth Baldwin; Omer Edhan; Ravi Jagadeesan; Paul Klemperer; Alexander Teytelboym

    We show that, with indivisible goods, the existence of competitive equilibrium fundamentally depends on agents' substitution effects, not their income effects. Our Equilibrium Existence Duality allows us to transport results on the existence of competitive equilibrium from settings with transferable utility to settings with income effects. One consequence is that net substitutability---which is a strictly

    更新日期:2020-07-01
  • R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
    arXiv.cs.GT Pub Date : 2020-06-30
    Zhongxiang Dai; Yizhou Chen; Kian Hsiang Low; Patrick Jaillet; Teck-Hua Ho

    This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested agents with unknown, complex, and costly-to-evaluate payoff functions in repeated games, which we call Recursive Reasoning-Based BO (R2-B2). Our R2-B2 algorithm is general in that it does not constrain the relationship among

    更新日期:2020-07-01
  • Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
    arXiv.cs.GT Pub Date : 2020-06-29
    Steven Jecmen; Hanrui Zhang; Ryan Liu; Nihar B. Shah; Vincent Conitzer; Fei Fang

    We consider three important challenges in conference peer review: (i) reviewers maliciously attempting to get assigned to certain papers to provide positive reviews, possibly as part of quid-pro-quo arrangements with the authors; (ii) "torpedo reviewing," where reviewers deliberately attempt to get assigned to certain papers that they dislike in order to reject them; (iii) reviewer de-anonymization

    更新日期:2020-07-01
  • On Bellman's Optimality Principle for zs-POSGs
    arXiv.cs.GT Pub Date : 2020-06-29
    Olivier Buffet; Jilles Dibangoye; Aurélien Delage; Abdallah Saffidine; Vincent Thomas

    Many non-trivial sequential decision-making problems are efficiently solved by relying on Bellman's optimality principle, i.e., exploiting the fact that sub-problems are nested recursively within the original problem. Here we show how it can apply to (infinite horizon) 2-player zero-sum partially observable stochastic games (zs-POSGs) by (i) taking a central planner's viewpoint, which can only reason

    更新日期:2020-07-01
  • Most Competitive Mechanisms in Online Fair Division
    arXiv.cs.GT Pub Date : 2020-06-29
    Martin Aleksandrov; Toby Walsh

    This paper combines two key ingredients for online algorithms - competitive analysis (e.g. the competitive ratio) and advice complexity (e.g. the number of advice bits needed to improve online decisions) - in the context of a simple online fair division model where items arrive one by one and are allocated to agents via some mechanism. We consider four such online mechanisms: the popular Ranking matching

    更新日期:2020-06-30
  • Expected Outcomes and Manipulations in Online Fair Division
    arXiv.cs.GT Pub Date : 2020-06-29
    Martin Aleksandrov; Toby Walsh

    Two simple and attractive mechanisms for the fair division of indivisible goods in an online setting are LIKE and BALANCED LIKE. We study some fundamental computational problems concerning the outcomes of these mechanisms. In particular, we consider what expected outcomes are possible, what outcomes are necessary, and how to compute their exact outcomes. In general, we show that such questions are

    更新日期:2020-06-30
  • Strategy-proofness, Envy-freeness and Pareto efficiency in Online Fair Division with Additive Utilities
    arXiv.cs.GT Pub Date : 2020-06-29
    Martin Aleksandrov; Toby Walsh

    We consider fair division problems where indivisible items arrive one-by-one in an online fashion and are allocated immediately to agents who have additive utilities over these items. Many existing offline mechanisms do not work in this online setting. In addition, many existing axiomatic results often do not transfer from the offline to the online setting. For this reason, we propose here three new

    更新日期:2020-06-30
  • Group Envy Freeness and Group Pareto Efficiency in Fair Division with Indivisible Items
    arXiv.cs.GT Pub Date : 2020-06-29
    Martin Aleksandrov; Toby Walsh

    We study the fair division of items to agents supposing that agents can form groups. We thus give natural generalizations of popular concepts such as envy-freeness and Pareto efficiency to groups of fixed sizes. Group envy-freeness requires that no group envies another group. Group Pareto efficiency requires that no group can be made better off without another group be made worse off. We study these

