Algorithmic Game Theory Research Articles

Sensor network security defense strategy based on attack graph and improved binary PSO

Attack graph is a common tool for qualitative analysis of Sensor network security and it can provide an important basis for security administrators to prevent malicious intrusion. In order to conduct Sensor network security assessment and active defense, this paper proposed a Sensor network security defense strategy based on attack graphs and improved binary PSO. Based on each intrusion action in the attack graph, it constructed a set of weighted defense strategies to emphasize the defense cost. In order to prevent Sensor network malicious intrusion with minimum cost, the strategy introduced and improved binary particle swarm optimization algorithm and obtains the minimum key strategy set of attack graph. Based on the principle of M-IDS combined with game theory and attack pattern mining algorithm of Markov Decision Process (MDP), the optimal protection strategy is determined by game theory, and MDP is used to predict future attacks and design corresponding protection strategies. Simulation experiments show that compared with the ant colony algorithm and greedy algorithm, the proposed strategy can effectively obtain the optimal solution of the minimum key strategy set and it is more efficient.

IT governance in collaboration mode: building IT collaboration network using a socio technical approach based on actor network theory

IT governance of projects needs collaboration among several organisms. Collaboration is ensured by building network of collaboration between the collaborating entities; IT collaboration between organisations can play an important role to achieve business objectives. In order to build such networks of collaboration at a real scale need modelling social interactions between different actors in order to share, analyse, and suggest improvements for a collaborative perspective. This paper describes a new tool for collaboratively modelling based on actor network theory. *CollabANT system is based on actor network theory and game theory algorithm that efficiently provides abstract models of collaboration between different actors aiming at uncovering cost allocations concerns. We demonstrate the effectiveness of our approach with a real case study. The analyse of *interessement phase reveals that we are able to increase the cost saving objectives within a collaborative mode. We also present a what-if simulation feature to assess the impact of scenarios related to future collaboration evolution. Furthermore, we provide a live deployment of the *CollabANT system that allows users to explore the dynamics of collaboration networks in place as well as their involvement over time.

FPGA based acceleration of game theory algorithm in edge computing for autonomous driving

Abstract For autonomous driving area, the communication among autonomous vehicles is quite frequent and inevitable to perceive the surrounding environment. Massive data needs to be processed in time on the edge to control vehicles to make suitable strategies. For a group of vehicles, collaborative strategies are necessary to avoid unintentional congestions. However, less attention is paid to competition and cooperation between vehicle groups. In this paper, we explore the possibility of adopting game theory for decision making to obtain win-win between autonomous vehicles. The Lemke-Howson algorithm in game theory is the best known combinatorial algorithm that computes a Nash equilibrium of a bimatrix game. More importantly, we implement the Lemke-Howson algorithm on FPGA to accelerate the computation process. We explain the design challenges of solving the performance bottleneck and how to make optimizations. We implement the Lemke-Howson accelerator on a KCU116 board and obtain a speedup of about 2.4 times versus that running on a CPU.

A Survey on the Combined Use of Optimization Methods and Game Theory

Game theory is a field of applied mathematics that studies strategic behavior of rational factors. In other words, game theory is a collection of analytical tools that can be used to make optimal choices in interactional and decision making problems. Optimization in mathematics and computer science is the choice of the best member of an existing collection for a specific purpose. Several optimization methods have been used in many problems to minimize costs or maximize profits. From a particular point of view, it can be said that the game theory is in fact a kind of optimization. In this paper, a combined use of game theory and optimization algorithms has been reviewed and a new categorization is presented for researches which have been conducted in this area. In some of these combinations, game theory has been used to improve the performance of optimization algorithms, and in some others, optimizations methods help to solve game theory problems. Game theory and optimization algorithms are also used together to solve some other problems.

A Proof of Work: Securing Majority-Attack in Blockchain Using Machine Learning and Algorithmic Game Theory

Blockchain's vast applications in different industries have drawn several researchers to pursue extensive research in securing blockchain technologies. In recent times we could see several institutions coming together to create consortium based blockchain networks such as Hyperledger. Although for applications of blockchain such as Bitcoin, Litcoin, etc. the majority-attack might not be a great threat but for consortium based blockchain networks where we could see several institutions such as public, private, government, etc. are collaborating, the majority-attack might just prove to be a prevalent threat if collusion among these institutions takes place. This paper proposes a methodology where we can use intelligent software agents to monitor the activity of stakeholders in the blockchain networks to detect anomaly such as collusion, using supervised machine learning algorithm and algorithmic game theory and stop the majority attack from taking place.

