Some New Words found in papers

本文探讨了计算机科学中空白状态的概念,特别是在自主代理的背景下,并解释了它如何应用于合成语言解析器的设计中。

In computer science, tabula rasa refers to the development of autonomous agents with a mechanism to reason and plan toward their goal, but no “built-in” knowledge-base of their environment. Thus they truly are a blank slate.

In reality autonomous agents possess an initial data-set or knowledge-base, but this cannot be immutable or it would hamper autonomy and heuristic ability.[citation needed] Even if the data-set is empty, it usually may be argued that there is a built-in bias in the reasoning and planning mechanisms.[citation needed] Either intentionally or unintentionally placed there by the human designer, it thus negates the true spirit of tabula rasa.[13]

A synthetic (programming) language parser (LR(1), LALR(1) or SLR(1), for example) could be considered a special case of a tabula rasa, as it is designed to accept any of a possibly infinite set of source language programs, within a single programming language, and to output either a good parse of the program, or a good machine language translation of the program, either of which represents a success, or, alternately, a failure, and nothing else. The “initial data-set” is a set of tables which are generally produced mechanically by a parser table generator, usually from a BNF representation of the source language, and represents a “table representation” of that single programming language.


Vice versa is a Latin phrase that means “the other way around”.

内容概要:本文围绕六自由度机械臂的人工神经网络(ANN)设计展开,重点研究了正向与逆向运动学求解、正向动力学控制以及基于拉格朗日-欧拉法推导逆向动力学方程,并通过Matlab代码实现相关算法。文章结合理论推导与仿真实践,利用人工神经网络对复杂的非线性关系进行建模与逼近,提升机械臂运动控制的精度与效率。同时涵盖了路径规划中的RRT算法与B样条优化方法,形成从运动学到动力学再到轨迹优化的完整技术链条。; 适合人群:具备一定机器人学、自动控制理论基础,熟悉Matlab编程,从事智能控制、机器人控制、运动学六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)建模等相关方向的研究生、科研人员及工程技术人员。; 使用场景及目标:①掌握机械臂正/逆运动学的数学建模与ANN求解方法;②理解拉格朗日-欧拉法在动力学建模中的应用;③实现基于神经网络的动力学补偿与高精度轨迹跟踪控制;④结合RRT与B样条完成平滑路径规划与优化。; 阅读建议:建议读者结合Matlab代码动手实践,先从运动学建模入手,逐步深入动力学分析与神经网络训练,注重理论推导与仿真实验的结合,以充分理解机械臂控制系统的设计流程与优化策略。
To find more information on a specific topic, consider the following methods: Utilize search engines like Google, Bing, or Yahoo. These platforms allow you to enter keywords related to your query and provide a wide range of results from websites, articles, and blogs. Visit online encyclopedias such as Wikipedia for general knowledge or specialized encyclopedias for niche subjects. These resources often contain comprehensive overviews and references for further reading. Explore academic databases like JSTOR, PubMed, or IEEE Xplore if you are looking for scholarly articles, research papers, or technical documents. Access might require a subscription or affiliation with an educational institution. Check out official websites of organizations, government bodies, or institutions that specialize in the area of interest. They frequently offer detailed reports, statistics, and updates directly from authoritative sources. Consult digital libraries such as Project Gutenberg or Internet Archive for access to free eBooks, historical texts, and multimedia content covering various topics. Join forums, discussion boards, or social media groups dedicated to the subject matter. Engaging with communities can lead to discovering new resources and gaining insights from experts or enthusiasts. ```python # Example of searching programmatically using Python (not part of the answer but for reference) import requests def search(query): response = requests.get(f"https://www.googleapis.com/customsearch/v1?key=YOUR_API_KEY&cx=YOUR_CX&q={query}") return response.json() result = search("example topic") ```
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