A game-theoretic approach to integration of modules

This paper offers a new approach to integration of modules in an intelligent sensor system. Such a system requires that a set of modules-each doing a smaller portion of the overall task-be integrated within a unifying framework. From the perspective of computational systems, this problem holds a considerable interest because it is characterized by a set of coexisting mathematical objectives that need to be optimized simultaneously. In this sense, the design considerations necessitate the introduction of problem solving with multiple objectives. This paper explores these issues in the instance when each module is associated with a mathematical objective that is a function of the outputs of other modules. The integration problem is formulated and what is required of a good solution is presented. This examination interprets the decentralized mediation of conflicting subgoals as promoting a N-player game amongst the modules to be integrated and proposes a game-theoretic integration framework. We model the interaction among the modules as a noncooperative game and argue that this strategy leads to a framework in which the solutions correspond to a compromise decision. The application of this framework in image analysis motivates the hope that a framework such as game-theoretic integration will facilitate the development of general design principles for “modular” systems     

A game-theoretic approach to decision in FDI

Fault detection and isolation in dynamic systems rises decision problems in the presence of nuisance parameters (unknown initial states, unknown inputs, uncertain parameters). The strict decoupling strategy proposed in control approaches may sometimes fail to be applicable, while statistical decision rules can only be proven to be asymptotically optimal under special assumptions on the faults. In this note, a game-theoretic problem formulation, which is not purely geometrical and not purely statistical, is proposed and solutions are characterized under some reasonable assumptions.     

A game-theoretic approach to non-cooperative target assignment

In this paper, we have adopted a game-theoretic approach to consider the optimal assignment of targets to vehicles. We have taken optimality to mean the minimum sum of costs which are proportional to the Euclidean distance between a vehicle and its assigned target; this occurs when the set of targets is assigned to their nearest vehicles. In other words, each vehicle should assign itself to a target located within the Voronoi region associated with the vehicle. Standard schemes require communication between vehicles at least to rank the targets according to the nearest-neighbour rule. In our proposed model, there is no need for communication among the vehicles, and only limited communication between the targets and the vehicles is required. Specifically, we have introduced an appropriate utility function which depends on the distance between the vehicles and targets and the number of vehicles engaging a particular target. The vehicles negotiate their choice of targets via regret matching. We present simulations which demonstrate that vehicles select targets that fall within their Voronoi region. Vehicles that do not have any targets within their Voronoi region select their nearest unassigned target. We also present a communication model illustrating how the model can be implemented in a practical scenario.     

A game-theoretic approach to a finite-time disturbance attenuationproblem

A disturbance attenuation problem over a finite-time interval is considered by a game theoretic approach where the control, restricted to a function of the measurement history, plays against adversaries composed of the process and measurement disturbances, and the initial state. A zero-sum game, formulated as a quadratic cost criterion subject to linear time-varying dynamics and measurements, is solved by a calculus of variation technique. By first maximizing the quadratic cost criterion with respect to the process disturbance and initial state, a full information game between the control and the measurement residual subject to the estimator dynamics results. The resulting solution produces an n-dimensional compensator which expresses the controller as a linear combination of the measurement history. A disturbance attenuation problem is solved based on the results of the game problem. For time-invariant systems it is shown that under certain conditions the time-varying controller becomes time-invariant on the infinite-time interval. The resulting controller satisfies an H _∞ norm bound     

A passivity approach to game-theoretic CDMA power control

This paper follows a game-theoretical formulation of the CDMA power control problem and develops new decentralized control algorithms that globally stabilize the desired Nash equilibrium. The novel approach is to exploit the passivity properties of the feedback loop comprising the mobiles and the base station. We first reveal an inherent passivity property in an existing gradient-type algorithm, and prove stability from the Passivity Theorem. We then exploit this passivity property to develop two new designs. In the first design, we extend the base station algorithm with Zames-Falb multipliers which preserve its passivity properties. In the second design, we broaden the mobile power update laws with more general, dynamic, passive controllers. These new designs may be exploited to enhance robustness and performance, as illustrated with a realistic simulation study. We then proceed to show robustness of these algorithms against time-varying channel gains.     

