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Multi-Agent Constraint Systems with Preferences: Efficiency, Solution Quality, and Privacy Loss 总被引:1,自引:0,他引:1
In this paper, we consider multi-agent constraint systems with preferences, modeled as soft constraint systems in which variables and constraints are distributed among multiple autonomous agents. We assume that each agent can set some preferences over its local data, and we consider two different criteria for finding optimal global solutions: fuzzy and Pareto optimality. We propose a general graph-based framework to describe the problem to be solved in its generic form. As a case study, we consider a distributed meeting scheduling problem where each agent has a pre-existing schedule and the agents must decide on a common meeting that satisfies a given optimality condition. For this scenario we consider the topics of solution quality, search efficiency, and privacy loss, where the latter pertains to information about an agent's pre-existing meetings and available time-slots. We also develop and test strategies that trade efficiency for solution quality and strategies that minimize information exchange, including some that do not require inter-agent comparisons of utilities. Our experimental results demonstrate some of the relations among solution quality, efficiency, and privacy loss, and provide useful hints on how to reach a tradeoff among these three factors. In this work, we show how soft constraint formalisms can be used to incorporate preferences into multi-agent problem solving along with other facets of the problem, such as time and distance constraints. This work also shows that the notion of privacy loss can be made concrete so that it can be treated as a distinct, manipulable factor in the context of distributed decision making. 相似文献
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In multi-objective design optimization, it is quite desirable to obtain solutions that are multi-objectively optimum and insensitive to uncontrollable (noisy) parameter variations. We call such solutions robust Pareto solutions. In this paper we present a method to measure the multi-objective sensitivity of a design alternative, and an approach to use such a measure to obtain multi-objectively robust Pareto optimum solutions. Our sensitivity measure does not require a presumed probability distribution of uncontrollable parameters and does not utilize gradient information; therefore, it is applicable to multi-objective optimization problems that have non-differentiable and/or discontinuous objective functions, and also to problems with large parameter variations. As a demonstration, we apply our robust optimization method to an engineering example, the design of a vibrating platform. We show that the solutions obtained for this example are indeed robust. 相似文献
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In this paper, we consider the distributed bandit convex optimization of time-varying objective functions over a network. By introducing perturbations into the objective functions, we design a deterministic difference and a randomized difference to replace the gradient information of the objective functions and propose two classes of gradient-free distributed algorithms. We prove that both the two classes of algorithms achieve regrets of for convex objective functions and for strongly convex objective functions, with respect to the time index and consensus of the estimates established as well. Simulation examples are given justifying the theoretical results. 相似文献
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多agent系统作为分布式人工智能研究领域的重要分支,已被广泛应用于多个领域中复杂系统的建模.而分布式约束优化作为一种多agent系统求解的关键技术,已成为约束推理研究的热点.首先对其适用性进行分析,并基于对已有算法的研究,总结出采用该方法解决问题的基本流程,在此基础上,从解的质量保证、求解策略等角度对算法进行了完整的分类;其次,根据算法分类结果以及执行机制,对大量经典以及近年来的分布式约束优化算法进行了深入分析,并从通信、求解质量、求解效率等方面对典型算法进行了实验对比;最后,结合分布式约束优化技术的求解优势给出了分布式约束优化问题的实际应用特征,总结了目前存在的一些问题,并对下一步工作进行了展望. 相似文献
