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This paper presents an augmented reality-based Chinese character composition game (ARC) that employs augmented papers to engage lower primary school students in collaborative Chinese character learning. A design research approach was used to gain a holistic view of designing, enacting, and evaluating the ARC. The participants included four teachers and five classes of students from two primary schools. The school-based research findings suggested that: 1) the ARC was an effective system for improving beginning learners’ orthographic knowledge of Chinese characters, particularly for those students with low Chinese language proficiency; 2) the tangible paper interface engaged students in group work and increased the game playfulness; and 3) students’ language proficiency had a significant effect on their perceptions of ease of use and usefulness of the augmented paper game. This paper not only substantiated the potential of the augmented paper as a carrier of educational content, but also instantiated how to implement it to promote social and cognitive supports for interaction and collaboration. [ABSTRACT FROM AUTHOR]

In this paper, we consider encoding strategies for the Z-channel with noiseless feedback. We analyze the combinatorial setting where the maximum number of errors inflicted by an adversary is proportional to the number of transmissions, which goes to infinity. Without feedback, it is known that the rate of optimal asymmetric-error-correcting codes for the error fraction $\tau \ge 1/4$ vanishes as the blocklength grows. In this paper, we give an efficient feedback encoding scheme with $n$ transmissions that achieves a positive rate for any fraction of errors $\tau < 1$ and $n\to \infty $. Additionally, we state an upper bound on the rate of asymptotically long feedback asymmetric error-correcting codes. [ABSTRACT FROM AUTHOR]

This is the second part of the IEEE JSAC Special Issue on “Game Theory for Networks.” The response of the community to the call has been overwhelming. We received a total of 120 submissions. We want to thank all the authors who submitted their works to this Special Issue. After a strict and selective review process, we accepted 40 papers and decided to publish two issues, each of 20 papers. The first one was published in February 2017. The papers of this second issue cover a wide selection of topics as follows. [ABSTRACT FROM PUBLISHER]

Since modern smart grids have various and deeply coupled cyber-physical components, they are vulnerable to malicious cyber attacks. Although regular defenses including firewall and IDS are deployed, they may be weakened by zero-day vulnerabilities and sophisticated attack schemes. Therefore, defense strategies to mitigate the risk of blackouts during cyber attacks are necessary. This paper proposes a cyber-physical coordinated defense strategy to overcome the disruption and minimize the risk as much as possible. At the cyber layer, a zero-sum multilevel Markovian Stackelberg game is proposed to model sequential actions of the attacker and the defender. The defender distributes defensive resources to protect lines in a real-time manner, according to the attacker's action. If cyber attacks should result in physical outages, defense at the physical layer is then employed. A security-constrained optimal power flow reserving security margin of critical components will be performed to minimize the blackout scale and potential future risk. To solve the corresponding optimization problem and further get the optimal defense strategy, this paper devises a novel “water-pouring” algorithm. Lastly, test results show that the proposed dynamic defense strategy mitigates risk significantly and outperforms existing methods. [ABSTRACT FROM AUTHOR]

Wireless network virtualization, which decouples the physical supply process and the service provisioning process, can abstract, isolate, and share the physical infrastructure network equipment. This paper studies the resource virtualization in virtual multiple-input multiple-output single-carrier frequency-division-multiple-access uplink systems, where resources are abstracted to hide the complex details of the fading channel and the link rates are virtualized using the statistical method. Furthermore, the virtual link rates are scheduled and instantiated to different slices with customized delay-bounded quality of service (QoS) provisioning. In this scheme, a physical mobile network operator (PMNO) is in charge of the network resource at the physical layer, while virtual mobile network operators (VMNOs) are responsible for the traffic admission and the slice management at the MAC layer. Furthermore, we build up the resource virtualization problem as a cross-layer Stackelberg game, which has the interactive dual processes based on the QoS exponent: top-to-down sub-game of leaders at the MAC layer and down-to-top sub-game of follower at the physical layer. Using the newly designed functions for PMNO and VMNOs, we develop an effective dynamic algorithm with an iterative dual update to meet the optimization targets of PMNO and VMNOs. Simulation results verify the superiority and stability of delay-bounded QoS guaranteed wireless resource virtualization algorithm developed in this paper in terms of convergence, access rate, and delay-outage probability. [ABSTRACT FROM AUTHOR]

Operational decisions made by manufacturers and retailers are often affected by the power relationships of both vertical and horizontal competitions in a supply chain. Although many studies have been carried to assess how the power structure impacts supply chains, most of them only focus on the power dynamics in the vertical competition or horizontal competition. This paper systematically examines the impacts of bidimensional power structure on a supply chain. Considering a triadic setting that is made of two rival retailers and a common manufacturer, we compare the equilibriums for the supply chain and individual firms with five different possible power structures. The analysis results show that the bidimensional power dynamics have a significant impact. Furthermore, the impacts of the bidimensional power structure are also dependent on the nature of customer demand. This paper provides important management implications that are supportive for companies to make strategic and operational decisions. [ABSTRACT FROM AUTHOR]