    更新日期:2020-06-30
  • Monotone and Online Fair Division
    arXiv.cs.GT Pub Date : 2020-06-29
    Martin Aleksandrov; Toby Walsh

    We study a new but simple model for online fair division in which indivisible items arrive one-by-one and agents have monotone utilities over bundles of the items. We consider axiomatic properties of mechanisms for this model such as strategy-proofness, envy-freeness, and Pareto efficiency. We prove a number of impossibility results that justify why we consider relaxations of the properties, as well

    更新日期:2020-06-30
  • The Hylland-Zeckhauser Rule Under Bi-Valued Utilities
    arXiv.cs.GT Pub Date : 2020-06-28
    Haris Aziz

    The Hylland-Zeckhauser (HZ) rule is a well-known rule for probabilistic assignment of items. The complexity of the rule has received renewed interest recently with Vazirani and Yannakakis (2020) proposing a strongly polynomial-time algorithm for the rule under bi-valued utilities, and making several general insights. We study the rule under the case of agents having bi-valued utilities. We point out

    更新日期:2020-06-30
  • Dominate or Delete: Decentralized Competing Bandits with Uniform Valuation
    arXiv.cs.GT Pub Date : 2020-06-26
    Abishek Sankararaman; Soumya Basu; Karthik Abinav Sankararaman

    We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms) with unknown and heterogeneous valuations. Such markets abstract online matching platforms (for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et

    更新日期:2020-06-30
  • Flow-Based Network Creation Games
    arXiv.cs.GT Pub Date : 2020-06-26
    Hagen Echzell; Tobias Friedrich; Pascal Lenzner; Anna Melnichenko

    Network Creation Games(NCGs) model the creation of decentralized communication networks like the Internet. In such games strategic agents corresponding to network nodes selfishly decide with whom to connect to optimize some objective function. Past research intensively analyzed models where the agents strive for a central position in the network. This models agents optimizing the network for low-latency

    更新日期:2020-06-29
  • Which Random Matching Markets Exhibit a Stark Effect of Competition?
    arXiv.cs.GT Pub Date : 2020-06-25
    Yash Kanoria; Seungki Min; Pengyu Qian

    We revisit the popular random matching market model introduced by Knuth (1976) and Pittel (1989), and shown by Ashlagi, Kanoria and Leshno (2013) to exhibit a "stark effect of competition", i.e., with any difference in the number of agents on the two sides, the short side agents obtain substantially better outcomes. We generalize the model to allow "partially connected" markets with each agent having

    更新日期:2020-06-29
  • Kuhn's Equivalence Theorem for Games in Intrinsic Form
    arXiv.cs.GT Pub Date : 2020-06-26
    Benjamin HeymannCERMICS; Michel de LaraCERMICS; Jean-Philippe ChancelierCERMICS

    We state and prove Kuhn's equivalence theorem for a new representation of games, the intrinsic form. First, we introduce games in intrinsic form where information is represented by $\sigma$-fields over a product set. For this purpose, we adapt to games the intrinsic representation that Witsenhausen introduced in control theory. Those intrinsic games do not require an explicit description of the play

    更新日期:2020-06-29
  • Mobility operator resource-pooling contract design to hedge against network disruptions
    arXiv.cs.GT Pub Date : 2020-06-25
    Theodoros P. Pantelidis; Joseph Y. J. Chow; Oded Cats

    Public transportation delays due to systematic failures have a major impact on network users. We propose designing capacity pooling contracts to facilitate horizontal cooperation among operators to mitigate those costs and improve service resilience. When two or more public transport providers agree upon sharing resources, the total transportation costs can be reduced due to added flexibility in the

    更新日期:2020-06-26
  • Teamwise Mean Field Competitions
    arXiv.cs.GT Pub Date : 2020-06-24
    Xiang Yu; Yuchong Zhang; Zhou Zhou

    This paper studies competitions with rank-based reward among a large number of teams. Within each sizable team, we consider a mean-field contribution game in which each team member contributes to the jump intensity of a common Poisson project process; across all teams, a mean field competition game is formulated on the rank of the completion time, namely the jump time of Poisson project process, and

    更新日期:2020-06-26
  • Optimizing Affine Maximizer Auctions via Linear Programming: an Application to Revenue Maximizing Mechanism Design for Zero-Day Exploits Markets
    arXiv.cs.GT Pub Date : 2020-06-25
    Mingyu Guo; Hideaki Hata; Ali Babar

