Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake. To achieve efficient and rapid networks deployment, we employ noncooperative game theory and amended binary log-linear algorithm (BLLA) seeking for the Nash equilibrium which achieves the optimal network performance. We not only take channel overlap and power control into account but also consider coverage and the complexity of interference. However, extensive UAV game theoretical models show limitations in post-disaster scenarios which require large-scale UAV network deployments. Besides, the highly dynamic post-disaster scenarios cause strategies updating constraint and strategy-deciding error on UAV ad-hoc networks. To handle these problems, we employ aggregative game which could capture and cover those characteristics. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and reduce time consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Hence, the new model and algorithm are more suitable and promising for large-scale highly dynamic scenarios.
Game theory provides an efficient tool for the cooperation through resource allocation and sharing @cite_11 @cite_15 . A computation offloading game has been designed in order to balance the UAV's tradeoff between execution time and energy consumption @cite_34 . A sub-modular game is adopted in the scheduling of beaconing periods for the purpose of less energy consumption @cite_0 . applied the Bayesian game-theoretic methodology in UAV's intrusion detection and attacker ejection @cite_31 . However, most existing models focus on common scenarios with less number of UAVs, which are not compatible with large-scale scenarios with large numbers of UAVs @cite_21 . Aggregative game is a characteristic game model which treats other agents' strategies as a whole influence, thus avoids overwhelming strategies information from every single agent @cite_25 @cite_16 . Inspired by this, our model is built upon the aggregative game theory which suits for large-scale scenarios.
@cite_31 @cite_21 @cite_16 @cite_0 @cite_15 @cite_34 @cite_25 @cite_11
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Game theoretic models have found widespread use in the analysis and design of radio resource management algorithms for a wide variety of systems such as cellular, ad hoc and sensor networks. The fundamental principle behind such models and much of game theory has been the reliance on expected utility theory (EUT). In this paper, we consider a simple wireless random access game where instead of following the precepts of EUT, the players follow the precepts of Prospect Theory (PT), a theory developed by Kahneman and Tversky to explain real-life decision making that often deviates from the behavior expected under EUT. Specifically, we consider a game where selfish players adjust their transmission probabilities over a collision channel according to rewards received for successful transmission while incurring energy and delay costs. We compare and contrast the Nash equilibria achieved under both EUT and PT and highlight the differences in them. We propose a hybrid model for automatically acquiring a policy for a complex game, which combines online learning with mining knowledge from a corpus of human game play. Our hypothesis is that a player that learns its policies by combining (online) exploration with biases towards human behaviour that's attested in a corpus of humans playing the game will outperform any agent that uses only one of the knowledge sources. During game play, the agent extracts similar moves made by players in the corpus in similar situations, and approximates their utility alongside other possible options by performing simulations from its current state. We implement and assess our model in an agent playing the complex win-lose board game Settlers of Catan, which lacks an implementation that would challenge a human expert. The results from the preliminary set of experiments illustrate the potential of such a joint model. We consider the problem of having a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) pursue a second team of evaders while concurrently building a map in an unknown environment. We cast the problem in a probabilistic game theoretical framework, and consider two computationally feasible greedy pursuit policies: local-mar and global-max. To implement this scenario on real UAVs and UGVs, we propose a distributed hierarchical hybrid system architecture which emphasizes the autonomy of each agent, yet allows for coordinated team efforts. We describe the implementation of the architecture on a fleet of UAVs and UGVs, detailing components such as high-level pursuit policy computation, map building and interagent communication, and low-level navigation, sensing, and control. We present both simulation and experimental results of real pursuit-evasion games involving our fleet of UAVs and UGVs, and evaluate the pursuit policies relating expected capture times to the speed and intelligence of the evaders and the sensing capabilities of the pursuers. Game theory has been used for decades in fields of science such as economics and biology, but recently it was used to model routing and packet forwarding in wireless ad-hoc and sensor networks. However, the clustering problem, related to self-organization of nodes into large groups, has not been studied under this framework. In this work our objective is to provide a game theoretical modeling of clustering for ad-hoc and sensor networks. The analysis is based on a non-cooperative game approach where each sensor behaves selfishly in order to conserve its energy and thus maximize its lifespan. We prove the Nash Equilibria of the game for pure and mixed strategies, the expected payoffs and the price of anarchy corresponding to these equilibria. Then, we use this analysis