In this paper, a variation of the congestion situation is considered and a new game, the congestion game with player-specific (CGPS) utility functions, is proposed. This paper is motivated by some application scenarios that rule out the possibility of employing the existing game model to study such congestion situations. The CGPS game is characterized by adding a player-specific term and a weighted parameter with respect to the utility function. By using the semitensor product of matrices, the algebraic representation of the CGPS game is given and the existence of the weighted potential function is proved. Finally, the results are applied to solve the service chain composition problem in network function virtualization (NFV) and to analyze the effect of player-specific function on service chain configuration in NFV. Note to Practitioners—This paper is motivated by resource allocation problems in congestion networks where strategic users behave selfishly and aim at optimizing their own individual utility in the absence of a central controller. Compared with the centralized algorithms of poor reliability and scalability, game-theoretic control provides a promising distributed approach for resource allocation. In the game-theoretic framework, the existence and seeking of the desired solution are important issues. In this paper, a novel model is established to extend the utility functions space guaranteeing the existence of the solution. The developed utility design is used to capture users’ different sensitivities to the effects of the network system. Simultaneously, it is more meaningful from the view of engineering to design the utility functions so that the desirable behavior is reachable. We also give an explicit scheme to seek the desired solution. The proposed model is finally applied to the service chain composition problem in NFV, of which the aim is to find the best service chain of users that accommodates their individual requirements. The proposed model shows reliable and effective. [ABSTRACT FROM AUTHOR]

Growing cybersecurity risks in the power grid require that utilities implement a variety of security mechanisms (SMs), including VPNs, firewalls, authentication, and access control mechanisms. While these mechanisms provide some level of defense, they also may contain software vulnerabilities which allow an attack to bypass their protection. Because the same SM type is often used to protect a large number of substations, a single vulnerability could enable a coordinated attack by simultaneously targeting many substations. To protect against such an attack, utilities can adopt a strategy to use a diverse set of SMs, such that the impact from a vulnerability in any SM is minimized. This paper introduces a game-theoretic graph coloring technique to determine the optimal allocation of SM diversity that minimizes the impact of security vulnerabilities to the grid. This paper demonstrates that the proposed approach provides a Nash equilibrium solution. Furthermore, the technique is demonstrated against cyber-physical models for both IEEE-14 and IEEE-118 bus systems, and compared with other non-strategic diversity allocation methods to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyze the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The paper also highlights important open research challenges in the field. [ABSTRACT FROM PUBLISHER]

We study how to efficiently allocate the infinitesimal divisible resource under auction mechanism in a dynamic way. We propose a Vickrey–Clarke–Groves-type auction mechanism with a 2-D bid which specifies a per unit price and a maximum of the demand. Due to the absence of enough information related to the infinite dimensional valuations of individual players in a single-bid strategy, it is challenging to implement the efficient Nash equilibrium (NE) in a dynamic way. In this paper, we introduce a pair of parameters related to players’ valuations, and design a decentralized dynamic process assisted with this pair of values, such that at each iteration, a single player updates its best bid under a constrained set of demand. Under the proposed auction mechanism, we show the incentive compatibility, efficiency, and uniqueness of the NE. Furthermore, our method is guaranteed to converge to the efficient NE, and it presents the enhanced convergence performance compared with those methods proposed in the literature. Case studies are given to demonstrate the results developed in this paper. [ABSTRACT FROM PUBLISHER]

In this paper, we study the stabilization and finite-time stabilization of probabilistic Boolean control networks (PBCNs). A complete family of reachable sets is defined first, based on which, feedback stabilization conditions are obtained. Then a way to find all possible state feedback controllers are presented for the stabilization of PBCNs accordingly. Moreover, it has been stated that the approach in this paper can also be applied to finite-time stabilization via some changes in the construction of set sequence. Finally, an evolutionary networked game is given as an example to illustrate the efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

At the 2006 IEEE International Conference on Systems of Systems Engineering, Boardman and Sauser presented a set of characteristics based on an integrative review of the literature. These characteristics are the foundation of the Boardman–Sauser system of systems (SoS) theory. We accept this theory as the basis of collaborative SoS and desire a means to validate this assertion. While numerous simulations have been published, the target SoS is usually a specific implementation. These specialized simulations produce results applicable primarily to the system under study but not necessarily SoS in general. Simulating the mathematical model of the Boardman–Sauser SoS theory in the form of an agent-based model produces results that can be compared with chemical reactions. This paper contends that the formation of molecules from atoms is an analog of systems forming any collaborative SoS, which we call our molecule organization model. The result is a supported logical argument that validates the Boardman–Sauser SoS theory as definitive of a generic SoS with behavior applicable to collaborative SoS. This paper seeks to provide a unique contribution to the SoS body of knowledge by increasing our understanding of collaboration in an SoS, so we may better understand their health, maintenance, replication, and evolution. [ABSTRACT FROM PUBLISHER]

Video games are one of the richest and most popular forms of human-computer interaction and, hence, their role is critical for our understanding of human behaviour and affect at a large scale. As artificial intelligence (AI) tools are gradually adopted by the game industry a series of ethical concerns arise. Such concerns, however, have so far not been extensively discussed in a video game context. Motivated by the lack of a comprehensive review on the ethics of AI as applied to games, we survey the current state of the art in this area and discuss ethical considerations of these systems from the holistic perspective of the affective loop. Through the components of this loop, we study the ethical challenges that AI faces in video game development. Elicitation highlights the ethical boundaries of artificially induced emotions; sensing showcases the trade-off between privacy and safe gaming spaces; and detection, as utilised during in-game adaptation, poses challenges to transparency and ownership. This paper calls for an open dialogue and action for the games of today and the virtual spaces of the future. By setting an appropriate framework we aim to protect users and to guide developers towards safer and better experiences for their customers. [ABSTRACT FROM AUTHOR]

This paper provides an accessible explanation of the working mechanism of Monte-Carlo Tree Search, an influential search algorithm. The paper summarizes the procedure of Monte-Carlo Tree Search, including selection, expansion, simulation, and backpropagation. Additionally, immersive examples based on Tic-Tac-Toe, Go, and Sokoban, two two-player competitive games and a classic single-player puzzle game, are presented to illustrate how Monte-Carlo Tree Search works. The full article with interactive contents is published on IEEE Xplore. [ABSTRACT FROM AUTHOR]

