We present a self-learning evolutionary Prisoner's Dilemma game model to study the evolution of cooperation in network-structured *** the evolutionary process,each agent updates its current strategy with a probabi...
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We present a self-learning evolutionary Prisoner's Dilemma game model to study the evolution of cooperation in network-structured *** the evolutionary process,each agent updates its current strategy with a probability depending on the difference feedback between its actual score and score *** agent's score is a weighed mean of its payoff coming from its neighbors(social partners) and the payoff of its social partners obtaining from *** results show that the cooperation level in the structured populations increases with increasing the weight of partners' obtaining payoff in the *** interestingly,we find that very similar evolution of cooperation can respectively emerge in lattice,small-world and scale-free networks under the learningfeedback updating ***,we provide theoretical analysis and qualitative explanations for these numerical *** work may provide an effective way to solve the dilemma of cooperation for structured populations.
In real situations, individuals often have moderate tolerance toward ambient cooperative environment in which they tend to avoid unfavorable interactions and search for favorable ones. How such social tolerance affect...
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In real situations, individuals often have moderate tolerance toward ambient cooperative environment in which they tend to avoid unfavorable interactions and search for favorable ones. How such social tolerance affects the evolution of cooperation and the resulting cooperative networks remains to be answered. To address this issue, here we present an effective model of co-evolutionary prisoner’s dilemma by introducing cooperative environment and social tolerance for networked players. An individual’s level of cooperative environment characterizes the cooperativity and sustainability of its interaction environment centered on itself. In our model, for paired individuals we assume that the one in better cooperative environment has a certain tolerance threshold to the opponent. If the opponent’s cooperative environment level is beyond the tolerance threshold, the one in better cooperative environment cuts unilaterally the link, and rewires to others. Otherwise, the link is not severed, and meanwhile an inhomogeneous strategy imitation process between them is considered. Moreover, a player’s cooperative environment is adjusted in response to the strategy choices in the neighborhood. Interestingly, we find that there exists a moderate tolerance threshold warranting the best promotion of cooperation. We explain the nontrivial results by investigating the time ratio of strategy (network) updating during the whole process and properties in emerging networks. Furthermore, we investigate the effect of memory-dependent discounting of individuals’ cooperative environment on the evolution of cooperation. We also demonstrate the robustness of our results by considering two other modified co-evolutionary rules. Our results highlight the importance of appropriate tolerance threshold for the evolution of cooperation during the entangled co-evolution of strategy and structure.
Model uncertainties and external disturbances extensively exist in UnmannedSurface Vehicle (USV) systems, which will greatly deteriorate the autonomousability. In this paper, a, new kind, of robust controller design s...
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This paper proposed a methodology for kinematic and dynamic modeling of biped robot with Heterogeneous legs (BRHL). Based on design characteristics in the knee joint used in intelligent prosthesis leg, i.e., multi-axe...
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Profiling program and user behaviors is an effective approach for detecting hostile attacks to a computer system. A new model based method by non-negative matrix factorization (NMF) is presented in this paper to profi...
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ISBN:
(纸本)0780386825
Profiling program and user behaviors is an effective approach for detecting hostile attacks to a computer system. A new model based method by non-negative matrix factorization (NMF) is presented in this paper to profile program and user behaviors for anomaly intrusion detection. In this new method, the audit data streams obtained from sequences of system calls and UNIX commands are used as the information source. The audit data is partitioned into segments with a fixed length. Program and user behaviors are, in turn, measured by the frequencies of individual system calls or commands embedded in each segment of the data, and NMF is applied to extract the features from the blocks of audit data associated with the normal behaviors. The model describing the normal program and user behaviors are built based on these features and deviation from the normal program and user behaviors above a predetermined threshold is considered as anomalous. The method is implemented and tested with the system call data from the University of New Mexico and the Unix command data from AT&T Research lab. Experiment results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for real-time intrusion detection.
This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of ...
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This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of the surface of 3-D objects with a multi-objective optimization function to meet the needs of a wide range of applications. Further, a new crossover operator for triangulation and a new 3-D quadrilateral mutation operator are also introduced.
Intelligent micromachines, with dimensions ranging from a few millimeters down to hundreds of nanometers, are miniature systems capable of performing specific tasks autonomously at small scales. Enhancing the intellig...
Intelligent micromachines, with dimensions ranging from a few millimeters down to hundreds of nanometers, are miniature systems capable of performing specific tasks autonomously at small scales. Enhancing the intelligence of micromachines to tackle the uncertainty and variability in complex microenvironments has applications in minimally invasive medicine, bioengineering, water cleaning, analytical chemistry, and more. Over the past decade, significant progress has been made in the construction of intelligent micromachines, evolving from simple micromachines to soft, compound, reconfigurable, encodable, multifunctional, and integrated micromachines, as well as from individual to multiagent, multiscale, hierarchical, self-organizing, and swarm micromachines. The field leverages two important trends in robotics research—the miniaturization and intelligentization of machines—but a compelling combination of these two features has yet to be realized. The core technologies required to make such tiny machines intelligent include information media, transduction, processing, exchange, and energy supply, but embedding all of these functions into a system at the micro- or nanoscale is challenging. This article offers a comprehensive introduction to the state-of-the-art technologies used to create intelligence for micromachines and provides insight into the construction of next-generation intelligent micromachines that can adapt to diverse scenarios for use in emerging fields.
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