Preface to the Special Issue on Algorithmic Game Theory

Preface to the Special Issue on Algorithmic Game Theory

Energy-Efficient Resource Allocation for Industrial Cyber-Physical IoT Systems in 5G Era

Cyber-physical Internet of things system (CPIoTS), as an evolution of Internet of things (IoT), plays a significant role in industrial area to support the interoperability and interaction of various machines (e.g., sensors, actuators, and controllers) by providing seamless connectivity with low bandwidth requirement. The fifth generation (5G) is a key enabling technology to revolutionize the future of industrial CPIoTS. In this paper, a communication framework based on 5G is presented to support the deployment of CPIoTS with a central controller. Based on this framework, multiple sensors and actuators can establish communication links with the central controller in full-duplex mode. To accommodate the signal data in the available channel band, the resource allocation problem is formulated as a mixed integer nonconvex programming problem, aiming to maximize the sum energy efficiency of CPIoTS. By introducing the transformation, we decompose the resource allocation problem into power allocation and channel allocation. Moreover, we consider an energy-efficient power allocation algorithm based on game theory and Dinkelbach's algorithm. Finally, to reduce the computational complexity, the channel allocation is modeled as a three-dimensional matching problem, and solved by iterative Hungarian method with virtual devices (IHM-VD). A comparison is performed with well-known existing algorithms to demonstrate the performance of the proposed one. The simulation results validate the efficiency of our proposed model, which significantly outperforms other benchmark algorithms in terms of meeting the energy efficiency and the QoS requirements.

Analyzing Power in Weighted Voting Games with Super-Increasing Weights

Weighted voting games (WVGs) are a class of cooperative games that capture settings of group decision making in various domains, such as parliaments or committees. Earlier work has revealed that the effective decision making power, or influence of agents in WVGs is not necessarily proportional to their weight. This gave rise to measures of influence for WVGs. However, recent work in the algorithmic game theory community have shown that computing agent voting power is computationally intractable. In an effort to characterize WVG instances for which polynomial-time computation of voting power is possible, several classes of WVGs have been proposed and analyzed in the literature. One of the most prominent of these are super increasing weight sequences. Recent papers show that when agent weights are super-increasing, it is possible to compute the agents’ voting power (as measured by the Shapley value) in polynomial-time. We provide the first set of explicit closed-form formulas for the Shapley value for super-increasing sequences. We bound the effects of changes to the quota, and relate the behavior of voting power to a novel function. This set of results constitutes a complete characterization of the Shapley value in weighted voting games, and answers a number of open questions presented in previous work.

Combined Bidding Strategy of Multi-owner Cascaded Hydropower Stations in Day-Ahead Market

Under the background of the electricity market reform, if the multi-owner cascade hydropower stations bid separately, the overall competitive advantages of river basin cannot be exerted, and the overall benefits cannot achieve the maximization. Based on the operation characteristics of cascade hydropower stations and the rule of competitive bidding, this paper established a bi-level optimal model for bidding game in day-ahead market, and used the Nash equilibrium principle of the game theory and genetic algorithm to solve this model, the optimal bidding strategies of the multi-owner cascade hydropower stations have been solved under the circumstances of bidding by oneself and alliance. The results from the calculating examples showed that the unified price declaration of the multi-owner cascade hydropower stations in day-ahead market can improve the overall and individual generation efficiency, and proved the effectiveness and feasibility of the combined bidding strategy in power market.

A Library for Algorithmic Game Theory in Ssreflect/Coq

We report on the formalization in Ssreflect/Coq of a number of concepts and results from algorithmic game theory, including potential games, smooth games, solution concepts such as Pure and Mixed Nash Equilibria, Coarse Correlated Equilibria, epsilon-approximate equilibria, and behavioral models of games such as best-response dynamics. We apply the formalization to prove Price of Stability bounds for, and convergence under best-response dynamics of, the Atomic Routing game, which has applications in computer networking. Our second application proves that Affine Congestion games are (5/3, 1/3)-smooth, and therefore have Price of Anarchy 5/2. Our formalization is available online.