A game-theoretic approach to advertisement dissemination in ephemeral networks

In ephemeral networks, disseminating advertisements faces two dilemmatic problems: on the one hand, disseminators own the limited resources and have privacy concerns, thus, often preferring to avoid disseminating advertisements without enough incentives; Even if advertisements are disseminated, their dissemination accuracy is lower. On the other hand, false advertisements may flood in ephemeral networks if too many incentives but no punishments are given. Thus, it is a challenge to design an effective scheme to guarantee rational disseminators have sufficient impetus to forward true advertisements to the interested consumers and report false advertisements, despite facing the limitation of resources and the risk of privacy leakage. To solve this problem, in this paper, a bargaining-based scheme is proposed to motive disseminators to forward the true advertisements to the interested node and a semi-grim policy is designed for punishing the disseminators who releases and disseminates false advertisements. Acknowledging the assumption of incomplete information, a repeated dissemination game is proposed to help disseminators to decide whether to forward advertisements or report false advertisements. Simulation results demonstrate that our scheme not only provides disseminators a strong impetus to disseminate the advertisements with higher dissemination accuracy, but also effectively prevents disseminators from forwarding false advertisements.     

Case-based exploration of bidirectional transformations in QVT Relations

QVT Relations (QVT-R), a standard issued by the Object Management Group, is a language for the declarative specification of model transformations. This paper focuses on a particularly interesting feature of QVT-R: the declarative specification of bidirectional transformations. Rather than writing two unidirectional transformations separately, a transformation developer may provide a single relational specification which may be executed in both directions. This approach saves specification effort and ensures the consistency of forward and backward transformations. This paper explores QVT-R’s support for bidirectional model transformations through a spectrum of transformation cases. The transformation cases vary with respect to several factors such as the size of the transformation definition or the relationships between the metamodels for source and target models. The cases are solved in QVT-R, but may be applied to other bidirectional transformation languages, as well; thus, they may be used as a benchmark for comparing bidirectional transformation languages. In our work, we focus on the following research questions: functionality of bidirectional transformations in terms of relations between source and target models, solvability (which problems may be solved by a single relational specification of a bidirectional transformation), variability (does a bidirectional transformation contain varying elements, i.e., elements being specific to one direction), comprehensibility (referring to the ease of understanding and constructing QVT-R transformations), and the semantic soundness of bidirectional transformations written in QVT-R.     

An evolutionary game-theoretic approach to congestion control

This paper investigates a system where a set of users sharing a bottleneck link must choose the transmission rate at which multimedia traffic is received. Users are assumed to be self-regarding and make their decisions with the sole goal of maximizing their perceived quality. We are interested in the dynamic process by which users adapt their data rates and the convergence of this process to equilibria. We propose a novel two-layer model to represent this system: the upper layer is an evolutionary game-theoretic model that captures how users adapt their rates; the lower layer model captures the network performance and the quality perceived by the users. Using the model proposed, we demonstrate analytically and numerically several interesting properties of the system equilibria. In particular, we establish the relationship between system states that have non-negligible steady state probabilities and Nash equilibria of the induced game.     

A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid

This paper proposes a Stackelberg game approach to maximize the profit of the electricity retailer (utility company) and minimize the payment bills of its customers. The electricity retailer determines the retail price through the proposed smart energy pricing scheme to optimally adjust the real-time pricing with the aim to maximize its profit. The price information is sent to the customers through a smart meter. According to the announced price, the customers can automatically manage the energy use of appliances in the households by the proposed optimal electricity consumption scheduling system with the aim to minimize their electricity bills. We model the interactions between the retailer and its electricity customers as a 1-leader, N-follower Stackelberg game. At the leader’s side, i.e., for the retailer, we adopt genetic algorithms to maximize its profit while at the followers’ side, i.e., for customers, we develop an analytical solution to the linear programming problem to minimize their bills. Simulation results show that the proposed approach is beneficial for both the customers and the retailer.     

