In this paper, we focus on the adaptive resource allocation issue for uplink OFDMA systems. The resources are allocated according to a proportional fairness criterion, which can strike an alterable balance between fairness and efficiency. Optimization theory is used to analyze the multi-constraint resource allocation problem and some heuristic characteristics about the optimal solution are obtained. To deal with the cohesiveness of the necessary conditions, we resort to bargaining theory that has been deeply investigated in game theory. Firstly, we summarize some assumptions about bargaining theory and show their similarities with the resource allocation process. Then we propose a priority-ranked bargaining model, whose primary contribution is applying the economic thought to the resource allocation process. A priority-ranked bargaining algorithm (PRBA) is subsequently proposed to permit the base station to auction the subcarriers one by one according to the users' current priority. By adjusting the predefined rate ratio flexibly, PRBA can achieve different degrees of fairness among the users' capacity. Simulation results show that PRBA can achieve similar performance of the max-min scheme and the NBS scheme in the case of appropriate predefined rate ratio.

The first part of this paper contains an introduction to Bell inequalities and Tsirelson's theorem for the non-specialist. The next part gives an explicit optimum construction for the "hard" part of Tsirelson's theorem. In the final part we describe how upper bounds on the maximal quantum violation of Bell inequalities can be obtained by an extension of Tsirelson's theorem, and survey very recent results on how exact bounds may be obtained by solving an infinite series of semidefinite programs.

Quantum random access coding (QRAC) is one of the basic tools in quantum computing. It uses a quantum state for encoding the sender's bit string so that the receiver can recover any single bit of the bit string with high probability. This article surveys recent developments of QRAC, with some concrete examples of QRAC using one quantum bit, and its applications, focusing on communication complexity and locally decodable codes.

Equipping wireless routers with multiple radios further improves the capacity by transmitting over multiple radios simultaneously using orthogonal channels. Efficient channel assignment schemes can greatly alleviate the interference effect of nearby transmissions. One of the distinctive features in wireless multi-hop networks is the lack of any central controller, in which each node makes its own decisions. Therefore, fully cooperative behaviors, such as cooperation for increasing link capacity, alleviating interferences for one another, might not be directly applied. In this paper, we aim to present some applications to show how such a framework can be invoked to design efficient channel assignment algorithms in a non-cooperative, topology-blind environment as well as in environments where the competing players share perfect information about channel usage and topology environment and so on. Simulation results are presented to illustrate the effectiveness of the algorithms.

This paper discusses the infinite horizon static output feedback stochastic Nash games involving state-dependent noise in weakly coupled large-scale systems. In order to construct the strategy, the conditions for the existence of equilibria have been derived from the solutions of the sets of cross-coupled stochastic algebraic Riccati equations (CSAREs). After establishing the asymptotic structure along with the positive semidefiniteness for the solutions of CSAREs, recursive algorithm for solving CSAREs is derived. As a result, it is shown that the proposed algorithm attains the reduced-order computations and the reduction of the CPU time. As another important contribution, the uniqueness of the strategy set is proved for the sufficiently small parameter ε. Finally, in order to demonstrate the efficiency of the proposed algorithm, numerical example is given.

Quantifying user satisfaction is essential, because the results can help service providers deliver better services. In this work, we propose a generalizable methodology, based on survival analysis, to quantify user satisfaction in terms of session times, i.e., the length of time users stay with an application. Unlike subjective human surveys, our methodology is based solely on passive measurement, which is more cost-efficient and better able to capture subconscious reactions. Furthermore, by using session times, rather than a specific performance indicator, such as the level of distortion of voice signals, the effects of other factors like loudness and sidetone, can also be captured by the developed models. Like survival analysis, our methodology is characterized by low complexity and a simple model-developing process. The feasibility of our methodology is demonstrated through case studies of ShenZhou Online, a commercial MMORPG in Taiwan, and the most prevalent VoIP application in the world, namely Skype. Through the model development process, we can also identify the most significant performance factors and their impacts on user satisfaction and discuss how they can be exploited to improve user experience and optimize resource allocation.

Wireless content sharing where peers share content and services via wireless access networks requires user contributions, as in fixed P2P content sharing. However, in wireless access environments, since the resources of mobile terminals are strictly limited, mobile users are not as likely to contribute as ones in fixed environments. Therefore, incentives to encourage user contributions are more significant in wireless access environments. Although an incentive service differentiation architecture where the content transfer rate is adjusted according to the contributions of each downloading user has been already proposed for fixed P2P, it may not work well in wireless access environments because several factors effect wireless throughput. In this paper, we propose a novel architecture for contribution-based transfer-rate differentiation using wireless quality of service (QoS) techniques that motivates users to contribute their resources for wireless content sharing. We also propose a radio resource assignment method for our architecture. Computer simulations and game-theoretic calculations validate our architecture.