In this paper, a novel management framework for fog computing with strategic computing speed control at fog nodes (FNs) is studied. In the considered model, mobile users declare requests of offloading resource-hungry computation tasks that are dynamically collected at a dedicated edge server (ES). Upon receiving these requests, the ES can decide to either self-process or delegate some workloads to third-party FNs for maximizing the overall management profit. Unlike the existing work, this paper takes into account strategic behaviors of FNs in computing speed control, i.e., each FN can strategically allocate its computing resource to maximize its utility, which consists of the benefit gained from executing offloaded tasks and the cost incurred by dissatisfied (delayed) service to its own subscribed tasks. To jointly address the long-term system performance and FNs’ strategic interactions, a scheduling mechanism integrating a noncooperative game and a queueing model is formulated. We then investigate two delegation reward settings, i.e., constant and utility-dependent delegation prices, and propose efficient adaptive algorithms to determine the optimal workload distribution at the ES and the computing speed equilibrium among FNs. Both theoretical analyses and simulations are conducted to evaluate the performance of the proposed solutions and demonstrate their superiorities over counterparts. [ABSTRACT FROM AUTHOR]

Gaming video streaming services have grown tremendously in the past few years, with higher resolutions, higher frame rates and HDR gaming videos getting increasingly adopted among the gaming community. Since gaming content as such is different from non-gaming content, it is imperative to evaluate the performance of the existing encoders to help understand the bandwidth requirements of such services, as well as further improve the compression efficiency of such encoders. Towards this end, we present in this paper GamingHDRVideoSET, a dataset consisting of eighteen 10-bit UHD-HDR gaming videos and encoded video sequences using four different codecs, together with their objective evaluation results. Additionally, the paper discusses the codec compression efficiency of most widely used practical encoders, i.e., x264 (H.264/AVC), x265 (H.265/HEVC) and libvpx (VP9), as well the recently proposed encoder libaom (AV1), on 10-bit, UHD-HDR content gaming content. Our results show that the latest compression standard AV1 results in the best compression efficiency, followed by HEVC, H.264, and VP9. [ABSTRACT FROM AUTHOR]

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multiobjective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e., multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called module mutation. Several versions of module mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games. [ABSTRACT FROM PUBLISHER]

A substantial body of literature on global design optimization techniques is based on an implicit assumption that a system under optimization is designed by a perfectly centralized design team with all team members committed to a global optimum solution. This assumption enables the system engineers to minimize all the objective functions simultaneously while controlling all design decisions at the same time. Recent research suggested that this inherent assumption may need to be revisited. This paper proposes a novel framework for design optimization using game theory, which represents a design process as a normal-form game and unifies two fundamental decisions of discipline designers, namely a system-global or a discipline-local optimum solution. This paper provides a general mathematical formulation of the framework for a design process with any number of discipline designers. The framework introduces the notion of the Nash front, which is a set of design solutions consisting of the Nash equilibria derived through the reallocation of design authority between discipline designers. This paper then tests the framework on a numerical optimization case study of a Stirling micro combined heat and power plant with two discipline designers. The result indicates that for the system capital cost and the system power output the difference between global Pareto-optimum designs and the Nash front designs on average across five different scenarios of design authority allocation was 22% and 16%, correspondingly. The application of the novel framework in different fields of engineering could help to build models that are more realistic and prevent impractical technical objectives. [ABSTRACT FROM AUTHOR]

This paper considers secret-key generation between several agents and a base station that observe independent and identically distributed realizations of correlated random variables. Each agent wishes to generate the longest possible individual key with the base station by means of public communication. All keys must be jointly kept secret from all external entities. In this many-to-one secret-key generation setting, it can be shown that the agents can take advantage of a collective protocol to increase the sum rate of their generated keys. However, when each agent is only interested in maximizing its own secret-key rate, agents may be unwilling to participate in a collective protocol. Furthermore, when such a collective protocol is employed, how to fairly allocate individual key rates arises as a valid issue. This paper studies the tension between cooperation and self-interest with a game-theoretic treatment. This paper establishes that cooperation is in the best interest of all individualistic agents and that there exist individual secret-key rate allocations that incentivize the agents to follow the protocol. In addition, an explicit coding scheme that achieves such allocations is proposed. [ABSTRACT FROM AUTHOR]

In this paper, we present a method to learn (infer) and refine a set of advices from the trajectories generated in the successful and failed attempts in a task or game, in the form of advisory signal temporal logic (STL) formulas. Each advice consists of an advisory motion STL formula that characterizes the spatial–temporal pattern of the motion as a feature of success and an advisory selection STL formula as a criterion for the environment to select the advice. For the inference of advisory STL formulas, we provide a theoretical framework of perfect classification with a labeled set of trajectories with different time lengths. We design an advisory controller that can drive the robots to satisfy an advisory motion STL formula based on the advice selected according to the advisory selection STL formula. The advisory controller can advise or guide the human operators or the robots for better performance with the shared autonomy between the human operator and the controller. We provide two case studies to test the effectiveness of the advisory controller, one with a Baxter-On-Wheels simulator and the other with two quadrotors in an experimental testbed in iteratively improving the success rates of completing the tasks with the help of the designed advisory controller. Note to Practitioners—The method described in this paper can be used to obtain knowledge or patterns of the environment based on the trajectories generated by the human operators/demonstrators and utilize the obtained knowledge or patterns for improving the performance of the future operators. The obtained information is the logical statements about the waypoints or subgoals to be reached, and obstacles or dangerous regions to be avoided during certain time intervals, when the environment satisfies certain conditions also expressed in the form of logical statements. The methodology of inferring knowledge from data and designing advisory controllers for guiding or helping future practices is potentially useful in many applications where the dynamic mathematical model of the external environment is unknown. [ABSTRACT FROM AUTHOR]

