通过侧支付的adcop局部最大值

Yair Vaknin, A. Meisels
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引用次数: 2

摘要

分布式约束优化问题是由多个受约束的智能体组成的,其解是所有具有最大全局社会福利的智能体的赋值问题。众所周知,dcop是NP-Hard的,因此需要不完全搜索算法。dcop的局部搜索算法是不完整的分布式算法,它从某个初始状态出发,试图找到改进的解([10,13])。当受约束代理的约束具有不同的值时,这种问题被称为非对称dcop或adcop。标准的本地搜索算法,如DSA或MGM,不能保证在adcop上收敛。当趋同确实发生时,解决方案不一定是全球社会福利的极值。对于专为非对称dcop设计的局部搜索算法也是如此[3]。基于adcop与多智能体博弈的类比,提出了一种adcop的迭代局部搜索算法。在算法的每次迭代中,代理可以向它们的邻居提出侧支付,作为回报,它们选择提供侧支付的代理更喜欢的分配。结果表明,该协议产生了分布式算法的行为,是多智能体博弈中最佳响应动力学的扩展。所提出的竞价有效均衡契约(BEECon)算法的这些特性保证了算法收敛于全局社会福利的局部最优。对随机生成的ADCOP进行了大量的实验评估,结果表明,该算法收敛速度快,且所得到的解比之前最好的ADCOP局部搜索算法具有更高的社会福利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local Maxima in ADCOPs via Side Payments
Distributed constraints optimization problems (DCOPs) are composed of multiple constrained agents and their solution is an assignment of values by all agents that has the maximal global social welfare. DCOPs are known to be NP-Hard and therefore need incomplete search algorithms. Local search algorithms for DCOPs are incomplete distributed algorithms that start from some initial state and attempt to find improved solutions ( [10, 13]). When the constraints have different values for the constrained agents, the problems are termed Asymmetric DCOPs, or ADCOPs. Standard local search algorithms, such as DSA or MGM, are not guaranteed to converge on ADCOPs. When convergence does happen, the solution is not necessarily an extremum of the global social welfare. This is true also for local search algorithms that were designed specifically for asymmetric DCOPs [3]. The present paper proposes an iterative local search algorithm for ADCOPs, that relies on the analogy between ADCOPs and multi-agents games. In each iteration of the algorithm agents can propose side-payments to their neighbors, in return to their choice of an assignment that is preferred by the agent offering the side payment. It is shown that the proposed protocol produces a behavior of the distributed algorithm that is an extension of the best-response dynamics in muti-agent games. These properties of the proposed Bidding Efficient Equilibria Contracts (BEECon) algorithm guarantee convergence towards a local optimum of the global social welfare. Extensive experimental evaluation on randomly generated ADCOPs demonstrates that convergence is fast and that the resulting solutions are of higher social welfare than those of the best former ADCOP local search algorithm.
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