    Optimizing within the affine maximizer auctions (AMA) is an effective approach for revenue maximizing mechanism design. The AMA mechanisms are strategy-proof and individually rational (if the agents' valuations for the outcomes are nonnegative). Every AMA mechanism is characterized by a list of parameters. By focusing on the AMA mechanisms, we turn mechanism design into a value optimization problem

    更新日期:2020-06-26
  • Revenue Maximizing Markets for Zero-Day Exploits
    arXiv.cs.GT Pub Date : 2020-06-25
    Mingyu Guo; Hideaki Hata; Ali Babar

    Markets for zero-day exploits (software vulnerabilities unknown to the vendor) have a long history and a growing popularity. We study these markets from a revenue-maximizing mechanism design perspective. We first propose a theoretical model for zero-day exploits markets. In our model, one exploit is being sold to multiple buyers. There are two kinds of buyers, which we call the defenders and the offenders

    更新日期:2020-06-26
  • Cost Sharing Security Information with Minimal Release Delay
    arXiv.cs.GT Pub Date : 2020-06-25
    Mingyu Guo; Yong Yang; Muhammad Ali Babar

    We study a cost sharing problem derived from bug bounty programs, where agents gain utility by the amount of time they get to enjoy the cost shared information. Once the information is provided to an agent, it cannot be retracted. The goal, instead of maximizing revenue, is to pick a time as early as possible, so that enough agents are willing to cost share the information and enjoy it for a premium

    更新日期:2020-06-26
  • Snitches Get Stitches: On The Difficulty of Whistleblowing
    arXiv.cs.GT Pub Date : 2020-06-25
    Mansoor Ahmed-Rengers; Ross Anderson; Darija Halatova; Ilia Shumailov

    One of the most critical security protocol problems for humans is when you are betraying a trust, perhaps for some higher purpose, and the world can turn against you if you're caught. In this short paper, we report on efforts to enable whistleblowers to leak sensitive documents to journalists more safely. Following a survey of cases where whistleblowers were discovered due to operational or technological

    更新日期:2020-06-26
  • Optimizing Voting Order on Sequential Juries: A Median Voter Theorem
    arXiv.cs.GT Pub Date : 2020-06-24
    Steve Alpern; Bo Chen

    We consider an odd-sized "jury", which votes sequentially between two states of Nature (say A and B, or Innocent and Guilty) with the majority opinion determining the verdict. Jurors have private information in the form of a signal in [-1,+1], with higher signals indicating A more likely. Each juror has an ability in [0,1], which is proportional to the probability of A given a positive signal, an analog

    更新日期:2020-06-26
  • DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency
    arXiv.cs.GT Pub Date : 2020-06-11
    Lewis Gudgeon; Sam M. Werner; Daniel Perez; William J. Knottenbelt

    We coin the term *Protocols for Loanable Funds (PLFs)* to refer to protocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the main applications within Decentralized Finance (DeFi), and use smart contract code to facilitate the intermediation of loanable funds. In doing so, these protocols allow agents to borrow and save programmatically. Within these

    更新日期:2020-06-25
  • A Parameterized Family of Meta-Submodular Functions
    arXiv.cs.GT Pub Date : 2020-06-23
    Mehrdad Ghadiri; Richard Santiago; Bruce Shepherd

    Submodular function maximization has found a wealth of new applications in machine learning models during the past years. The related supermodular maximization models (submodular minimization) also offer an abundance of applications, but they appeared to be highly intractable even under simple cardinality constraints. Hence, while there are well-developed tools for maximizing a submodular function

    更新日期:2020-06-25
  • Lower Bounds on Rate of Convergence of Matrix Products in All Pairs Shortest Path of Social Network
    arXiv.cs.GT Pub Date : 2020-06-24
    Dezhou Shen

    With the rapid development of social network applications, social network has become an important medium for people to interact. For the minimum distance computation of all pairs in networks, Alon N[4] proposed an algorithm with matrix multiplication, combining with distance product association law and block matrix multiplication, all pairs shortest path length algorithm on networks has time bound

