随机扩散搜索的稳态资源分配分析
Steady State Resource Allocation Analysis of the Stochastic Diffusion Search
摘要：
本文介绍了随机扩散搜索（SDS）的长期行为分析，一个分布式的代理为基础的系统，最适合的模式匹配。SDS通过将简单代理分配到搜索空间的不同区域来操作。智能体独立地提出关于搜索空间中模式的存在及其潜在失真的假设。假设关于模式匹配的假设的组成结构，代理基于来自假设的解决方案的部分证据执行推断。对模式提出相互一致假设的代理相互支持，并抑制具有不一致假设的代理。这导致出现一个稳定的代理群体，识别所需的解决方案。通过代理之间的信息扩散的正反馈显着有助于解决方案群体形成的速度。SDS模型在相互作用的马尔可夫链方面的制定使其能够在代理或计算资源的分配方面进行表征。该分析表征代理的活动的固定概率分布，这导致在其与目标模式的相似性方面的解决方案人口的特征。
英文摘要：
This article presents the long-term behaviour analysis of Stochastic Diffusion Search (SDS), a distributed agent-based system for best-fit pattern matching. SDS operates by allocating simple agents into different regions of the search space. Agents independently pose hypotheses about the presence of the pattern in the search space and its potential distortion. Assuming a compositional structure of hypotheses about pattern matching agents perform an inference on the basis of partial evidence from the hypothesised solution. Agents posing mutually consistent hypotheses about the pattern support each other and inhibit agents with inconsistent hypotheses. This results in the emergence of a stable agent population identifying the desired solution. Positive feedback via diffusion of information between the agents significantly contributes to the speed with which the solution population is formed. The formulation of the SDS model in terms of interacting Markov Chains enables its characterisation in terms of the allocation of agents, or computational resources. The analysis characterises the stationary probability distribution of the activity of agents, which leads to the characterisation of the solution population in terms of its similarity to the target pattern.
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