【摘要】：塞爾對(duì)強(qiáng)人工智能的批判實(shí)際上提出了這樣的問(wèn)題:如果沒(méi)有外部觀察者的解釋,形式符號(hào)系統(tǒng)的句法屬性或物理屬性是否能夠充分決定語(yǔ)義內(nèi)容?雖然塞爾給出了否定的答案,但這個(gè)問(wèn)題卻啟發(fā)了人工智能的研究者去研究物理系統(tǒng)內(nèi)的符號(hào)如何能夠自動(dòng)地獲得意義的問(wèn)題。哈納德把這個(gè)問(wèn)題概括為"符號(hào)奠基問(wèn)題"。為了解決這個(gè)問(wèn)題,目前大致有內(nèi)在奠基、因果奠基、指號(hào)學(xué)奠基和基于行動(dòng)的語(yǔ)義學(xué)等幾種主要方案,但它們的解法都存在不同程度的缺陷。如何將不同進(jìn)路的人工智能架構(gòu)糅合起來(lái),既免于各種進(jìn)路的缺點(diǎn)而又能發(fā)揮其優(yōu)勢(shì),以更好地解決符號(hào)奠基問(wèn)題,是人工智能哲學(xué)中的值得研究的方向。
[Abstract]:Searle's criticism of strong artificial intelligence actually raises the question: can the syntactic or physical attributes of the formal symbol system fully determine the semantic content without an outside observer's explanation? Although Searle gave a negative answer, this question has inspired researchers in artificial intelligence to study how symbols in physical systems can automatically acquire meaning. Harnard summed up the problem as "the groundbreaking of symbols." In order to solve this problem, there are several main schemes, such as intrinsic foundation, causal foundation, indexology foundation and action-based semantics, but their solutions have some defects. How to combine the artificial intelligence architecture with different approaches can not only avoid the shortcomings of various approaches but also play its advantages, so as to solve the problem of laying the foundation of symbols better, which is the direction worth studying in the philosophy of artificial intelligence.
【作者單位】： 華南師范大學(xué)公共管理學(xué)院;北京師范大學(xué)哲學(xué)學(xué)院;
【基金】：國(guó)家社科基金“認(rèn)知科學(xué)對(duì)當(dāng)代哲學(xué)的挑戰(zhàn)”(編號(hào)11AZD120) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助(編號(hào)SKZZB2015040)的階段性成果
【分類號(hào)】：TP18-02
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本文編號(hào)：2430969