This paper aims to develop the insights into Bayesian truth serum (BTS) mechanism by postulating a sequence of seven natural conditions reminiscent of axioms in information theory. The condition that reduces a larger family of mechanisms to BTS is additivity, akin to the axiomatic development of entropy. The seven conditions identify BTS as the unique scoring rule for ranking respondents in situations in which respondents are asked to choose an alternative from a finite set and provide predictions of their peers’ propensities to choose, for finite or infinite sets of respondents. [ABSTRACT FROM AUTHOR]

In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging start time and charging energy profiles to minimize overall EV charging energy cost by including consideration of the actions of the neighboring aggregators. The competitive interactions of the aggregators are modeled by developing a two-stage non-cooperative game among the aggregators. The game is then studied under prospect theory to examine the impacts of non-ideal actions of the aggregators in selecting EV charging start times according to subjectively evaluating their opponents’ actions. It is shown that the non-cooperative interactions among the aggregators lead to a subgame perfect $\epsilon $ -Nash equilibrium when the game is played with either ideal, or non-ideal, actions of the aggregators. A case study presented demonstrates that the benefits of the coordinated EV charging strategy, in terms of energy cost savings and peak-to-average ratio reductions, are significantly resilient to non-ideal actions of the aggregators. [ABSTRACT FROM AUTHOR]

With the deep integration of modern technology and agricultural production, the development of unmanned precision agriculture has become a breakthrough for agricultural upgrading. Unmanned farms rely on multi-source data provided by dense wireless sensor networks (WSN), which causes greater pressure on the scarce spectrum resources. Combining fuzzy clustering and game theory, this paper studies a joint optimization method for cognitive wireless sensor networks (CWSN) to optimize agricultural data transmission and make full use of network resources. Two objective functions are designed to maximize the network energy efficiency and minimize the transmit power, so as to optimize the transmit power and transmission channel in CWSN. Different optimization objectives are regarded as different players, which turns the multi-objective optimization into a game decision-making problem. By defining the influence model of design variables on different objective functions, fuzzy clustering is adopted to divide the strategy subspace for each player. Then, this paper studies a two-layer cooperative game algorithm. Each node chooses the transmit power and channel in the strategy space based on their price function. The game scheme is obtained by a multi-round two-layer cooperative game. The experimental results show that compared with NSGA-ò, the reinforcement learning (RL) algorithm, and the non-cooperative game method, the joint optimization algorithm can reduce network interference and improve network QoS. [ABSTRACT FROM AUTHOR]

This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). Under a business agreement with the InP, a third-party service provider provides computing services to the subscribed mobile users (MUs). MUs compete for the shared spectrum and computing resources over time to achieve their distinctive goals. From the perspective of an MU, we deliberately define the age of update to capture the staleness of information from refreshing computation outcomes. Given the system dynamics, we model the interactions among MUs as a stochastic game. In the Nash equilibrium without cooperation, each MU behaves in accordance with the local system states and conjectures. We can hence transform the stochastic game into a single-agent Markov decision process. As another major contribution, we develop an online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks to approximate the Q-factor and the post-decision Q-factor, respectively. The deep RL scheme allows each MU to optimize the behaviours with unknown dynamic statistics. Numerical experiments show that our proposed scheme outperforms the baselines in terms of the average utility under various system conditions. [ABSTRACT FROM AUTHOR]

Currently, electroencephalogram (EEG) is mostly analyzed in a supervised way, which requires EEG labels (e.g., EEG classification). With the ever-increasing amount of unlabeled/mislabeled EEG in neuropsychiatric disorder diagnosis, BCI, and rehabilitation, manually labeling of EEG data is a labor intensive and time-consuming process, and few labs have developed algorithms to analyze EEG in an unsupervised manner (i.e., EEG clustering). In this paper, we propose a cooperative game inspired approach to cluster multi-trial EEG data. The idea is to map multi-trial EEG clustering to the coalition formation in a cooperative game, and then identify cluster center (the EEG trial with highest Shapley value) and assign EEG trials into proper clusters based on their cross correlation-transformed Shapley values. We demonstrate the mapped EEG cooperative game is convex, and it leads to an algorithm for multi-trial EEG clustering named CoGEEGc. The CoGEEGc yields high-quality multi-trial EEG clustering with respect to intra-cluster compactness and inter-cluster scatter. We show that CoGEEGc outperforms 15 state-of-the-art EEG or time series clustering approaches through detailed experimentation on real-world multi-trial EEG datasets. Comparison against 15 methods with four theoretical properties of clustering further illustrates the superiority of CoGEEGc, as it satisfies two properties while other approaches only satisfy one. [ABSTRACT FROM AUTHOR]

To securely integrate Mobile Edge Computing (MEC) into Wireless Blockchain Network (WBN), this paper proposes a framework for blockchain based offloading strategy, where blockchain nodes are categorized as blockchain users and blockchain miners from a motivation perspective. Particularly, aiming at improving the motivation ability, a block generation process is first designed for blockchain miners’ short transaction processing time lower bound. Then, to further maximize the utilities of both blockchain users and blockchain miners, an optimization problem is formulated to determine an optimal strategy which involves a trade-off between the fast transaction confirmation rate required by blockchain users and the transaction fees obtained by blockchain miners. A Stackelberg game is introduced to model the interaction between the blockchain users and miners. Meanwhile, a distributed algorithm is designed to converge this strategy in an iterative manner based on the buyer-seller negotiation. Additionally, double-spending attack and selfish mining attack are analysed to examine their impact on the system performance in terms of confirmation delay and throughput while guaranteeing the high security level. Finally, extensive experiments have been conducted to show the rightness and effectiveness of the proposed equilibrium-based strategy and mathematical analysis, and some insights are discussed for the further guide as well. [ABSTRACT FROM AUTHOR]