D2D-Enabled Small Cell Network Control Scheme Based on the Dynamic Stackelberg Game

For current and future cellular networks, small cell structure with licensed and unlicensed bandwidth, caching content provisioning, and device-to-device (D2D) communications is seen as a necessary architecture. Recently, a series of control methods have been developed to address a myriad of challenges in next-generation small cell networks. In this study, we focus on the design of novel D2D-enabled small cell network control scheme by allowing caching and unlicensed D2D communications. Motivated by game theory and learning algorithm, the proposed scheme adaptively selects caching contents and splits the available bandwidth for licensed and unlicensed communications. Under dynamically changing network environments, we capture the dynamics of the network system and design a new dynamic Stackelberg game model. Based on a hierarchical and feedback based control manner, small base stations and users can be leaders or followers dynamically while improving 5G network performance. Simulations and performance analysis verify the efficiency of the proposed scheme, showing that our approach can outperform existing schemes by about 5%~15% in terms of bandwidth utilization, cache hit ratio, and system throughput.

Decision-making method of reconfigurable manufacturing systems’ reconfiguration by a Gale-Shapley model

The companies need to rapid response to new product introduction, mix and demand changes to stay competitive. A reconfigurable manufacturing system can quickly react to changes in products and market. The control method to reconfigure the machines of a reconfigurable manufacturing system is crucial for the performance level. This paper proposes a reconfiguration decision-making method based on a Game-Theory algorithm, and in particular the Gale-Shapley model. A periodic review strategy is used to create two sets: one set of machine over-loaded and one set under-loaded. The Gale-Shapley model forms a coupled of over-loaded and under-loaded machines. The reconfiguration concerns the under-loaded machine of the coupled adding also the task performed by the over-loaded machine. This paper presents a simulation environment developed to evaluate the proposed method and highlight the main topics. The simulation results highlight how the game-theory approach developed improves all the performance measures with controlled number of machines’ reconfigurations.

Setting production capacities for production agents making selfish routing decisions

ABSTRACTTraditionally, the capacity dimensioning step within the manufacturing system design process takes a known and fixed distribution of production flow across path alternatives to derive capacity demand based on a desired target utilisation rate. Setting target rates for machine utilisation provides limits on throughput times and allows to provide capacity buffers against changes in the production mix. In this contribution, we transfer this rationale into the world of Industry 4.0, where Cyber-Physical Systems can make autonomous and selfish routing decisions. Under this new framework, non-cooperative agents make decisions based on the capacity allocation and the decisions of all other agents, thus creating a feedback between flow and capacity distribution that makes existing methods for capacity dimensioning inapplicable. We use methods and insight from algorithmic game theory and operations research to investigate the capacity dimensioning process in this context. We prove properties of the throughput-time optimal allocation of production capacity under fixed target utilisation rates for important queue classes. Our findings not only provide a quantitative, easy to operationalise tool for production system designers, but also explore a trade-off between cost and flexibility that arises naturally in this regime.

Solving generic nonarchimedean semidefinite programs using stochastic game algorithms

A general issue in computational optimization is to develop combinatorial algorithms for semidefinite programming. We address this issue when the base field is nonarchimedean. We provide a solution for a class of semidefinite feasibility problems given by generic matrices. Our approach is based on tropical geometry. It relies on tropical spectrahedra, which are defined as the images by the valuation of nonarchimedean spectrahedra. We establish a correspondence between generic tropical spectrahedra and zero-sum stochastic games with perfect information. The latter have been well studied in algorithmic game theory. This allows us to solve nonarchimedean semidefinite feasibility problems using algorithms for stochastic games. These algorithms are of a combinatorial nature and work for large instances.

Game-theoretic modeling of curtailment rules and network investments with distributed generation

Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of several curtailment rules widely used in UK renewable energy projects, and their effect on the viability of renewable generation investment. Moreover, we propose a new curtailment rule which guarantees fair allocation of curtailment amongst all generators with minimal disruption. Another key knowledge gap faced by DNOs is how to incentivise private network upgrades, especially in settings where several generators can use the same line against the payment of a transmission fee. In this work, we provide a solution to this problem by using tools from algorithmic game theory. Specifically, this setting can be modelled as a Stackelberg game between the private transmission line investor and local renewable generators, who are required to pay a transmission fee to access the line. We provide a method for computing the equilibrium of this game, using a model that captures the stochastic nature of renewable energy generation and demand. Finally, we use the practical setting of a grid reinforcement project from the UK and a large dataset of wind speed measurements and demand to validate our model. We show that charging a transmission fee as a proportion of the feed-in tariff price between 15% and 75% would allow both investors to implement their projects and achieve desirable distribution of the profit. Overall, our results show how using game-theoretic tools can help network operators to bridge the knowledge gap about setting the optimal curtailment rule and determining transmission charges for private network infrastructure.

