In the monograph "Strong artificial intelligence. On the Approaches to Superintelligence" contains an overview of general artificial intelligence (AGI). As an anthropomorphic research area, it includes Brain Principles Programming (BPP) -- the formalization of universal mechanisms (principles) of the brain work with information, which are implemented at all levels of the organization of nervous tissue. This monograph contains a formalization of these principles in terms of category theory. However, this formalization is not enough to develop algorithms for working with information. In this paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed earlier, which modeling cognitive functions and base on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin Theory of Functional Brain Systems, Eleanor Rosch prototypical categorization theory, Bob Rehder theory of causal models and "natural" classification. As a result, a formalization of BPP is obtained and computer experiments demonstrating the operation of algorithms are presented.
翻译:在专著《强力人工智能。关于超自然智能的方法》的专著《超自然智能》中,包含对一般人工智能的概述。作为一个人类形态研究领域,它包括大脑原理编程(BPP) -- -- 大脑工作与信息的普遍机制(原则)的正规化,在神经组织组织的各个级别上实施。该专著包含这些原则在分类理论方面的正规化。然而,这种正规化并不足以为与信息合作制定算法。在本文中,为了描述和建模BPP,建议应用早先开发的数学模型和算法,这些模型和算法以众所周知的生理、心理和其他自然科学理论为模型和基础。论文使用了以下理论的数学模型和算法:P.K.Anokhin 功能性脑系统理论、Eleanor Rosch 原型分类理论、因果模型和“自然”分类的Bob Rehder理论。因此获得了BPP的正规化和计算机实验,并展示了算法的运作。