In this paper, the security problem for a cloud control system (CCS) is studied. In the CCS, so-called advanced persistent threats (APTs) can be launched by a malicious attacker to reduce the quality of service of cloud and deteriorate the system performance further. To defend against APTs and create a security as a service scheme, a defender needs to allocate defense resource to different units serving to different plants for improving the overall system performance when the CCS accommodates multiple physical plants simultaneously. After observing the defender's action, the attacker decides which serve units to comprise. Considering that both defender and attacker are subject to resource constraints, the interaction of two sides is modeled by a Stackelberg game. The optimal solutions for two players under different types of budget constraints are investigated. Simulation examples and comparison results are provided to verify the main results of this paper. [ABSTRACT FROM AUTHOR]

This paper presents a market-based resource adequacy assessment framework to analyze the system generation portfolio that results in a competitive market environment. In particular, we apply the modeling framework to investigate the impact of high penetration levels of variable renewable energy resources and different market designs on achieving resource adequacy. The model captures the strategic capacity investment and retirement decision-making of profit-maximizing generation companies. In contrast to many previous studies, this paper considers markets for capacity, energy, and reserve products in detail; thus, the modeling framework allows improved analysis of generation expansion and revenue sufficiency in a competitive market environment. The modeling framework is based on Stackelberg leader–follower games and is formulated as a bi-level optimization problem, which is then transformed into a mathematical program with equilibrium constraints. Furthermore, we employ a Lagrangian decomposition algorithm to enhance computational performance. In a case study of the electric reliability council of texas (ERCOT) system, we compare the results from the proposed model with those obtained from a traditional centralized least-cost planning model. The results demonstrate the effectiveness of the proposed modeling framework and highlight the importance of considering strategic behavior in a competitive market framework when assessing resource adequacy. [ABSTRACT FROM AUTHOR]

Resource allocation in mmWave small cell is a key issue of the fifth generation (5G) systems. To solve this problem, we first present a hybrid beamforming structure for base station of small cell, which consists of orthogonal frequency division multiplexing (OFDM), identity matrix and switched-beam. Next, we formulate the joint allocation of the beam and transmitted power on the sub-carriers into a mixed integer non-linear programming problem (MINLP). Due to the non-convexity of this problem, we decompose it into two subproblems which are the beam selection and the power allocation. For the beam selection, we propose an algorithm based on cooperative games. For the power allocation, we present an optimal and sub-optimal solution based on Lagrange duality and non-cooperative games respectively. Simulation results show that the beam allocation algorithm proposed in this paper can effectively improve the sum rate of the system, and the optimal power allocation solution is far superior to the sub-optimal one in sum rate. [ABSTRACT FROM AUTHOR]

With the continuing development of affordable immersive virtual reality (VR) systems, there is now a growing market for consumer content. The current form of consumer systems is not dissimilar to the lab-based VR systems of the past 30 years: the primary input mechanism is a head-tracked display and one or two tracked hands with buttons and joysticks on hand-held controllers. Over those 30 years, a very diverse academic literature has emerged that covers design and ergonomics of 3D user interfaces (3DUIs). However, the growing consumer market has engaged a very broad range of creatives that have built a very diverse set of designs. Sometimes these designs adopt findings from the academic literature, but other times they experiment with completely novel or counter-intuitive mechanisms. In this paper and its online adjunct, we report on novel 3DUI design patterns that are interesting from both design and research perspectives: they are highly novel, potentially broadly re-usable and/or suggest interesting avenues for evaluation. The supplemental material, which is a living document, is a crowd-sourced repository of interesting patterns. This paper is a curated snapshot of those patterns that were considered to be the most fruitful for further elaboration. [ABSTRACT FROM AUTHOR]

In order to explore the nature of the human-vehicle game problem and analyze the torque-angle interaction between the two agents, i.e. the driver and advanced driver assistance system (ADAS), a human-vehicle game stability control strategy based on Nash negotiation principle is proposed. First a six-order vehicle dynamic model, a driver neuromuscular (NMS) model, etc., are set up to simulate drivers’ steering characteristics and vehicles’ response to the input of the two agents and external disturbance. Secondly, several significant parameters in NMS model are recognized, and an active rear steering (ARS) controller is designed using the sliding mode variable structure algorithm. Then, the Nash negotiation solution is worked out according to Nash negotiation principle, and a self-tuning method for the weight of ARS controller is put forward employing the fuzzy control theory. Finally simulations are carried out under the standard double-lane change maneuver. The results indicate that the stability control strategy proposed in this paper can effectively solve the game problem and achieve good vehicular stability control performance. [ABSTRACT FROM AUTHOR]

Communication-based Train Control (CBTC) systems are the burgeoning directions for developing future train control systems. With the adoption of wireless communication and network techniques, train control systems are more vulnerable to cyber-attacks. Notably, the jamming attacks, aiming at the handoff process that is the weakest part of train ground communication systems, will cause long disruption of communication. It will have a severe impact on train control operation efficiency. Current research regarding industry control system security is hard to model the impact of the jamming attacks on the train control system quantitatively, and current countermeasure schemes against jamming attacks are not designed for the operating mechanism of train control systems. This paper first builds the train control security state transition probability model under jamming attacks. A cross-layer defense scheme is then proposed from the aspect of the physical layer, the cyber layer and the management layer. In the physical layer, this paper designs a model prediction control algorithm to track dynamic target signals, in the hopes of eventually tracking the dynamic target quickly and smoothly. In the cyber layer, a multi-stage and zero-sum stochastic game model is built for the channel selection for the attack and the defense, whereby the channel selection randomized policy will be obtained. In the management layer, a dynamic train travel speed profile generation algorithm is proposed to mitigate the jamming attacks’ impact on train control systems. Extensive simulation results are shown that jamming attack impact on CBTC can be mitigated effectively with our proposed cross-layer defense scheme. [ABSTRACT FROM AUTHOR]

