Streaming systems that can maintain Quality of Experience (QoE) for users have attracted much attention because they can be applied in various fields, such as emergency response training and medical surgery. Dynamic Adaptive Streaming over HTTP (DASH) is a typical protocol for streaming system. In order to improve QoE in DASH, a multi-server system has been presented by pseudo-increasing bandwidth through multiple servers. This multi-server system is designed to share streaming content efficiently in addition to having redundant server resources for each streaming content, which is excellent for fault tolerance. Assigning DASH server to users in these multi-servers environment is important to maintain QoE, thus a method of server assignment of users (user allocation method) for multi-servers is presented by using cooperative game theory. However, this conventional user allocation method does not take into account the size of the server bandwidth, thus users are concentrated on a particular server at the start of playback. Although the average required bit rate of video usually fluctuates, bit rate fluctuations are not taken into account. These phenomena may decrease QoE. In this paper, we propose a novel user allocation method using coalition structure generation in cooperative game theory to improve the QoE of all users in an immediate and stable manner in DASH environment. Our proposed method can avoid user concentration, since the bandwidth used by the overall system is taken into account. Moreover, our proposed method can be performed every time the average required bit rate changes. We demonstrate the effectiveness of our method through simulations using Network Simulator 3 (NS3).

Synthesis problems on multiplayer non-zero-sum games (MG) with multiple environment players that behave rationally are the problems to find a good strategy of the system and have been extensively studied. This paper concerns the synthesis problems on stochastic MG (SMG), where a special controller other than players, called nature, which chooses a move in its turn randomly, may exist. Two types of synthesis problems on SMG exist: cooperative rational synthesis problem (CRSP) and non-cooperative rational synthesis problem (NCRSP). The rationality of environment players is modeled by Nash equilibria, and CRSP is the problem to decide whether there exists a Nash equilibrium that gives the system a payoff not less than a given threshold. Ummels et al. studied the complexity of CRSP for various classes of objectives and strategies of players. CRSP fits the situation where the system can make a suggestion of a strategy profile (a tuple of strategies of all players) to the environment players. However, in real applications, the system may rarely have an opportunity to make suggestions to the environment, and thus CRSP is optimistic. NCRSP is the problem to decide whether there exists a strategy σ0 of the system satisfying that for every strategy profile of the environment players that forms a 0-fixed Nash equilibrium (a Nash equilibrium where the system's strategy is fixed to σ0), the system obtains a payoff not less than a given threshold. In this paper, we investigate the complexity of NCRSP for positional (i.e. pure memoryless) strategies. We consider ω-regular objectives as the model of players' objectives, and show the complexity results of the problem for several subclasses of ω-regular objectives. In particular, the problem for terminal reachability (TR) objectives is shown to be Σp2-complete.

In this paper, we investigate a wireless-powered relays assisted batteryless IoT network based on the non-linear energy harvesting model, where there exists an energy service provider constituted by the hybrid access point (HAP) and an IoT service provider constituted by multiple clusters. The HAP provides energy signals to the batteryless devices for information backscattering and the wireless-powered relays for energy harvesting. The relays are deployed to assist the batteryless devices with the information transmission to the HAP by using the harvested energy. To model the energy interactions between the energy service provider and IoT service provider, we propose a Stackelberg game based framework. We aim to maximize the respective utility values of the two providers. Since the utility maximization problem of the IoT service provider is non-convex, we employ the fractional programming theory and propose a block coordinate descent (BCD) based algorithm with successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques to solve it. Numerical simulation results confirm that compared to the benchmark schemes, our proposed scheme can achieve larger utility values for both the energy service provider and IoT service provider.

Calculation is a solitaire card game with a standard 52-card deck. Initially, cards A, 2, 3, and 4 of any suit are laid out as four foundations. The remaining 48 cards are piled up as the stock, and there are four empty tableau piles. The purpose of the game is to move all cards of the stock to foundations. The foundation starting with A is to be built up in sequence from an ace to a king. The other foundations are similarly built up, but by twos, threes, and fours from 2, 3, and 4 until a king is reached. Here, a card of rank i may be used as a card of rank i + 13j for j ∈ {0, 1, 2, 3}. During the game, the player moves (i) the top card of the stock either onto a foundation or to the top of a tableau pile, or (ii) the top card of a tableau pile onto a foundation. We prove that the generalized version of Calculation Solitaire is NP-complete.

