计算智能中的朗道自适应集成理论
Landau Theory of Adaptive Integration in Computational Intelligence
摘要：
计算智能（CI）是人工智能范式的一个分支，专注于研究在复杂和不断变化的环境中实现或促进智能行为的适应机制。CI有几种范例[如人工神经网络，进化计算，群体智能，人工免疫系统，模糊系统等]，每一种都起源于生物系统[生物神经系统，自然达尔文进化，社会行为，免疫系统，生物体与环境的相互作用]。这些范例中的大多数演变成单独的机器学习（ML）技术，其中概率方法与CI技术互补使用，以有效地结合学习，适应，进化和模糊逻辑的元素，以创建启发式算法，在某种意义上，智能。目前的趋势是开发共识技术，因为没有一种机器学习算法在所有可能的情况下都优于其他算法。为了克服这个问题，ML中提出了几种元方法，重点是将不同方法的结果整合到单个预测中。我们在这里讨论的朗道理论的非线性方程，可以描述自适应集成的信息从一个独立的学习代理。每个个体代理对其他学习者的影响类似于社会影响理论。共识系统的最终决策结果是在稳态极限下使用多数规则计算的，但少数解决方案可以作为相反意见的复杂间歇性集群在多数群体中生存。
英文摘要：
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelligence, artificial immune systems, fuzzy systems and many others], each of these has its origins in biological systems [biological neural systems, natural Darwinian evolution, social behavior, immune system, interactions of organisms with their environment]. Most of those paradigms evolved into separate machine learning (ML) techniques, where probabilistic methods are used complementary with CI techniques in order to effectively combine elements of learning, adaptation, evolution and Fuzzy logic to create heuristic algorithms that are, in some sense, intelligent. The current trend is to develop consensus techniques, since no single machine learning algorithms is superior to others in all possible situations. In order to overcome this problem several meta-approaches were proposed in ML focusing on the integration of results from different methods into single prediction. We discuss here the Landau theory for the nonlinear equation that can describe the adaptive integration of information acquired from an ensemble of independent learning agents. The influence of each individual agent on other learners is described similarly to the social impact theory. The final decision outcome for the consensus system is calculated using majority rule in the stationary limit, yet the minority solutions can survive inside the majority population as the complex intermittent clusters of opposite opinion.
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