While efficient use of network resources is an important control objective of call admission control (CAC), the issue of fairness among services should also be taken into account. Game theory provides a suitable framework for formulating such fair and efficient CAC problem. Thus, in this paper, a game theoretic framework for selecting fair-efficient threshold parameters of CAC for the asymmetrical traffic case in CDMA mobile multimedia systems is proposed. For the cooperative game, the arbitration schemes for the interpersonal comparisons of utility and the bargaining problem, including the Nash, Raiffa, and modified Thomson solutions, are investigated. Furthermore, since CAC should be simple and flexible to provide a fast response to diverse QoS call requests during a connection setup, this paper also applies the concept of load factor to the previous Jeon and Jeong's CAC scheme and proposes an approximation approach to reduce the computational complexity (proposed throughput-based CAC scheme). From the numerical results, the proposed throughput-based CAC scheme shows a comparable performance to the previous Jeon and Jeong's CAC scheme while achieving lower computational complexity. All the solutions attain the fairness by satisfying their different fairness senses and efficiency by the Pareto optimality.

As a result of the growth of sensor-enabled mobile devices, in recent years, users can utilize diverse digital contents everywhere and anytime. However, the interfaces of mobile applications are often unnatural due to limited computational capability, miniaturized input/output controls, and so on. To complement the poor user interface (UI) and fully utilize mobility as feature of mobile devices, we explore possibilities for a new UI of mobile devices. This paper describes the method for recognizing and analyzing a user's continuous action including the user's various gestures and postures. The application example we created is mobile game called AM-Fishing game on mobile devices that employ the accelerometer as the main interaction modality. The demonstration shows the evaluation for the system usability.

In evolutionary game theory, to the best of our knowledge, individuals' perceptions have not been taken into consideration explicitly. When an individual interacts with the other individual under coexistence of heterogeneous sub-populations, the individual may be willing to change his/her strategy depending on the sub-population the other individual belongs to. Moreover, in such a situation, each individual may make an error about the sub-population the other individual belongs to. In this paper, we propose a multi-population model with such erroneous perceptions. We define an evolutionarily stable strategy (ESS) and formulate replicator dynamics in this model, and prove several properties of the proposed model. Moreover, we focus on a two-population chicken game with erroneous perceptions and discuss characteristics of equilibrium points of its replicator dynamics.

If some differences of perceptions arise between populations, then strategies which are regarded as the same strategy in a population may be perceived distinguishably in the other populations. To discuss such a situation, replicator dynamics for multi-population hypergames has been proposed. However, it is assumed that players' perceptions are given and fixed. In this paper, we consider that each population has various interpretation functions and choose one of them depending on payoffs, and we propose a hybrid system representation of replicator dynamics with changes of interpretation functions. Moreover, we apply our proposed model to a well-known example of a hypergame "Soccer Hooliganism" and show that behaviors converging to heteroclinic orbits can appear by the changes of the interpretation functions.

In this paper, we propose a robust statistical framework for extracting scenes from a baseball broadcast video. We apply multi-stream hidden Markov models (HMMs) to control the weights among different features. To achieve a large robustness against new scenes, we used a common simple structure for all the HMMs. In addition, scene segmentation and unsupervised adaptation were applied to achieve greater robustness against differences in environmental conditions among games. The F-measure of scene-extracting experiments for eight types of scene from 4.5 hours of digest data was 77.4% and was increased to 78.7% by applying scene segmentation. Furthermore, the unsupervised adaptation method improved precision by 2.7 points to 81.4%. These results confirm the effectiveness of our framework.

In this paper, we develop a new distributed adaptive power control framework for multi-cell OFDM systems based on the game theory. A specific utility function is defined considering the users' achieved average utility per power, i.e., power unit based utility. We solve the subcarrier allocation issue naturally as well as the power control. Each user tries to maximize its utility by adjusting the transmit power on each subcarrier. A Nash equilibrium for the game is shown to exist and the numerical results show that our proposal outperforms the pure water-filling algorithm in terms of efficiency and fairness.

This paper deals with graph colouring games, an example of pseudo-telepathy, in which two players can convince a verifier that a graph G is c-colourable where c is less than the chromatic number of the graph. They win the game if they convince the verifier. It is known that the players cannot win if they share only classical information, but they can win in some cases by sharing entanglement. The smallest known graph where the players win in the quantum setting, but not in the classical setting, was found by Galliard, Tapp and Wolf and has 32,768 vertices. It is a connected component of the Hadamard graph GN with N=c=16. Their protocol applies only to Hadamard graphs where N is a power of 2. We propose a protocol that applies to all Hadamard graphs. Combined with a result of Frankl, this shows that the players can win on any induced subgraph of G12 having 1609 vertices, with c=12. Moreover combined with a result of Godsil and Newman, our result shows that all Hadamard graphs GN (N ≥ 12) and c=N yield pseudo-telepathy games.