    更新日期:2020-06-25
  • Dynamic Information Flow Tracking for Detection of Advanced Persistent Threats: A Stochastic Game Approach
    arXiv.cs.GT Pub Date : 2020-06-22
    Shana Moothedath; Dinuka Sahabandu; Joey Allen; Andrew Clark; Linda Bushnell; Wenke Lee; Radha Poovendran

    Advanced Persistent Threats (APTs) are stealthy customized attacks by intelligent adversaries. This paper deals with the detection of APTs that infiltrate cyber systems and compromise specifically targeted data and/or infrastructures. Dynamic information flow tracking is an information trace-based detection mechanism against APTs that taints suspicious information flows in the system and generates

    更新日期:2020-06-23
  • A survey of queueing systems with strategic timing of arrivals
    arXiv.cs.GT Pub Date : 2020-06-22
    Moshe Haviv; Liron Ravner

    Consider a population of customers each of which needs to decide independently when to arrive to a facility that provides a service during a fixed period of time, say a day. This is a common scenario in many service systems such as a bank, lunch at a cafeteria, music concert, flight check-in and many others. High demand for service at a specific time leads to congestion that comes at a cost, e.g.,

    更新日期:2020-06-23
  • Envy-freeness up to one item: Shall we add or remove resources?
    arXiv.cs.GT Pub Date : 2020-06-19
    Martin Aleksandrov

    We consider a fair division model in which agents have general valuations for bundles of indivisible items. We propose two new axiomatic properties for allocations in this model: EF1+- and EFX+-. We compare these with the existing EF1 and EFX. Although EF1 and EF1+- allocations often exist, our results assert eloquently that EFX+- and PO allocations exist in each case where EFX and PO allocations do

    更新日期:2020-06-23
  • Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers
    arXiv.cs.GT Pub Date : 2020-06-22
    Lily Xu; Andrew Perrault; Andrew Plumptre; Margaret Driciru; Fred Wanyama; Aggrey Rwetsiba; Milind Tambe

    Applications of artificial intelligence for wildlife protection have focused on learning models of poacher behavior based on historical patterns. However, poachers' behaviors are described not only by their historical preferences, but also their reaction to ranger patrols. Past work applying machine learning and game theory to combat poaching have hypothesized that ranger patrols deter poachers, but

    更新日期:2020-06-23
  • Adaptive Discretization for Adversarial Bandits with Continuous Action Spaces
    arXiv.cs.GT Pub Date : 2020-06-22
    Chara Podimata; Aleksandrs Slivkins

    Lipschitz bandits is a prominent version of multi-armed bandits that studies large, structured action spaces such as the [0,1] interval, where similar actions are guaranteed to have similar rewards. A central theme here is the adaptive discretization of the action space, which gradually "zooms in" on the more promising regions thereof. The goal is to take advantage of "nicer" problem instances, while

    更新日期:2020-06-23
  • A Second-order Equilibrium in Nonconvex-Nonconcave Min-max Optimization: Existence and Algorithm
    arXiv.cs.GT Pub Date : 2020-06-22
    Oren Mangoubi; Nisheeth K. Vishnoi

    Min-max optimization, with a nonconvex-nonconcave objective function $f: \mathbb{R}^d \times \mathbb{R}^d \rightarrow \mathbb{R}$, arises in many areas, including optimization, economics, and deep learning. The nonconvexity-nonconcavity of $f$ means that the problem of finding a global $\varepsilon$-min-max point cannot be solved in $\mathrm{poly}(d, \frac{1}{\varepsilon})$ evaluations of $f$. Thus

    更新日期:2020-06-23
  • MAD-HTLC: Because HTLC is Crazy-Cheap to Attack
    arXiv.cs.GT Pub Date : 2020-06-22
    Itay Tsabary; Matan Yechieli; Ittay Eyal

    Smart Contracts and transactions allow users to implement elaborate constructions on cryptocurrency blockchains like Bitcoin, Ethereum, and Libra. Many of these, including operational payment channels, use a building block called Hashed Time-Locked Contract (HTLC). In this work, we distill from HTLC a specification (HTLCSpec), and present an implementation called Mutual-Assured-Destruction Hashed Time-Locked