In task offloading systems, it is imperative to guarantee that an offloaded task is completed within a pre-specified deadline. In this paper, we propose a distributed device-to-device (D2D) offloading system (DDOS) in which a task owner opportunistically broadcasts an offloading request that includes its mobility level and task completion deadline. After receiving the request, mobile devices in the vicinity of the task owner employ a constraint stochastic game to decide, in a distributed manner, whether to accept the request or not. We devise a best response dynamics-based algorithm (BRDA) to obtain a multi-policy constrained Nash equilibrium. Evaluation results demonstrate that DDOS can guarantee a high on-time task completion probability, as well as a low energy consumption. [ABSTRACT FROM AUTHOR]

In a typical new product development (NPD) project, there are many alternatives, ranging from incremental changes or re-engineering of an existing product in order to compete better in an existing market, to breakthrough thinking and the development of a new product using new technologies that satisfy the needs and expectations of customers in new markets. We tested the hypothesis that competition influences the decisions of entrepreneurs managing NPD projects. The findings presented in this paper are based on a controlled experiment conducted in several project management courses using a simulation game. The results reveal that a competitive environment in which participants compete for the same customers tends to impact the planning of NPD projects. The paper contributes to the managerial practice by offering integration of “What if?” analysis in the planning phase of NPD projects. It can assist entrepreneurs to evaluate the tradeoffs of the feasible alternatives in a noncompetitive environment, and explain how market conditions, such as competitor entry behavior, affect the project plan. A decision on the best project plan, among all the feasible project plans, is based on management strategy that takes into account the level of competition along with the uncertainty in the technology, market, and environment. The game, combined with a simulation-based training tool, used in this study, offer an opportunity to develop and to try different alternatives for the management of NPD projects without risking the consequences of project failure. [ABSTRACT FROM AUTHOR]

Based on the evolutionary game theory, this paper explores the evolutionary stable strategy (ESS) of university–industry collaborative innovation. By utilizing MATLAB software, the analysis of the influence of agent coupling degree, group size, and government policies on the stable evolution of university–industry collaborative innovation is taken into account, while simulating. Our findings indicate that, first, agent coupling degree is positively and nonlinearly related to the stable evolution of university–industry collaborative innovation; second, when an enterprise's size is equal to university's, group size is more significant to the stable evolution of university–industry collaborative innovation than agent coupling degree; third, when a group size of an enterprise and university is different, the combinations of agent coupling degree and group size have various effects on the stable evolution of enterprise group and university group; and fourth, government policies have a significantly positive impact on the stable evolution of university–industry collaborative innovation, and incentive measures are more effective than punitive ones. Research conclusions have developed the relationship among university–industry collaborative innovation agents, expanded the boundary of the stability study of collaborative innovation, and provided new ideas for improving the effectiveness of government innovation policies. [ABSTRACT FROM AUTHOR]

Rail transportation, especially, high-speed rails (HSR), is an important infrastructure for the development of national economy and the promotion of passenger experience. Due to the large bandwidth, millimeter wave (mmWave) communication is regarded as a promising technology to meet the demand of high data rates. However, since mmWave communication has the characteristic of high attenuation, mobile relay (MR) is considered in this paper. Also, full-duplex (FD) communications have been proposed to improve the spectral efficiency. However, because of the high speed, as well as the problem of penetration loss, passengers on the train have a poor quality of service. Consequently, an effective user association scheme for HSR in mmWave band is necessary. In this paper, we investigate the user association optimization problem in mmWave mobile relay systems where the MRs operate in the FD mode. To maximize the system capacity, we propose a cooperative user association approach based on coalition formation game, and develop a coalition formation algorithm to solve the challenging NP-hard problem. We also prove the convergence and Nash-stable property of the proposed algorithm. Extensive simulations are done to show the system performance of the proposed scheme under various network settings. It is demonstrated that the proposed distributed low complexity scheme achieves a near-optimal performance and outperforms two baseline schemes in terms of average system throughput. [ABSTRACT FROM AUTHOR]

Thermostatically controlled loads (TCLs) are regarded as one of the promising resources for suppressing power fluctuations due to renewable energy (RENs). However, due to the great burdens of fully considering each users’ characteristics, it is difficult to achieve unity of the individual optimal decisions and global optimum in demand-side management (DSM). This paper proposes a game-theoretic DSM that can optimize the global power consumption schedule by individual TCL user's optimization. By integrating the prediction of REN outputs into the pricing mechanism, the proposed DSM can guide the users to make their best power consumption schedules along with intermittent REN generations, thus to smooth the tie-line power of microgrids. In this paper, a novel pricing mechanism is firstly developed based on the concave N-person game theory, which is more adaptive and flexible compared with existing game-theoretic DSM. Then, an individual's power consumption optimization and its simplified model are developed considering the constraints of the TCL model and personal preferences. The simplified model can be easily solved and achieve a fast solution in the power consumption game. An implementation framework of the proposed DSM is further developed for practical application. Finally, numerical studies verify the effectiveness of the proposed models and methods. [ABSTRACT FROM AUTHOR]