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In this paper, the resource allocation problems of multiagent systems are investigated. Different from the well‐studied resource allocation problems, the dynamics of agents are taken into account in our problem, which results that the problem could not be solved by most of existing resource allocation algorithms. Here, the agents are in the form of second‐order dynamics, which causes the difficulties in designing and analyzing distributed resource allocation algorithms. Based on gradient descent and state feedback, two distributed resource allocation algorithms are proposed to achieve the optimal allocation, and their convergence are analyzed by constructing suitable Lyapunov functions. One of the two algorithms can ensure that the decisions of all agents asymptotically converge to the exact optimal solution, and the other algorithm achieves the exponential convergence. Finally, numerical examples about the economic dispatch problems of power grids are given to verify the effectiveness of the obtained results. 相似文献
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多Agent协作过程中的许多挑战都可以建模为分布式约束优化问题.针对低约束密度的分布式约束优化问题,提出了一种基于贪婪和回跳思想的求解算法.在该算法中,各Agent基于贪婪原则进行决策,能够利用低约束密度问题中大量赋值组合代价为0这一特点来加快求解速度.同时,Agent间的回跳机制可以在贪婪原则陷入局部最优时保证算法的完全性.相对于已有主流算法,该算法可以在保持多项式级别的消息长度/空间复杂度的前提下,以较少的消息数目求解低约束密度的分布式约束优化问题.给出了算法关键机制的正确性证明,并通过实验验证了算法的上述性能优势. 相似文献
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This paper studies the distributed optimization problem of second-order multiagent systems containing external disturbances. To reject the external disturbances and lead agents' states to converge to the optimal consensus point, an adaptive event-triggered controller is proposed based on the internal model principle. With the adaptive mechanism, both the controller and the event-triggering condition do not contain the parameters related to global information, such as the maximum Lipschitz constant and the minimum strongly convex constant of local cost functions, and hence the event-triggered controller is fully distributed. By utilizing the event-triggered scheme, the consumption of communication among neighbors and the computing resources are saved. Furthermore, with the Lyapunov analysis framework, the optimal consensus can be proved to achieve and Zeno behavior is excluded from the event-triggering condition. Finally, the effectiveness of the proposed protocol is verified by numerical simulations. 相似文献
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Feasible Formation of Coalitions Among Autonomous Agents in Nonsuperadditive Environments 总被引:2,自引:0,他引:2
Cooperating and sharing resources by creating coalitions of agents are important ways for autonomous agents to execute tasks and to maximize payoff. Such coalitions will form only if each member of a coalition gains more by joining the coalition than it could gain otherwise. There are several ways of creating such coalitions and dividing the joint payoff among the members. In this paper we present algorithms for coalition formation and payoff distribution in nonsuperadditive environments. We focus on a low-complexity kernel-oriented coalition formation algorithm. The properties of this algorithm were examined via simulations. These have shown that the model increases the benefits of the agents within a reasonable time period, and more coalition formations provide more benefits to the agents.
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This paper mainly discusses distributed constrained optimization problem for second-order multi-agent system under undirected communication network. The task of all agents is to minimize the sum of the local convex functions, where each agent is individual and only accesses to one objective function. Different from the most existing results, where the objective functions are assumed to be time-invariable, this paper considers the situation of time-varying objective function. Besides, we don't require that the Hessian matrices are identical and the gradients are bounded. First, a novel time-varying optimization algorithm is proposed based on the projection algorithm. Second, by using convex analysis and Lyapunov theory, it is shown that the states of all agents can reach consensus and asymptotically converge to the neighborhood of the optimal solution. Finally, some numerical examples are given to verify the effectiveness of our algorithms. 相似文献
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针对某生物杀螺剂制作中多目标约束问题,提出了一种应用Pareto遗传算法来解决问题的优化方法。建立了用于多目标优化的适应度函数,使用排列选择方法将带约束的多目标问题转换为无约束优化问题;并根据计算中的收敛情况引入了适当的移民算子,改善了遗传算法的进化性能,得到了Pareto最优解集,成功地解决了该生物杀螺剂的最优配方问题。 相似文献
