论文标题
使用人工叙事理解（i）测试定量时空假设：从情节叙事中含义被视为特征景观
Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (I) : Bootstrapping Meaning from Episodic Narrative viewed as a Feature Landscape
论文作者
论文摘要
通过使用文本叙述作为测试用例，研究了在没有事先培训的情况下提取感官数据流中重要而有意义的部分的问题。这是一项关于从时空过程中提取概念及其在混合符号学习“人工智能”中的知识表示的更大研究的一部分。文本分析的大多数方法都广泛使用了人类对语言和语义的意识。在这项工作中，在不了解语义知识的情况下，对流进行解析，仅在不断变化的符号流中使用可测量的模式（大小和时间） - 作为事件“景观”。这是干涉法的一种形式。这项工作使用可在单个CPU上在几秒钟内运行的轻巧程序，研究语义时空假设的有效性，以提取概念作为过程不变性。然后，这种“语义前处理器”可以充当更复杂的基于图形的学习技术的前端。结果表明，我们认为对感官体验的重要和有趣的是，不仅基于更高的推理，而是基于简单的时空过程提示，这可能是一开始就在认知过程中引起认知处理的方式。
The problem of extracting important and meaningful parts of a sensory data stream, without prior training, is studied for symbolic sequences, by using textual narrative as a test case. This is part of a larger study concerning the extraction of concepts from spacetime processes, and their knowledge representations within hybrid symbolic-learning `Artificial Intelligence'. Most approaches to text analysis make extensive use of the evolved human sense of language and semantics. In this work, streams are parsed without knowledge of semantics, using only measurable patterns (size and time) within the changing stream of symbols -- as an event `landscape'. This is a form of interferometry. Using lightweight procedures that can be run in just a few seconds on a single CPU, this work studies the validity of the Semantic Spacetime Hypothesis, for the extraction of concepts as process invariants. This `semantic preprocessor' may then act as a front-end for more sophisticated long-term graph-based learning techniques. The results suggest that what we consider important and interesting about sensory experience is not solely based on higher reasoning, but on simple spacetime process cues, and this may be how cognitive processing is bootstrapped in the beginning.