Even though digital learning games have become common in education, relatively little is known about the usefulness of game-based assessment. This paper aims to explore if a game-based math test can provide added value to math education with respect to cognitive and affective outcomes. We used in-game measures, embedded in the game called Semideus Exam, focusing on conceptual fraction knowledge. In order to validate the game-based assessment approach, we compared the cognitive outcomes of 51 Finnish sixth graders, who completed both paper-based and game-based math tests in a randomized order. In addition, the students’ test anxiety and flow experience were measured to evaluate the affective outcomes. The results indicate that the game-based test scores correlated significantly with the paper-based test scores suggesting that the game-based assessment was successfully implemented and the game provided comparable data with the paper-based test approach. More importantly, the results revealed that game-based assessment lowered test anxiety and increased engagement which is likely to decrease assessment bias caused by test anxiety. In addition, the results show that earlier playing experience and gender did not influence the game-based test score suggesting fairness of the game-based assessment approach. Although we identified several benefits of the game-based assessment approach, more evidence is needed on the usefulness and fairness of game-based assessments. [ABSTRACT FROM AUTHOR]

In face of the explosive service demands, solving the problem of spectrum scarcity is becoming more important than ever. To utilize the spectrum resources more thoroughly and efficiently, virtualization technologies have been proposed, which can be a means to mitigating resource granularity and increasing efficiency in heterogeneous network environments. In this paper, a traffic-oriented resource virtualization with demand-supply dynamic analysis is proposed for optimized resource allocation of heterogeneous networks with multiple types of services. On the supply side, i.e., the network side, a low-complexity matching game approach is introduced with the novel “Match-Degree” conception, which could be defined with the Grey relational analysis. The complexity of generating preference list can be reduced by unifying various dimensions of network parameters. On the demand side, i.e., the user side, bandwidth allocation algorithm is designed to consider the comprehensive network traffic characteristics, energy consumption, and network price factors, to maximize the overall utility. Except from theoretic analysis, simulation has also been employed to compare the proposed scheme with prior and traditional ones. To further verify the practicability, tractability, and effectiveness of the proposed demand-supply scheme, a test bed is designed and developed in this paper. [ABSTRACT FROM PUBLISHER]

Mobile edge computing (MEC), as an emerging and prospective mobile computing paradigm, allows a content provider to serve its users by allocating their mobile devices to nearby edge servers to lower the latency in the delivery of its content to those mobile services. From the content provider's perspective, a cost-effective mobile device allocation (MDA) aims to allocate maximum mobile devices to minimum edge servers. However, the allocation of excessive mobile devices to an edge server may result in severe communication interference and consequently, impact mobile devices’ data rates. Sometimes, not all the mobile devices can be allocated to edge servers and thus have to retrieve content from the remote cloud through base stations with high latency. The connection between these mobile devices and base stations also incur communication interference. In this paper, we formally model this Interference-aware mobile edge device allocation (I-MEDA) problem, and propose a game-theoretic based approach named I-MEDAGame to formulate the I-MEDA problem as an I-MEDA game. In the I-MEDA game, allocation decisions are made for individual mobile devices in parallel to alleviate the need for centralized optimization. Our theoretical analysis of I-MEDAGame shows that it admits at least one Nash equilibrium. To solve the I-MEDA problem, I-MEDAGame employs a novel decentralized algorithm to find the Nash equilibrium of the IMEDA game. The performance of I-MEDAGame is theoretically analyzed and experimentally evaluated. The results show that I-MEDAGame can solve the I-MEDA problem effectively and efficiently, outperforming four representative approaches significantly. [ABSTRACT FROM AUTHOR]

This paper proposes a semi-analytical method to obtain the equilibrium bidding strategies for generation and demand units in a combined oligopoly and oligopsony wholesale electricity market. Such market structure is the outcome of the increasing deployment of demand response programs that facilitate active participation of demand-side players in the price-setting process. In this analysis, the concept of supply function equilibrium (SFE) is used to investigate the oligopolistic competition among generation units. The SFE model is extended and the demand function equilibrium (DFE) is obtained to study the oligopsonistic competition among demand units. The economic behavior of a market participant is formulated as a bi-level programming (BLP) problem. The imperfect competition among generation units, as well as among demand units, are modeled as a non-cooperative game. Next, a direct method is developed to calculate all candidate equilibriums of the market, and the locational marginal prices (LMPs) in terms of the bidding strategies of the market participants. The BLP problem is solved by obtaining the coordinated Pareto-dominant Nash equilibrium of the market participants’ non-cooperative games. Finally, the proposed analysis is examined in case studies. Accordingly, we report insightful observations with respect to the impact of the changes in the new market structure, at firm-level and market-level, such as in terms of mitigating market power of generation units, the market clearing prices and quantities, surplus for generation units and demand units, and potential impact on market efficiency. [ABSTRACT FROM AUTHOR]

The ongoing digitalization enables stable control processes and smooth operations of Industrial Control Systems (ICSs). A direct consequence of the highly interconnected architecture of ICSs is the introduced cyber vulnerability and increasing cyber security threats to ICSs. Numerous researches pay attention to the security problem of ICSs. However, most current studies face two challenges. First, the interaction problem between the cyber layer and the physical layer of ICSs may result in incorrect attack response strategies. Second, ICSs are real-time systems, but existing defense decision algorithms based on game theory or reinforcement learning techniques have high computational complexity, which prevents them from making decisions quickly. In this paper, we design a new multi-attribute based method for quantifying rewards and propose a multi-attribute based Q-learning algorithm to resolve the interaction problem. In addition, to overcome the limitation of slow convergence, we develop an effective parallel Q-learning (PQL) algorithm to quickly find the optimal strategy. The experimental results show the effectiveness of the PQL algorithm. Compared with the Q-learning algorithm (QL) and the deep Q-network (DQN) algorithm, our proposed solution can reduce the average completion time by 12.5 to 37 percent. [ABSTRACT FROM AUTHOR]

