For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems
For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems
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DOI：
10.1109/fas-w.2017.119
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
J. Pitt;E. Hart
中科院分区：
文献类型：
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作者：
J. Pitt;E. Hart
这份立场文件关注的是工程多尺度和持久的系统，其操作是由一套相互商定的，传统的规则的挑战。问题的核心是，流动有多个相互依存的层面，需要考虑许多背景因素。一方面，流动的这些维度包括规则本身;另一方面，系统组件（人口），他们的社会网络和操作环境。然而，似乎没有一种“放之四海而皆准”的最佳规则适用于人口、社会网络和环境的所有组合;（考虑到背景因素）也没有一种规划型算法可以为人口、社会网络和环境的任何特定组合计算出一种“理想”规则。问题的这些特征表明，机器学习和进化计算的最新进展可以提供工具，促进基于规则的系统在不同的时间尺度上的自适应。本文提出，从社会和生物启发计算的概念的整合可以铺平道路，最终发展的计算框架，这将使可持续的自适应规则为基础的系统的原则（方法）的发展。
This position paper is concerned with the challenge of engineering multi-scale and long-lasting systems, whose operation is regulated by sets of mutually-agreed, conventional rules. The core of the problem is that there are multiple, inter-dependent dimensions of flux, with numerous contextual factors to take into account. These dimensions of flux include, on the one hand, the set of rules itself; and on the other, the system components (population), their social network, and the operating environment. However, there appears to be no `one size fits all' optimum ruleset for all combinations of population, social network and environment; nor (given the contextual factors) is there a planning-type algorithm that can compute an `ideal' ruleset for any particular combination of population, social network and environment. These features of the problem suggest that recent advances in machine learning and evolutionary computation can provide the instruments for facilitating self-adaptation of a rule-based system over different timescales. This paper proposes that the integration of concepts from socially- and biologically-inspired computing can pave the way for eventual development of a computational framework that will enable principled (methodological) development of sustainable adaptive rule-based systems.