One of the long-standing research problems on logic programming is to treat the cut predicate in a logical, high-level way. We argue that this problem can be solved by adopting linear logic and choice-disjunctive goal formulas of the form G0 ⊕ G1 where G0, G1 are goals. These goals have the following intended semantics: choose the true disjunct Gi and execute Gi where i (= 0 or 1), while discarding the unchosen disjunct. Note that only one goal can remain alive during execution. These goals thus allow us to specify mutually exclusive tasks in a high-level way. Note that there is another use of cut which is for breaking out of failure-driven loops and efficient heap management. Unfortunately, it is not possible to replace cut of this kind with use of choice-disjunctive goals.

This study focuses on creating arithmetical stories as a sub-task of problem posing and proposes a game named “Tri-prop scrabble” as a learning environment based on a fusion method of learning and game. The problem-posing ability has a positive relationship with mathematics achievement and understanding the mathematical structure of problems. In the proposed game, learners are expected to experience creating and concatenating various arithmetical stories by integrating simple sentences. The result of a preliminary feasibility study shows that the participants were able to pose and concatenate a variety of types of arithmetic stories and accept this game is helpful for learning arithmetic word problems.

To the best of our knowledge, there have been very few work on computational algorithms for the core or its variants in MC-nets games. One exception is the work by [Hirayama, et.al., 2014], where a constraint generation algorithm has been proposed to compute a payoff vector belonging to the least core. In this paper, we generalize this algorithm into the one for finding a payoff vector belonging to ϵ-core with pre-specified bound guarantee. The underlying idea behind this algorithm is basically the same as the previous one, but one key contribution is to give a clearer view on the pricing problem leading to the development of our new general algorithm. We showed that this new algorithm was correct and never be trapped in an infinite loop. Furthermore, we empirically demonstrated that this algorithm really presented a trade-off between solution quality and computational costs on some benchmark instances.

With the popularity of smart devices, mobile crowdsensing, in which the crowdsensing platform gathers useful data from users of smart devices, e.g., smartphones, has become a prevalent paradigm. Various incentive mechanisms have been extensively adopted for the crowdsensing platform to incentivize users of smart devices to offer sensing data. Existing works have concentrated on rewarding smart-device users for their short term effort to provide data without considering the long-term factors of smart-device users and the quality of data. Our previous work has considered the quality of data of smart-device users by incorporating the long-term reputation of smart-device users. However, our previous work only considered a quality maximization problem with budget constraints on one location. In this paper, multiple locations are considered. Stackelberg game is utilized to solve a two-stage optimization problem. In the first stage, the crowdsensing platform allocates the budget to different locations and sets price as incentives for users to maximize the total data quality. In the second stage, the users make efforts to provide data to maximize its utility. Extensive numerical simulations are conducted to evaluate proposed algorithm.

This paper investigates the charging control strategy design problem of a large-scale plug-in electric vehicle (PEV) group, where each PEV aims to find an optimal charging strategy to minimize its own cost function. It should be noted that the collective behavior of the group is coupled in the individual cost function, which complicates the design of decentralized charging strategies. To obtain the decentralized charging strategy, a mean-field game (MFG) formulation is proposed where a penalty on collective consensus is embedded and a class of mean-field coupled time-varying stochastic systems is targeted for solving the MFG which involves the charging model of PEVs as a special case. Then, an augmented system with dimension extension and the policy iteration algorithm are proposed to solve the mean-field game problem for the class of mean-field coupled time-varying stochastic systems. Moreover, analysis of the convergence of proposed approach has been studied. Last, simulation is conducted to illustrate the effectiveness of the proposed MFG-based charging control strategy and shows that the charging control strategy can achieve desired mean-field state and impact to the power grid can be buffered.

To maintain blockchain-based services with ensuring its security, it is an important issue how to decide a mining reward so that the number of miners participating in the mining increases. We propose a dynamical model of decision-making for miners using an evolutionary game approach and analyze the stability of equilibrium points of the proposed model. The proposed model is described by the 1st-order differential equation. So, it is simple but its theoretical analysis gives an insight into the characteristics of the decision-making. Through the analysis of the equilibrium points, we show the transcritical bifurcations and hysteresis phenomena of the equilibrium points. We also design a controller that determines the mining reward based on the number of participating miners to stabilize the state where all miners participate in the mining. Numerical simulation shows that there is a trade-off in the choice of the design parameters.