    更新日期:2020-06-23
  • Learning Trembling Hand Perfect Mean Field Equilibrium for Dynamic Mean Field Games
    arXiv.cs.GT Pub Date : 2020-06-21
    Kiyeob Lee; Desik Rengarajan; Dileep Kalathil; Srinivas Shakkottai

    Mean Field Games (MFG) are those in which each agent assumes that the states of all others are drawn in an i.i.d. manner from a common belief distribution, and optimizes accordingly. The equilibrium concept here is a Mean Field Equilibrium (MFE), and algorithms for learning MFE in dynamic MFGs are unknown in general due to the non-stationary evolution of the belief distribution. Our focus is on an

    更新日期:2020-06-23
  • A Stackelberg Security Investment Game for Voltage Stability of Power Systems
    arXiv.cs.GT Pub Date : 2020-06-20
    Lu An; Aranya Chakrabortty; Alexandra Duel-Hallen

    We formulate a Stackelberg game between an attacker and a defender of a power system. The attacker attempts to alter the load setpoints of the power system covertly and intelligently, so that the voltage stability margin of the grid is reduced, driving the entire system towards a voltage collapse. The defender, or the system operator, aims to compensate for this reduction by retuning the reactive power

    更新日期:2020-06-23
  • Optimal Statistical Hypothesis Testing for Social Choice
    arXiv.cs.GT Pub Date : 2020-06-19
    Lirong Xia

    We address the following question in this paper: "What are the most robust statistical methods for social choice?'' By leveraging the theory of uniformly least favorable distributions in the Neyman-Pearson framework to finite models and randomized tests, we characterize uniformly most powerful (UMP) tests, which is a well-accepted statistical optimality w.r.t. robustness, for testing whether a given

    更新日期:2020-06-23
  • Gradient-free Online Learning in Games with Delayed Rewards
    arXiv.cs.GT Pub Date : 2020-06-19
    Amélie Héliou; Panayotis Mertikopoulos; Zhengyuan Zhou

    Motivated by applications to online advertising and recommender systems, we consider a game-theoretic model with delayed rewards and asynchronous, payoff-based feedback. In contrast to previous work on delayed multi-armed bandits, we focus on multi-player games with continuous action spaces, and we examine the long-run behavior of strategic agents that follow a no-regret learning policy (but are otherwise

    更新日期:2020-06-22
  • Representing Pure Nash Equilibria in Argumentation
    arXiv.cs.GT Pub Date : 2020-06-19
    Bruno Yun; Srdjan Vesic; Nir Oren

    In this paper we describe an argumentation-based representation of normal form games, and demonstrate how argumentation can be used to compute pure strategy Nash equilibria. Our approach builds on Modgil's Extended Argumentation Frameworks. We demonstrate its correctness, prove several theoretical properties it satisfies, and outline how it can be used to explain why certain strategies are Nash equilibria

    更新日期:2020-06-22
  • Opinion Maximization in Social Trust Networks
    arXiv.cs.GT Pub Date : 2020-06-19
    Pinghua Xu; Wenbin Hu; Jia Wu; Weiwei Liu

    Social media sites are now becoming very important platforms for product promotion or marketing campaigns. Therefore, there is broad interest in determining ways to guide a site to react more positively to a product with a limited budget. However, the practical significance of the existing studies on this subject is limited for two reasons. First, most studies have investigated the issue in oversimplified

    更新日期:2020-06-22
  • Neutralizing Self-Selection Bias in Sampling for Sortition
    arXiv.cs.GT Pub Date : 2020-06-18
    Bailey Flanigan; Paul Gölz; Anupam Gupta; Ariel Procaccia

    Sortition is a political system in which decisions are made by panels of randomly selected citizens. The process for selecting a sortition panel is traditionally thought of as uniform sampling without replacement, which has strong fairness properties. In practice, however, sampling without replacement is not possible since only a fraction of agents is willing to participate in a panel when invited

    更新日期:2020-06-19
  • On Subgame Perfect Equilibria in Turn-Based Reachability Timed Games
    arXiv.cs.GT Pub Date : 2020-06-18
    Thomas Brihaye; Aline Goeminne

    We study multiplayer turn-based timed games with reachability objectives. In particular, we are interested in the notion of subgame perfect equilibrium (SPE). We prove that deciding the constrained existence of an SPE in this setting is EXPTIME-complete.