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多Agent之间的协调(coordination)与协作(cooperation)已经成为多Agent系统(multiagent system,MAS)中的一个关键问题。这是因为MAS的主要研究目标之一就是使得多Agent的信念、意图、期望、行为达到协调甚至协作。在开放、动态的MAS环境下,具有不同目标的多个Agent必须对其资源的使用以及目标的实现进行协调[1,4]。例如,在出现资源冲突时,若没有很好的协调机制,就有可能出现死锁。而在另一种情况下,当单个Agent无法独立完成目标,需要其它Agent帮助时,则需要协作。本文提出了一种基于正关系的多Agent协调机制和协调算法。在该算法中,通过使用这种协调机制,Agent能委托或接受交互中的子计划,从而形成系统负载均衡和有效降低系统运行开销。 相似文献
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The next generation of smart grid technologies demands intelligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multi-agent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this article we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-configurable mechanism that allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which energy is purchased from the market. 相似文献
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多智能体系统分布式优化由于其高效性、灵活性和可靠性等特点吸引了大量学者的关注,在多机器人协同控制、无线传感器网络、能源系统等领域具有广泛的应用前景.分布式优化的基本目标是利用智能体的个体目标函数梯度、自身及其邻居状态信息设计分布式控制协议,驱动所有智能体的状态或输出到全局目标函数的最优解,系统动力学是影响智能体状态演化的重要因素.鉴于此,在回顾现有连续时间分布式优化算法的基础上,根据系统动力学分类,尽可能全面地评述具有复杂动力学的多智能体系统分布式优化问题的最新研究进展,并对未来发展方向进行展望. 相似文献
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In this paper, we study the resource allocation problem of second‐order multiagent systems with exogenous disturbances, and the communication networks are weight‐balanced digraphs. Different from the well‐studied resource allocation problems, our problem involves the disturbed second‐order dynamics of agents. In order to achieve the optimal allocation, we propose a distributed algorithm based on gradient descent and internal model approach. Furthermore, we analyze the convergence of the algorithm by constructing a suitable Lyapunov function. Moreover, we prove that the agents in the network can achieve the exact optimal allocation even in the presence of external disturbances. Finally, we provide two examples to illustrate our result. 相似文献
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《International journal of systems science》2012,43(14):2616-2631
ABSTRACTThis paper focuses on the coordinated circumnavigation problem for multiple agents with directed communication topology. All agents are required to be evenly spaced around a moving target, and orbit around it with prescribed radii and circular velocity. We divide the coordinated circumnavigation system into a cascaded system consisting of a standoff tracking subsystem and a spacing distribution subsystem. A tracking controller and an additional controller for the two subsystems are developed, respectively. The global uniform stability of the coordinated circumnavigation system is analysed using the cascaded control theory. The presented controllers render each individual agent circumnavigating the target with the desired requirements. More importantly, we show that the derived tracking controller is a general extension of existing controllers for single agent circumnavigation problem. Another feature of the proposed controllers is that the directed topology considered only needs to have a directed spanning tree. Furthermore, the proposed controllers take the velocity constraints into consideration, that is more rational for practical applications. Numerical simulations are provided to verify the effectiveness of the proposed methods. 相似文献
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《Computer Animation and Virtual Worlds》2017,28(3-4)
In this paper, we propose a distributed control approach for flocking and group maneuvering of nonholonomic agents, with constrained kinematic properties commonly found in practical systems, such as fixed‐wing unmanned aerial vehicles. Flocking of agents with differential drive kinematics is addressed by introducing a virtual leader–follower mechanism into the Olfati‐Saber's algorithm, which is originally proposed for holonomic agents with double integrator kinematics. Then, group maneuverability of the flock is achieved by superimposing a group motion onto each agent's flocking motion. Moreover, it is proven that speed limits are intrinsically guaranteed by the approach, which renders it more applicable in practical systems. Experimental results in MATLAB and Gazebo, a popular robotic simulator, are presented to evaluate the performance and demonstrate the effectiveness of the proposed approach. 相似文献
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足球机器人决策子系统设计与分析 总被引:15,自引:0,他引:15
足球机器人系统目前已成为人工智能应用技术研究的重要实验平台,系统的核心部分就是决策子系统。本文介绍了一种决策子系统的分层控制结构设计,它由协调层,运动规划层和基本动作层组成,这三层在逻辑上紧密联系又相对独立,使该决策子系统具有适应性强,扩展性好等特点。 相似文献