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arXiv 1209.4290cs.AI
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普遍智力的认知偏差

Cognitive Bias for Universal Algorithmic Intelligence

AIDEUS

Alexey Potapov, Sergey Rodionov, Andrew Myasnikov, Galymzhan Begimov

摘要:

现有的理论上通用的算法智能模型在实际中是不可实现的。更实用的方法来人工智能的一般是基于认知架构,这是,然而,非通用的意义上,他们可以构建和使用的环境模型,只能从图灵不完全模型空间。我们认为,通往真正的AGI的道路在于弥合这两种方法之间的差距。这是可能的,如果一个人认为认知功能是一种“认知偏见”(先验和搜索偏好),应该纳入通用算法智能的模型,而不违反其普遍性。早期报道的结果适合这种方法和它的整体可行性进行了讨论的感知,规划,知识表示,注意,心理理论,语言,和其他一些人的例子。

英文摘要:

Existing theoretical universal algorithmic intelligence models are not practically realizable. More pragmatic approach to artificial general intelligence is based on cognitive architectures, which are, however, non-universal in sense that they can construct and use models of the environment only from Turing-incomplete model spaces. We believe that the way to the real AGI consists in bridging the gap between these two approaches. This is possible if one considers cognitive functions as a "cognitive bias" (priors and search heuristics) that should be incorporated into the models of universal algorithmic intelligence without violating their universality. Earlier reported results suiting this approach and its overall feasibility are discussed on the example of perception, planning, knowledge representation, attention, theory of mind, language, and some others.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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