12月 28, 2018

澳洲靠谱代写:AI模型和架构应用

澳洲靠谱代写:AI模型和架构应用

Sun(2012)的工作提出了用于认知建模的实际AI模型架构。《号角》模仿了人类思维的运作方式,它应该与人类现实主义和对事件的主观理解相结合。心理依赖认知建模是认知心理学中比较复杂的一个方面。根据研究人员的说法,理性思维完全来源于事实,并且是由规则定义的,这使得它不那么复杂。当把情绪、气质、人格等心理方面纳入语境时,建模领域就变得更加复杂。号角的发展是这样的,它确实模仿了这些人的行为和模式。CLARION被认为利用了与分类、推理、决策模型和归纳有关的准定律来发展这种优势。

澳洲靠谱代写:AI模型和架构应用

正如Sun(2012)所说,“我们可以在这里回忆起,符号逻辑范式。符号方法始于最高的知识层次:符号操作和语言。这种方法的基本假设是,人脑不仅是能够产生思维过程的物质支持。符号模型提供了一个互补的配置文件,这意味着它们在进行推理过程、推理和推理、自然的符号操作和它们的辩护时获得了性能。它们的表示是局部性和显式的,而且非常容易理解”

这些心理现象是在CLARION的非行动中心子系统(NACS)中捕捉到的。这项工作是弥合人工智能和心理现实(大脑/思维启发)模型之间、数学上简单的认知模型和一般认知结构之间差距的一步。因此,它是一种建立心理上现实的认知主体的方法,可以开始应对上述挑战。

澳洲靠谱代写:AI模型和架构应用

The work of Sun (2012) presented the actual AI model architecture that is used in cognitive modeling. The CLARION mimics the working of the human mind and is supposed to work with the factor of human realism and subjective understanding of events. Psychologically dependent cognitive modeling is one of the more complex aspects in cognitive psychology. According to researchers, rational thinking is solely derived from facts and is defined with rules, making it much less complex. When the aspects of psychology such as mood, temperament, personality etc are brought into context, the modeling domain becomes more complex. The CLARION is developed such that it does mimic most of these humans like behavior and patterns. CLARION is seen to have developed this advantage by drawing on quasi-laws pertaining to the categorization, reasoning, decision-making models and induction.

澳洲靠谱代写:AI模型和架构应用

As Sun (2012) stated, “We can recall here, symbolic logic paradigm. Symbolic approach begins at the highest knowledge level: symbolic manipulation and language. The fundamental hypothesis of this approach is that the human brain is not only a material support capable of producing thinking processes. Symbolic models offer a complementary profile, which means that they obtain performance in doing the reasoning process, inferences and deductions, natural symbolic manipulation and their justification. Their representations are localized and explicit, and they are very easy to understand”.
“The psychological phenomena were captured within the Non-Action-Centered Subsystem (NACS) of CLARION. This work is a step in bridging the gap between artificial agents and psychologically realistic (brain/mind-inspired) models, and between mathematically simple cognitive models and general cognitive architectures. Hence, it serves as an approach towards building psychologically realistic cognitive agents that can start addressing the challenges above” .

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