    更新日期:2020-06-19
  • Information Extraction from a Strategic Sender: The Zero Error Case
    arXiv.cs.GT Pub Date : 2020-06-18
    Anuj S. Vora; Ankur A. Kulkarni

    We introduce a setting where a receiver aims to perfectly recover a source known privately to a strategic sender over a possibly noisy channel. The sender is endowed with a utility function and sends signals to the receiver with the aim of maximizing this utility. Due to the strategic nature of the sender not all the transmitted information is truthful, which leads to question: how much true information

    更新日期:2020-06-19
  • Competitive Policy Optimization
    arXiv.cs.GT Pub Date : 2020-06-18
    Manish Prajapat; Kamyar Azizzadenesheli; Alexander Liniger; Yisong Yue; Anima Anandkumar

    A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties. To tackle this, we propose competitive policy optimization (CoPO), a novel policy gradient approach that exploits the game-theoretic nature of competitive games to derive policy updates. Motivated by the competitive gradient

    更新日期:2020-06-19
  • DREAM: Deep Regret minimization with Advantage baselines and Model-free learning
    arXiv.cs.GT Pub Date : 2020-06-18
    Eric Steinberger; Adam Lerer; Noam Brown

    We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies in imperfect-information games with multiple agents. Formally, DREAM converges to a Nash Equilibrium in two-player zero-sum games and to an extensive-form coarse correlated equilibrium in all other games. Our primary innovation is an effective algorithm that, in contrast to other regret-based deep learning algorithms

    更新日期:2020-06-19
  • Competitive Mirror Descent
    arXiv.cs.GT Pub Date : 2020-06-17
    Florian Schäfer; Anima Anandkumar; Houman Owhadi

    Constrained competitive optimization involves multiple agents trying to minimize conflicting objectives, subject to constraints. This is a highly expressive modeling language that subsumes most of modern machine learning. In this work we propose competitive mirror descent (CMD): a general method for solving such problems based on first order information that can be obtained by automatic differentiation

    更新日期:2020-06-19
  • Mechanism Design for Perturbation Stable Combinatorial Auctions
    arXiv.cs.GT Pub Date : 2020-06-17
    Giannis Fikioris; Dimitris Fotakis

    Motivated by recent research on combinatorial markets with endowed valuations by (Babaioff et al., EC 2018) and (Ezra et al., EC 2020), we introduce a notion of perturbation stability in Combinatorial Auctions (CAs) and study the extend to which stability helps in social welfare maximization and mechanism design. A CA is $\gamma\textit{-stable}$ if the optimal solution is resilient to inflation, by

    更新日期:2020-06-18
  • Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response
    arXiv.cs.GT Pub Date : 2020-06-17
    Rui Yan; Xiaoming Duan; Zongying Shi; Yisheng Zhong; Jason R. Marden; Francesco Bullo

    This paper introduces two metrics (cycle-based and memory-based metrics), grounded on a dynamical game-theoretic solution concept called \emph{sink equilibrium}, for the evaluation, ranking, and computation of policies in multi-agent learning. We adopt strict best response dynamics (SBRD) to model selfish behaviors at a meta-level for multi-agent reinforcement learning. Our approach can deal with dynamical

    更新日期:2020-06-18
  • Sketch-Guided Scenery Image Outpainting
    arXiv.cs.GT Pub Date : 2020-06-17
    Yaxiong Wang; Yunchao Wei; Xueming Qian; Li Zhu; Yi Yang

    The outpainting results produced by existing approaches are often too random to meet users' requirement. In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance. To this end, we propose an encoder-decoder based network to conduct sketch-guided outpainting, where two alignment modules are adopted to

    更新日期:2020-06-18
  • Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
    arXiv.cs.GT Pub Date : 2020-06-16
    Tom Yan; Christian Kroer; Alexander Peysakhovich

    Can we predict how well a team of individuals will perform together? How should individuals be rewarded for their contributions to the team performance? Cooperative game theory gives us a powerful set of tools for answering these questions: the Characteristic Function (CF) and solution concepts like the Shapley Value (SV). There are two major difficulties in applying these techniques to real world

    更新日期:2020-06-18
  • Linear Last-iterate Convergence for Matrix Games and Stochastic Games
    arXiv.cs.GT Pub Date : 2020-06-16
    Chung-Wei Lee; Haipeng Luo; Chen-Yu Wei; Mengxiao Zhang

    Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point optimization has received growing attention due to its favorable last-iterate convergence. However, its behavior for simple two-player matrix games is still not fully understood -- previous analysis lacks explicit convergence rates, only applies to exponentially small learning rate, or requires additional conditions such as uniqueness

    更新日期:2020-06-18
  • Edge computing based incentivizing mechanism for mobile blockchain in IOT
    arXiv.cs.GT Pub Date : 2020-06-16
    Liya Xu; Mingzhu Ge; Weili Wu

    Mining in the blockchain requires high computing power to solve the hash puzzle for example proof-of-work puzzle. It takes high cost to achieve the calculation of this problem in devices of IOT, especially the mobile devices of IOT. It consequently restricts the application of blockchain in mobile environment. However, edge computing can be utilized to solve the problem for insufficient computing power

    更新日期:2020-06-16
  • Matching Queues, Flexibility and Incentives
    arXiv.cs.GT Pub Date : 2020-06-16
    Francisco Castro; Peter Frazier; Hongyao Ma; Hamid Nazerzadeh; Chiwei Yan

    Motivated in part by online marketplaces such as ridesharing and freelancing platforms, we study two-sided matching markets where agents are heterogeneous in their compatibility with different types of jobs: flexible agents can fulfill any job, whereas each specialized agent can only be matched to a specific subset of jobs. When the set of jobs compatible with each agent is known, the full-information

    更新日期:2020-06-16
  • Certifying Strategyproof Auction Networks
    arXiv.cs.GT Pub Date : 2020-06-15
    Michael J. Curry; Ping-Yeh Chiang; Tom Goldstein; John Dickerson

    Optimal auctions maximize a seller's expected revenue subject to individual rationality and strategyproofness for the buyers. Myerson's seminal work in 1981 settled the case of auctioning a single item; however, subsequent decades of work have yielded little progress moving beyond a single item, leaving the design of revenue-maximizing auctions as a central open problem in the field of mechanism design

    更新日期:2020-06-15
  • Sound Search in Imperfect Information Games
    arXiv.cs.GT Pub Date : 2020-06-15
    Michal Šustr; Martin Schmid; Matej Moravčík; Neil Burch; Marc Lanctot; Michael Bowling

    Search has played a fundamental role in computer game research since the very beginning. And while online search has been commonly used in perfect information games such as Chess and Go, online search methods for imperfect information games have only been introduced relatively recently. This paper addresses the question of what is sound search in an imperfect information setting of two-player zero-sum

    更新日期:2020-06-15
  • Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
    arXiv.cs.GT Pub Date : 2020-06-15
    Stephen McAleer; John Lanier; Roy Fox; Pierre Baldi

    Finding approximate Nash equilibria in zero-sum imperfect-information games is challenging when the number of information states is large. Policy Space Response Oracles (PSRO) is a deep reinforcement learning algorithm grounded in game theory that is guaranteed to converge to an approximate Nash equilibrium. However, PSRO requires training a reinforcement learning policy at each iteration, making it

    更新日期:2020-06-15
  • Existential Theory of the Reals Completeness of Stationary Nash Equilibria in Perfect Information Stochastic Games
    arXiv.cs.GT Pub Date : 2020-06-15
    Kristoffer Arnsfelt Hansen; Steffan Christ Sølvsten

    We show that the problem of deciding whether in a multi-player perfect information recursive game (i.e. a stochastic game with terminal rewards) there exists a stationary Nash equilibrium ensuring each player a certain payoff is Existential Theory of the Reals complete. Our result holds for acyclic games, where a Nash equilibrium may be computed efficiently by backward induction, and even for deterministic

    更新日期:2020-06-15
  • Blocking defector invasion by focusing on the most successful partner
    arXiv.cs.GT Pub Date : 2020-06-15
    Attila Szolnoki; Xiaojie Chen

    According to the standard protocol of spatial public goods game, a cooperator player invests not only into his own game but also into the games organized by neighboring partners. In this work, we relax this assumption by allowing cooperators to decide which neighboring group to prefer instead of supporting them uniformly. In particular, we assume that they select their most successful neighbor and

    更新日期:2020-06-15
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