Despite its advantages of flexility and low-cost networking, unmanned aerial vehicle (UAV) communications face various attacks such as eavesdropping. Existing studies on secure UAV communications assume fixed-location eavesdroppers and rarely consider interactions between legitimate nodes and eavesdroppers. In this paper, we investigate eavesdropping and anti-eavesdropping interaction between a UAV-enabled eavesdropper (UAV-E) and a UAV-enabled base station (UAV-BS) in a downlink wiretap system. The UAV-E aims to wiretap downlink signals by adaptively adjusting its trajectory while the UAV-BS aims to maximize secrecy-sum-rate with minimum power consumption by jointly optimizing user scheduling, power control, and trajectory. Dynamic differential equations are formulated to characterize motions of UAVs, following which a zero-sum differential game is formulated to model the “pursuit-evasion” interaction between the UAV-BS and the UAV-E. Definition and existence of Nash equilibrium (NE) are provided. To obtain the NE, Pontryagins minimum principle is leveraged to solve the trajectory design problem. Further, Gauss-Seidel-like implicit finite-difference method is leveraged to obtain saddle-point strategies at NE. Finally, numerical results are provided to verify the effectiveness of the proposed game model. It is revealed that the differential game can well-characterize the strategy interactions between UAVs. Moreover, results show that the initial positions and weights of UAVs, the energy consumption factor, and the user scheduling have key impacts on motion interactions between the UAV-BS and the UAV-E and further on UAV-BS’s power control. [ABSTRACT FROM AUTHOR]

Wireless multiplayer interactive virtual reality (VR) game has the high computing workload of VR and unpredictable interaction among players, which brings severe challenges to the design of wireless communication systems. In this paper, we propose a wireless multiplayer interactive VR game transmission framework based on mobile edge computing (MEC) that is able to model the interaction among players and compute the post-processing procedures at the MEC server or the mobile VR device. In the framework, the absolute delay of each player is used to avoid VR vertigo and the inter-player delay among players is used to model the fairness of the interactive game. Aiming to minimize the average inter-player delay, we optimize the computing resource allocation of the MEC server, the wireless bandwidth allocation and the post-processing decision policy subject to the constraints of the absolute delay requirements, the local energy limits of players, the total bandwidth limit and the computing resources limit. To tackle the non-convex problem efficiently, we design an iterative algorithm based on the NESTT-G algorithm which iteratively optimizes the truncated first-order Taylor approximation of the objective. Numerical results demonstrate the proposed algorithm can reduce the average inter-player delay significantly with lower complexity, and also reveal the impact of different parameters and the channel state conditions on the post-processing decision and edge resource allocation. [ABSTRACT FROM AUTHOR]

Vehicular networks which are paralyzed by natural disasters is faced with communication dilemma. Through building a decentralized communication network, unmanned aerial vehicles (UAVs) with high mobility and flexibility are expected to be the solution to the post-disaster vehicular networks. The blockchain technology has been widely used in UAV networks to provision the prompt security of distributed communications. However, existing works ignore the dynamics of the network in that due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blockchain transactions. The delay of synchronization can result in severe security issues. On addressing the issues, this paper proposes UAVs-aided blockchain offline transactions to ensure the security and effectiveness of delay-tolerant blockchain transactions when UAVs are offline. In specific, we consider vehicle-to-vehicle (V2V) charging transactions in post-disaster vehicular networks. By establishing offline channel between charging and discharging electric vehicles (EVs) by hashed time locked contract (HTLC), we design a UAVs aided penalty algorithm with accountable assertions to prevent deposit forging attacks and double-spending attacks. In addition, considering the selfishness of EVs, a Stackelberg game based incentive scheme is developed to encourage EVs to participate the offline transactions and to improve their trust values. By suing the mechanisms above, our proposal addresses the security of offline EV charging, as well as the selfishness of participant. Using extensive simulations, we demonstrate that the proposed scheme can realize secure transactions among EVs and can effectively improve the utilities of EVs through the comparison with conventional schemes. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious social problem in modern society, while vehicle merging is one of the main causes of congestion since human driver has to decelerate to avoid collision when merging. Connected and automated vehicles (CAVs) can realize the coordinated merging which will improve the traffic efficiency, reduce the fuel consumption and improve the driving comfort. In this paper, a cooperative merging strategy for CAVs is proposed based on cooperative game theory with transferable utility (TU) and optimal control. By introducing the value of time (VOT), an economic payoff function including time benefit, fuel consumption and driving comfort is constructed. The cooperative game determines the merging sequence (MS) of vehicles and the side payment among them, and can achieve a win-win situation, while the coordinated merging trajectory of each vehicle is computed based on optimal control. The effectiveness and feasibility of the proposed framework are verified though simulation in different cases, and the comparison of simulation results indicates that the proposed framework can improve the economic benefit of vehicles and have the potential for practical application. [ABSTRACT FROM AUTHOR]

How can we model affect in a general fashion, across dissimilar tasks, and to which degree are such general representations of affect even possible? To address such questions and enable research towards general affective computing, this paper introduces The Arousal video Game AnnotatIoN (AGAIN) dataset. AGAIN is a large-scale affective corpus that features over 1,100 in-game videos (with corresponding gameplay data) from nine different games, which are annotated for arousal from 124 participants in a first-person continuous fashion. Even though AGAIN is created for the purpose of investigating the generality of affective computing across dissimilar tasks, affect modelling can be studied within each of its 9 specific interactive games. To the best of our knowledge AGAIN is the largest—over 37 hours of annotated video and game logs—and most diverse publicly available affective dataset based on games as interactive affect elicitors. [ABSTRACT FROM AUTHOR]