This paper proposes novel methods to provide intelligence for characters in fighting action games by using neural networks. First, how a character learns basic game rules and matches against randomly acting opponents is considered. Since each action takes more than one time unit in general fighting action games, the results of a character's action are exposed not immediately but several time units later. We evaluate the fitness of a decision by using the relative score change caused by the decision. Whenever the scores of fighting characters are changed, the decision causing the score change is identified, and then the neural network is trained by using the score difference and the previous input and output values which induced the decision. Second, how to cope more properly with opponents that act with predefined action patterns is addressed. The opponents' past actions are utilized to find out the optimal counter-actions for the patterns. Lastly, a method in order to learn moving actions is proposed. To evaluate the performance of the proposed algorithm, we implement a simple fighting action game. Then the proposed intelligent character (IC) fights with the opponent characters (OCs) which act randomly or with predefined action patterns. The results show that the IC understands the game rules and finds out the optimal counter-actions for the opponents' action patterns by itself.

Flash bulk files downloading in style of P2P through perpendicular pattern becomes more popular recently. Many peers download different pieces of shared files from the source in parallel. They try to reconstruct complete files by exchanging needed pieces with other downloading peers. The throughput of entire downloading community, as well as the perceived downloading rate of each peer, greatly depends on uploading bandwidth contributed by every individual peer. Unfortunately, without proper built-in incentive mechanism, peers inherently tend to relentlessly download while intentionally limiting their uploading bandwidth. In this paper, we propose a both effective and efficient incentive approach--Reciprocity, which is only based on end-to-end measurement and reaction: a peer caps uploading rate to each of its peers at the rate that is proportional to its downloading rate from that one. It requires no centralized control, or electronic monetary payment, or certification. Preliminary experiments' results reveal that this approach offers favorable performance for cooperative peers, while effectively punishing defective ones.

This paper investigates the effects of group (or inter-destination) synchronization control over haptic media in a networked game where two players move objects competitively by manipulating haptic interface devices. The group synchronization control adjusts the output timing of haptic media among multiple players. By experiment, we demonstrate the effectiveness of the control. We also discuss the fairness between the two players quantitatively.

The process of evaluating and selecting Commercial Off-The-Shelf (COTS) products is complicated because of conflicting priorities of the stakeholders, complex interdependences among the evaluation criteria, multiple evaluation objectives, changing system requirements, and a large number of similar COTS products with extreme capability differences. Numerous COTS evaluation and selection methods have been proposed to address the complexity of the process. Some of these methods have been successfully applied in industry. However, negotiation to resolve stakeholder conflicts is still an ad hoc process. In this paper, we present a systematic model that assists the COTS selection stakeholders in identifying conflicts, as well as in determining and evaluating conflict resolution options. Our model is facilitated by an agent-based decision support system, which has user agents that assist the stakeholders in the COTS evaluation and negotiation process. The agents utilize a hybrid of analytic and artificial intelligence techniques to identify conflicts and the corresponding agreement options. Moreover, each user agent analyzes the agreement options in detail before advising its client about which goals to optimize, and which goals to compromise in order to reach agreement with the other stakeholders. Finally, the community of agents facilitates information sharing among stakeholders in a bid to improve the quality of their COTS selection decisions.

In contention-based wireless ad hoc networks, power control is an efficient way to improve the spatial reuse by allowing multiple pairs to communicate simultaneously. In this paper, we propose a game-theoretical approach for joint power and rate control in ad hoc networks, where the transmit rate of each link is maximized. Meanwhile we consider the transmit power as the cost, since higher power leads to higher interference and more energy consumption. In particular, we introduce a novel auction-like pricing algorithm in which the cost per unit power steps up until the network settles down at a Nash equilibrium, which is a feasible power and rate allocation, even when the Signal to Interference and Noise Ratio (SINR) requirements are initially infeasible. Numerical results show significant throughput improvement and energy consumption savings compared with the previously proposed algorithm that defers the link with minimum SINR.

Over the past few years, a considerable number of studies have been conducted on modeling the bargaining game using artificial agents on within-model interaction. However, very few attempts have been made at study on the interaction and co-evolutionary process among heterogeneous artificial agents. Therefore, we present two kinds of artificial agents, based on genetic algorithm (GA) and reinforcement learning (RL), which play a game on between-model interaction. We investigate their co-evolutionary processes and analyze their parameters using the analysis of variance.