to formulate a clustering mechanism (which we called Clustered Routing for Selfish Sensors--CROSS), that can be applied to sensor networks in practice. Comparing this mechanism to a popular clustering technique, we show via simulations that CROSS achieves a performance similar to that of a very popular clustering algorithm. Part I of this paper has described a new theory for the analysis of games with incomplete information. It has been shown that, if the various players' subjective probability distributions satisfy a certain mutual-consistency requirement, then any given game with incomplete information will be equivalent to a certain game with complete information, called the “Bayes-equivalent” of the original game, or briefly a “Bayesian game.” Part II of the paper will now show that any Nash equilibrium point of this Bayesian game yields a “Bayesian equilibrium point” for the original game and conversely. This result will then be illustrated by numerical examples, representing two-person zero-sum games with incomplete information. We shall also show how our theory enables us to analyze the problem of exploiting the opponent's erroneous beliefs. However, apart from its indubitable usefulness in locating Bayesian equilibrium points, we shall show it on a numerical example the Bayes-equivalent of a two-person cooperative game that the normal form of a Bayesian game is in many cases a highly unsatisfactory representation of the game situation and has to be replaced by other representations e.g., by the semi-normal form. We shall argue that this rather unexpected result is due to the fact that Bayesian games must be interpreted as games with “delayed commitment” whereas the normal-form representation always envisages a game with “immediate commitment.” We study competition between wireless devices with incomplete information about their opponents. We model such interactions as Bayesian interference games. Each wireless device selects a power profile over the entire available bandwidth to maximize its data rate. Such competitive models represent situations in which several wireless devices share spectrum without any central authority or coordinated protocol. In contrast to games where devices have complete information about their opponents, we consider scenarios where the devices are unaware of the interference they cause to other devices. Such games, which are modeled as Bayesian games, can exhibit significantly different equilibria. We first consider a simple scenario of simultaneous move games, where we show that the unique Bayes-Nash equilibrium is where both devices spread their power equally across the entire bandwidth. We then extend this model to a two-tiered spectrum sharing case where users act sequentially. Here one of the devices, called the primary user, is the owner of the spectrum and it selects its power profile first. The second device (called the secondary user) then responds by choosing a power profile to maximize its Shannon capacity. In such sequential move games, we show that there exist equilibria in which the primary user obtains a higher data rate by using only a part of the bandwidth. In a repeated Bayesian interference game, we show the existence of reputation effects: an informed primary user can bluff to prevent spectrum usage by a secondary user who suffers from lack of information about the channel gains. The resulting equilibrium can be highly inefficient, suggesting that competitive spectrum sharing is highly suboptimal. Predictable allocations of security resources such as police officers, canine units, or checkpoints are vulnerable to exploitation by attackers. Recent work has applied game-theoretic methods to find optimal randomized security policies, including a fielded application at the Los Angeles International Airport (LAX). This approach has promising applications in many similar domains, including police patrolling for subway and bus systems, randomized baggage screening, and scheduling for the Federal Air Marshal Service (FAMS) on commercial flights. However, the existing methods scale poorly when the security policy requires coordination of many resources, which is central to many of these potential applications. We develop new models and algorithms that scale to much more complex instances of security games. The key idea is to use a compact model of security games, which allows exponential improvements in both memory and runtime relative to the best known algorithms for solving general Stackelberg games. We develop even faster algorithms for security games under payoff restrictions that are natural in many security domains. Finally, introduce additional realistic scheduling constraints while retaining comparable performance improvements. The empirical evaluation comprises both random data and realistic instances of the FAMS and LAX problems. Our new methods scale to problems several orders of magnitude larger than the fastest known algorithm. Game-theoretic tools are becoming a popular design choice for distributed resource allocation algorithms. A central component of this design choice is the assignment of utility functions to the individual agents. The goal is to assign each agent an admissible utility function such that the resulting game possesses a host of desirable properties, including scalability, tractability, and existence and efficiency of pure Nash equilibria. In this paper we formally study this question of utility design on a class of games termed distributed welfare games. We identify several utility design methodologies that guarantee desirable game properties irrespective of the specific application domain. Lastly, we illustrate the results in this paper on two commonly studied classes of resource allocation problems: “coverage” problems and “coloring” problems.