Game theoretic approach on Real‐time decision making for IoT‐based traffic light control

SummarySmart traffic light control at intersections is 1 of the major issues in Intelligent Transportation System. In this paper, on the basis of the new emerging technologies of Internet of Things, we introduce a new approach for smart traffic light control at intersection. In particular, we firstly propose a connected intersection system where every objects such as vehicles, sensors, and traffic lights will be connected and sharing information to one another. By this way, the controller is able to collect effectively and mobility traffic flow at intersection in real‐time. Secondly, we propose the optimization algorithms for traffic lights by applying algorithmic game theory. Specially, 2 game models (which are Cournot Model and Stackelberg Model) are proposed to deal with difference scenarios of traffic flow. In this regard, based on the density of vehicles, controller will make real‐time decisions for the time durations of traffic lights to optimize traffic flow. To evaluate our approach, we have used Netlogo simulator, an agent‐based modeling environment for designing and implementing a simple working traffic. The simulation results shows that our approach achieves potential performance with various situations of traffic flow.

Algorithmic information structure design

Information structure design , also sometimes known as signaling or persuasion , is concerned with understanding the effects of information on the outcomes of strategic interactions (the descriptive question), and in characterizing and computing the information sharing strategies which optimize some design objective (the prescriptive question). Both questions are illuminated through the lens of algorithms and complexity, as evidenced by recent work on the topic in the algorithmic game theory community. This monograph is a biased survey of this work, and paints a picture of the current state of progress and challenges ahead. We divide information structure design into single agent and multiple agent models, and further subdivide the multiple agent case into the public channel and private channel modes of information revelation. In each of these three cases, we describe the most prominent models and applications, survey the associated algorithms and complexity results and their structural implications, and outline directions for future work.

Empirical Distribution of Equilibrium Play and Its Testing Application

We show that in any n-player, m-action normal-form game, we can obtain an approximate equilibrium by sampling any mixed-action equilibrium a small number of times. We study three types of equilibria: Nash, correlated, and coarse-correlated. For each one we obtain upper and lower bounds on the number of samples required for the empirical distribution over the sampled action profiles to form an approximate equilibrium with probability close to 1.These bounds imply that using a small number of samples we can test whether or not players are playing according to an approximate equilibrium, even in games where n and m are large. In addition, our results substantially improve previously known upper bounds on the support size of approximate equilibria in games with many players. In particular, for the three types of equilibria we show the existence of approximate equilibrium with support-size polylogarithmic in n and m, whereas the previously best-known upper bounds were polynomial in n (Hémon et al. [Hémon S, de Rougemont M, Santha M (2008) Approximate nash equilibria for multi-player games. Algorithmic Game Theory (Springer), 267–278], Germano and Lugosi [Germano F, Lugosi G (2007) Existence of sparsely supported correlated equilibria. Econom. Theory 32(3):575–578], Hart et al. [Hart S, Mas-Colell A, Babichenko Y (2013) Simple Adaptive Strategies: From Regret-Matching to Uncoupled Dynamics, Vol. 4 (World Scientific Publishing Company)]).

A combination of game theory and genetic algorithm for load balancing in distributed computer systems

High demand of computation and communication in recent decades has increased the importance of distributed computer systems. Because of the heterogeneity of resources, resource management is one of the main challenges in designing distributed computer systems. In this paper, a new method has been proposed for solving load balancing problem in distributed computer systems using game theory and a genetic algorithm. The load balancing problem has been modelled as a non-cooperative game among users of the system. The payoff function of players was computed using an introduced parameter in order to decrease the users' expected response time. Certain Nash equilibriums are unstable in a multi agent problem. Thus, a genetic algorithm based on the concept of Nash equilibrium is used for solving the load balancing game. Simulation results show the performance of the proposed load balancing algorithm in terms of the expected response time and fairness index as parameters in evaluating the performance of load balancing algorithms.

A combination of game theory and genetic algorithm for load balancing in distributed computer systems

High demand of computation and communication in recent decades has increased the importance of distributed computer systems. Because of the heterogeneity of resources, resource management is one of the main challenges in designing distributed computer systems. In this paper, a new method has been proposed for solving load balancing problem in distributed computer systems using game theory and a genetic algorithm. The load balancing problem has been modelled as a non-cooperative game among users of the system. The payoff function of players was computed using an introduced parameter in order to decrease the users' expected response time. Certain Nash equilibriums are unstable in a multi agent problem. Thus, a genetic algorithm based on the concept of Nash equilibrium is used for solving the load balancing game. Simulation results show the performance of the proposed load balancing algorithm in terms of the expected response time and fairness index as parameters in evaluating the performance of load balancing algorithms.