We study the computational complexity of the puzzle game Critter Crunch, where the player has to rearrange Critters on a board in order to eliminate them all. Smaller Critters can be fed to larger Critters, and Critters will explode if they eat too much. Critters come in several different types, sizes, and colors. We prove the NP-hardness of levels that contain Blocker Critters, as well as levels where the player must clear the board in a given number of moves (i.e., “puzzle mode”). We also characterize the complexity of the game, as a function of the number of columns on the board, in two settings: (i) the setting where Critters may have several different colors, but only two possible sizes, and (ii) the setting where Critters come in all three sizes, but with no color variations. In both settings, the game is NP-hard for levels with exactly two columns, and solvable in linear time for levels with only one column or more than two columns.

Sharding is a solution to the blockchain scalability problem. A sharded blockchain divides consensus nodes (validators) into groups called shards and processes transactions separately to improve throughput and latency. In this paper, we analyze the rational behavior of users in account/balance model-based sharded blockchains and identify a phenomenon in which accounts (users' wallets and smart contracts) eventually get concentrated in a few shards, making shard loads unfair. This phenomenon leads to bad user experiences, such as delays in transaction inclusions and increased transaction fees. To solve this problem, we propose two load balancing methods in account/balance model-based sharded blockchains. Both methods perform load balancing by periodically reassigning accounts: in the first method, the blockchain protocol itself performs load balancing and in the second method, wallets perform load balancing. We discuss the pros and cons of the two protocols, and apply the protocols to the execution sharding in Ethereum 2.0, an existing sharding design. Further, we analyze by simulation how the protocols behave to confirm that we can observe smaller transaction delays and fees. As a result, we released the simulation program as “Shargri-La,” a simulator designed for general-purpose user behavior analysis on the execution sharding in Ethereum 2.0.

Visual script languages with a node-based interface have commonly been used in the video game industry. We examined the bug database obtained in the development of FINAL FANTASY XV (FFXV), and noticed that several types of bugs were caused by simple mis-descriptions of visual scripts and could therefore be mechanically detected. We propose a method for the automatic verification of visual scripts in order to improve productivity of video game development. Our method can automatically detect those bugs by using symbolic model checking. We show a translation algorithm which can automatically convert a visual script to an input model for NuSMV that is an implementation of symbolic model checking. For a preliminary evaluation, we applied our method to visual scripts used in the production for FFXV. The evaluation results demonstrate that our method can detect bugs of scripts and works well in a reasonable time.

In this paper, we propose a hierarchical Stackelberg game based resource allocation algorithm (HGRAA) to jointly allocate the wireless and computational resources of a mobile edge computing (MEC) system. The proposed HGRAA is composed of two levels: the lower-level evolutionary game (LEG) minimizes the cost of mobile terminals (MTs), and the upper-level exact potential game (UEPG) maximizes the utility of MEC servers. At the lower-level, the MTs are divided into delay-sensitive MTs (DSMTs) and non-delay-sensitive MTs (NDSMTs) according to their different quality of service (QoS) requirements. The competition among DSMTs and NDSMTs in different service areas to share the limited available wireless and computational resources is formulated as a dynamic evolutionary game. The dynamic replicator is applied to obtain the evolutionary equilibrium so as to minimize the costs imposed on MTs. At the upper level, the exact potential game is formulated to solve the resource sharing problem among MEC servers and the resource sharing problem is transferred to nonlinear complementarity. The existence of Nash equilibrium (NE) is proved and is obtained through the Karush-Kuhn-Tucker (KKT) condition. Simulations illustrate that substantial performance improvements such as average utility and the resource utilization of MEC servers can be achieved by applying the proposed HGRAA. Moreover, the cost of MTs is significantly lower than other existing algorithms with the increasing size of input data, and the QoS requirements of different kinds of MTs are well guaranteed in terms of average delay and transmission data rate.

DAIHINMIN, which means Grand Pauper, is a popular playing-card game in Japan. TANHINMIN is a simplified variant of DAIHINMIN, which was proposed by Nishino in 2007 in order to investigate the mathematical properties of DAIHINMIN. In this paper, we consider a 2-player generalized TANHINMIN, where the deck size is arbitrary n. We present a linear-time algorithm that determines which player has a winning strategy after all cards are distributed to the players.