A game-theoretic approach for the web services scheduling problem

We address the design of an Internet-based business process composed of several web services by using multiobjective optimization and game-theoretic methods. Adopting a suitable representation for the business process, we present a mathematical optimization problem which considers several quality-of-service objectives: cost, execution time, reliability, availability and reputation. The web service scheduling problem is formulated as a multiobjective mixed-integer linear optimization problem and solved through a goal optimization method. The optimal solution of the scheduling problem assigns suppliers to all the tasks that comprise the business process, thus establishing the revenues – utilities – of all the suppliers. We then model the interaction between the suppliers as an incomplete information (Bayesian) game: the structure of the game is common knowledge of all the suppliers, but each supplier knows only his/her own utility function. A characterization of the Bayes–Nash equilibria of the game is provided. The paper includes numerical examples.     

A game-theoretic approach for integrity assurance in resource-bounded systems

Assuring communication integrity is a central problem in security. However, overhead costs associated with cryptographic primitives used toward this end introduce significant practical implementation challenges for resource-bounded systems, such as cyber-physical systems. For example, many control systems are built on legacy components which are computationally limited, but have strict timing constraints. If integrity protection is a binary decision, it may simply be infeasible to introduce into such systems; without it, however, an adversary can forge malicious messages, which can cause significant physical or financial harm. To bridge the gap between such binary decisions, we propose a stochastic message authentication approach that can explicitly trade computational cost off for security. We introduce a formal game-theoretic framework for optimal stochastic message authentication, providing provable guarantees for resource-bounded systems based on an existing message authentication scheme. We use our framework to investigate attacker deterrence, as well as optimal stochastic message authentication when deterrence is impossible, in both short-term and long-term equilibria. Additionally, we propose two schemes for implementing stochastic message authentication in practice, one for saving computation only at the receiver and one for saving computation at both ends, and demonstrate the associated computational savings using an actual implementation.     

Efficient management of interconnected power systems: A game-theoretic approach

The optimal management over a one year planning horizon, of two interconnected hydro-thermal power systems is considered. The optimal production in each system is modelled as a stochastic control problem whose solution is searched in a particular class of control strategies. The efficient exchange of energy between the two systems and its pricing are viewed as a cooperative game and the Nash-Harsanyi bargaining solution is characterized. Various information structures for the exchange and price strategies are discussed and it is shown that, in all cases, the price strategy is equivalent to the definition of a compensatory side payment which equalizes the advantages accruing to each of the two players with respect to a status quo situation where no interconnection is available. A numerical illustration based on a typical European power system is presented to assess the potential gain when using a closed loop exchange strategy instead of an open loop one.     

A simple approach to system modeling

This paper describes an approach to system modeling based on heuristic mean value analysis. The virtues of the approach are conceptual simplicity and computational efficiency. The approach can be applied to a large variety of systems, and can handle features such as resource constraints, tightly and loosely coupled multiprocessors, distributed processing, and certain types of CPU priorities. Extensive validation results are presented, including truly predictive situations. The paper is intended primarily as a tutorial on the method and its applications, rather than as an exposition of research results.     

Power-efficient video encoding on resource-limited systems: A game-theoretic approach

Most mobile video applications are often deployed on battery-operated devices. With limited power supply, it is a challenging issue to support video applications with high resolution due to the complex functionality and high resource requirements. Thus, power-efficient design is important in computation intensive applications especially for mobile video terminals. Previous works on power-efficient control in video encoding systems focus on the low complexity design while typically ignoring the impact of scalable design by considering various power consumption involved in the encoding process. This paper is dedicated to developing a power-scalable video encoding (PSVE) strategy for energy-limited mobile terminals. PSVE can help the video encoding terminal to adjust its power consumption budget efficiently so as to enhance the power-scalable capability in mobile video terminals. This paper first establishes game theoretical analysis to model the power consumption problem as a bargaining problem. Then, the tradeoff between encoding effect and power consumption achieved by the use of game theory. The scalable and low power video encoding system based on Nash equilibrium solution is derived through the analysis on the power consumption and encoding effect. Experimental results demonstrate the efficiency of the proposed approach.     

