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Deep IA-BI and Five Actions in Circling

机译:Deep Ia-Bi和Fircling的五项行动

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摘要

Deep bidirectional Intelligence (BI) via YIng YAng (IA) system, or shortly Deep IA-BI, is featured by circling A-mapping and I-mapping (or shortly AI circling) that sequentially performs each of five actions. A basic foundation of IA-BI is bidirectional learning that makes the cascading of A-mapping and I-mapping (shortly A-I cascading) approximate an identical mapping, with a nature of layered, topology-preserved, and modularised development. One exemplar is Lrnser that improves autoencoder by incremental bidirectional layered development of cognition, featured by two dual natures DPN and DCW. Two typical IA-BI scenarios are further addressed. One considers bidirectional cognition and image thinking, together with a proposal that combines theories of Hubel-Wiesel's versus Chen's. The other considers bidirectional integration of cognition, knowledge accumulation, and abstract thinking for improving implementation of searching, optimising, and reasoning. Particularly, an IA-DSM scheme is proposed for solving a doubly stochastic matrix (DSM) featured combinatorial tasks such as travelling salesman problem, and also a Subtree driven reasoning scheme is proposed for improving production rule based reasoning. In addition, some remarks are made on relations of Deep IA-BI to Hubel and Wiesel theory, Sperry theory, and A5 problem solving paradigm.
机译:通过ying yang(ia)系统或短期Ia-bi的深度双向智能(bi)通过盘旋映射和i-mapping(或短暂的ai盘旋)来序列地执行五个动作中的每一个。 IA-BI的基本基础是双向学习,使级联和I映射的级联(不久A-I级联)近似相同的映射,具有分层,拓扑保存和模块化开发的性质。一个示例是LRNSER,其通过通过两个双自由度DPN和DCW具有增量双向分层开发来改善AutoEncoder。进一步解决了两个典型的IA-BI场景。一个人认为双向认知和图像思维,以及结合Hubel-Wiesel的理论的提议。另一个考虑了认知,知识累积和抽象思考的双向整合,以改善搜索,优化和推理的实施。特别地,提出了一种用于求解双随机矩阵(DSM)特种组合任务的IA-DSM方案,例如旅行推销员问题,并且提出了子树驱动推理方案,用于改善基于生产规则的推理。此外,一些言论是对哈奶和威州理论,斯托里理论,斯派利理论和解决范例的A5问题的关系。

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