In the context of Web 2.0, the interaction between users and resources is more and more frequent in the process of resource sharing and consumption. However, the current research on resource pricing mainly focuses on the attributes of the resource itself, and does not weigh the interests of the resource sharing participants. In order to deal with these problems, the pricing mechanism of resource-user interaction evaluation based on multi-agent game theory is established in this paper. Moreover, the user similarity, the evaluation bias based on link analysis and punishment of academic group cheating are also included in the model. Based on the data of 181 scholars and 509 articles from the Wanfang database, this paper conducts 5483 pricing experiments for 13 months, and the results show that this model is more effective than other pricing models - the pricing accuracy of resource resources is 94.2%, and the accuracy of user value evaluation is 96.4%. Besides, this model can intuitively show the relationship within users and within resources. The case study also exhibits that the user's knowledge level is not positively correlated with his or her authority. Discovering and punishing academic group cheating is conducive to objectively evaluating researchers and resources. The pricing mechanism of scientific and technological resources and the users proposed in this paper is the premise of fair trade of scientific and technological resources.

In this letter, we study an interference scenario under a smart interferer which observes color channels and interferes with a visible light communication (VLC) network. We formulate the smart interference problem based on a Stackelberg game and propose an optimal response algorithm to overcome the interference by optimizing transmit power and sub-color channel allocation. The proposed optimization algorithm is composed with Lagrangian dual decomposition and non-linear fractional programming to have stability to get optimum points. Numerical results show that the utility by the proposed algorithm is increased over that of the algorithm based on the Nash game and the baseline schemes.

Cognitive radio network (CRN) provides an effective way of improving efficiency and flexibility in spectrum usage. Due to the coexistence of secondary user (SU) and primary user (PU), managing interference is a critical issue to be addressed if we are to reap the full benefits. In this paper, we consider the problem of joint interference management and resource allocation in a multi-channel ad hoc CRN. We formulate the problem as an overlapping coalition formation game to maximize the sum rate of SU links while guaranteeing the quality of service (QoS) of PU links. In the game, each SU link can make an autonomous decision and is allowed to participate in one or more cooperative coalitions simultaneously to maximize its payoff. To obtain the solution of the formulated game, a distributed, self-organizing algorithm is proposed for performing coalition formation. We analyze the properties of the algorithm and show that SU links can cooperate to reach a final stable coalition structure. Compared with existing approaches, the proposed scheme achieves appreciable performance improvement in terms of the sum rate of SU links, which is demonstrated by simulation results.

To tackle the distributed power optimization problems in wireless sensor networks localization systems, we model the problem as a hierarchical game, i.e. a multi-leader multi-follower Stackelberg game. Existing researches focus on the power allocation of anchor nodes for ranging signals or the power management of agent nodes for cooperative localization, individually. However, the power optimizations for different nodes are indiscerptible due to the common objective of localization accuracy. So it is a new challenging task when the power allocation strategies are considered for anchor and agent nodes simultaneously. To cope with this problem, a hierarchical game is proposed where anchor nodes are modeled as leaders and agent nodes are modeled as followers. Then, we prove that games of leaders and followers are both potential games, which guarantees the Nash equilibrium (NE) of each game. Moreover, the existence of Stackelberg equilibrium (SE) is proved and achieved by the best response dynamics. Simulation results demonstrate that the proposed algorithm can have better localization accuracy compared with the decomposed algorithm and uniform strategy.

Reinforcement learning methods achieve performance superior to humans in a wide range of complex tasks and uncertain environments. However, high performance is not the sole metric for practical use such as in a game AI or autonomous driving. A highly efficient agent performs greedily and selfishly, and is thus inconvenient for surrounding users, hence a demand for human-like agents. Imitation learning reproduces the behavior of a human expert and builds a human-like agent. However, its performance is limited to the expert's. In this study, we propose a training scheme to construct a human-like and efficient agent via mixing reinforcement and imitation learning for discrete and continuous action space problems. The proposed hybrid agent achieves a higher performance than a strict imitation learning agent and exhibits more human-like behavior, which is measured via a human sensitivity test.