Autonomic-computing approach to secure knowledge management: a game-theoretic analysis

The explosion of knowledge management systems (KMS) and the need for their wide accessibility and availability has created an urgent need for reassessing the security practices and policies in organizations. Security of these assets is a day-to-day job placing a tremendous cognitive load on information-technology (IT) professionals, which can make it almost impossible to manage the security aspects of KMS. Autonomic-computing systems are well suited to manage KMS, as they use high-level system objectives provided by administrators as the basis for managing the security of KMS. The authors model the self-protection and self-healing configuration attributes in autonomic systems through game-theoretic models. The proposed modeling approach progressively moves from a manual intervention-oriented security setup to an autonomic security setup. This allows the authors to compare and contrast the different approaches and provide insights on their applicability to different security environments. The authors find that moving to a partial autonomic system with self-healing mechanisms can provide a stable environment for securing enterprise knowledge assets and can reduce hacking. It is beneficial to implement an autonomic system when manual investigation costs are higher and/or when the volume of malicious traffic is very low. An autonomic approach is especially attractive when it is difficult to impose penalties on malicious users. Autonomic systems can be effective in securing organizational knowledge assets and in reducing the potential damage from malicious users.     

A simple approach to structural frequency optimization

This paper presents a simple solution strategy to find the shape and topology of a general structure that maximize or minimize the natural frequency. The structure is modelled with a fine mesh of finite elements. During an evolutionary process, a small part of the material is removed from the structure at the end of each finite element analysis. A criterion is established as to which elements should be eliminated so that the frequency of the resulting structure can be increased or reduced. It is found that the proposed simple method is effective in solving frequency optimization problems which usually require sophisticated mathematical programming techniques to solve.     

A minimax approach to a simple routing problem

A simple routing problem is considered in which there are two service stations. Subject to burstiness constraints on the arrival of work, the worst-case delay performance of dynamic routing algorithms is studied. Under a light loading condition, it is found that the well-known `route-to-shortest' (RTS) rule is optimal in some sense. On the other, it is shown that under a heavy load condition, the RTS rule is not optimal. In addition, the performance of RTS routing is compared with fixed routing. Finally, the burstiness constraints are relaxed to obtain a result which bounds the average backlogs in the stations for the RTS rule under light loading     

A simple approach to solving the Diophantine equation

Based on the state-space concepts, a simple approach to finding all polynomial matrix solutions of the Diophantine equation is proposed. The procedure presented is very simple in comparison to earlier ones. Unlike earlier ones, it is not necessary to solve any equation. Only two constant matrices which could be selected at random are required. All solutions are expressed in an explicit formula form     

Spatial approaches to broadband jamming in heterogeneous mobile networks: a game-theoretic approach

In this paper, we address the problem of a mobile intruder jamming the communication network in a vehicular formation. In order to understand the spatial aspect of the jamming problem, we consider a jamming model that takes into account the relative distance of the jammer from the vehicles. We formulate the problem as a zero-sum pursuit-evasion game between a jammer and a team of players with players possessing heterogeneous dynamics. We use Isaacs’ approach to arrive at the equations governing the optimal strategies of the team of players. Finally, we obtain the optimal trajectories in the neighborhood of termination by numerically simulating the strategies for some variants of the problem.     

A simple filter-and-fan approach to the facility location problem

The design of effective neighborhood search procedures is a primary issue for the performance of local search and advanced metaheuristic algorithms. Several recent studies have focused on the development of variable depth neighborhoods that generate sequences of interrelated elementary moves to create more complex compound moves. These methods are chiefly conceived to produce an adaptive search as the number of elementary moves in a compound move may vary from one iteration to another depending on the state of the search. The objective is to achieve this goal with modest computational effort. Although ejection chain methods are currently the most advanced methods in this domain, they usually require more complex implementations. The filter-and-fan (F&F) method appears as an alternative to ejection chain methods allowing for the creation of compound moves based on an effective tree search design.