Analogical Learning / Inference / Reasoning: A study based on Neural-Network Ideas and Cognitive Science

早些时候，s.a yasui （PI）提出了一种被称为CSDF的修剪算法，该算法被认为是冗余减少原理的一种形式，而冗余减少原理可能是许多真正的大脑功能的基础。本项目致力于完善/改进CSDF，并将其主要应用于以下两个领域(A)和(B)。(A)基于抽象的联结主义类比处理器（AB-CAP）：类比在心理学、认识论、教育学、科学史、认知科学和人工智能等各个学科中都有研究。我们的AB-CAP相对简单。通过对训练数据的学习，AB-CAP自动获得内部抽象模型，并在具体实体和抽象实体之间诱导适当的绑定。内部模型作为新的相关数据集的吸引器，使AB-CAP能够处理多个类比范式。这些前景已通过若干实例得到了成功的证明。(B)独立分量分析（ICA）或盲源分离（BSS）：ICA是一种新的有用的IT创新，通过它可以从传感器观察到的混合信号中提取其他未知信号。我们从这个项目中得出的方法与现有的基于信息/概率论的方法有本质的不同。它使用自编码器神经网络，以传感器信号作为输入向量，将输入-输出恒等映射的误差最小化。在解码器部分应用了CSDF。在CSDF剪枝中幸存下来的隐藏非线性单元将作为盲源提取器。此外，解码器矩阵重构了外部混合矩阵，使得整个解码器部分实际上是整个外部情况的内部模型。仿真结果表明，该方法具有较强的适应性和鲁棒性。

Earlier, S.Yasui (PI) presented a pruning algorithm called CSDF that is thought to be one form of the Principle of Redundancy Reduction which presumably underlies many of the real brain functions. This project has been devoted to refine/improve CSDF and also apply it mainly to the following two areas (A) and (B).(A) Abstraction-Based Connectionist Analogy Processor (AB-CAP):Analogy has been studied in various disciplines such as psychology, epistemology, pedagogy, science history, cognitive science and AI. Our AB-CAP is relatively simple. As a result of learning the training data, AB-CAP autonomously acquires an internal abstraction model as well as induces appropriate bindings between concrete and abstract entities. The internal model acts as an attractor of new relevant dataset, to allow AB-CAP to be able to deal with multiple analogy paradigms. These prospects have been successfully demonstrated with a number of examples.(B) Independent Component Analysis (ICA) or Blind Source Separation(BSS):ICA is a new useful IT innovation by which to extract otherwise unknown signals from their mixtures observed by sensors. Our method that came out from this project is fundamentally different from existing ones which are all based on information/probability theories. It uses the auto-encoder neural network which operates to minimize the error associated with the input-output identity mapping with the sensor signals as the input vector. CSDF is applied in the decoder part. The hidden nonlinear units that have survived the CSDF pruning will be the blind source extractors. Furthermore, the decoder matrix reconstructs the external mixing matrix, so that the entire decoder part is actually an internal model of the whole external situation. The method is characterized high adaptability and robustness, as has been shown by many simulation examples including real audio and visual data.