computability

简明释义

英[kəmˈpjuːtəbɪləti]美[kəmˈpjuːtəbɪlɪti]

n. 可计算性

英英释义

单词用法

同义词

反义词

例句

1.With the use of computability theory dividing whole-word coding into two parts: writing-input coding and computational coding, an method of none keyboard mapping for spelling language is proposed.

本文依据可计算性理论，提出了拼音文字非键盘映射编码方法，将整词编码分为输写码与计算码。

2.In general, questions of what can be computed by various machines are investigated in computability theory.

一般而言，计算性理论研究的问题是：什么是能够被各种机器计算的问题。

3.The model has clear mathematical and physical meaning and excellent computability. The efficiency of the method has been demostrated by both simulation and practice.

本方法数学、物理意义明确，可计算性强，无论电算、手算均具有很多优越性。

4.With the use of computability theory dividing whole-word coding into two parts: writing-input coding and computational coding, an method of none keyboard mapping for spelling language is proposed.

本文依据可计算性理论，提出了拼音文字非键盘映射编码方法，将整词编码分为输写码与计算码。

5.Experimental results show that scientifically controls the granularity of semantic depiction can enhance the computability of word senses, and improve the accuracy of word sense discrimination.

实验结果表明，科学控制词义刻画的粒度可以增强词义的可计算性，提高词义识别的精度。

6.The following table shows some of the classes of problems (or languages, or grammars) that are considered in computability theory (blue) and complexity theory (green).

下面的表格指出了在可计算性(蓝色)和复杂性理论(绿色)应当考虑的一些种类的问题。

7.This includes computability theory, computational complexity theory, and information theory.

这包括可计算性理论，计算复杂性理论，信息理论。

8.They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.

尔后课程中将可利用这些方法来设计与分析演算法、可计算理论、软体工程与电脑系统。

9.All these researches improve the computability of the language processing.

这些研究增强了语言处理的可计算性。

10.Researchers are exploring new models of computability 可计算性 that go beyond traditional Turing machines.

研究人员正在探索超越传统图灵机的新可计算性模型。

11.The study of computability 可计算性 helps us understand what problems can be solved by algorithms.

对可计算性的研究帮助我们理解哪些问题可以通过算法解决。

12.In computer science, computability 可计算性 is a fundamental concept that determines the limits of what can be computed.

在计算机科学中，可计算性是一个基本概念，它决定了可以计算的限制。

13.The Church-Turing thesis is a key principle in the theory of computability 可计算性.

教堂-图灵论题是可计算性理论中的一个关键原则。

14.Many real-world problems have been shown to be undecidable in terms of computability 可计算性.

许多现实世界的问题在可计算性方面被证明是不可判定的。

作文

In the realm of computer science, the concept of computability plays a fundamental role in understanding what can be computed by algorithms and machines. Computability refers to the ability of a problem to be solved by a computational process, typically involving a set of well-defined rules or procedures. This notion is crucial because it delineates the boundaries between problems that can be effectively solved and those that cannot, thus guiding researchers and practitioners in their work.One of the key figures in the study of computability is Alan Turing, who introduced the idea of the Turing machine. A Turing machine is a theoretical construct that helps us understand the limits of what can be computed. It operates on an infinite tape divided into cells, where each cell can hold a symbol. The machine reads these symbols and follows a set of rules to manipulate them, thereby performing computations. Turing's work laid the foundation for modern computer science and established the field of algorithmic theory.The significance of computability extends beyond theoretical implications; it has practical applications in various domains. For instance, in artificial intelligence, understanding which problems are computable allows developers to design algorithms that can effectively solve real-world issues. If a problem is proven to be non-computable, efforts can be redirected toward more tractable problems, optimizing resource allocation and time management.Moreover, the study of computability leads to intriguing philosophical questions about the nature of computation and intelligence. Can a machine ever truly replicate human thought? While machines can perform complex calculations and data processing at incredible speeds, they still operate within the constraints of computability. Certain aspects of human cognition, such as creativity and emotional understanding, may remain outside the reach of computational processes.Furthermore, computability is closely related to the concept of decidability, which refers to whether a problem can be definitively resolved through a computational procedure. Some problems, known as undecidable problems, cannot be solved by any algorithm. For example, the Halting Problem, which asks whether a given program will eventually halt or run indefinitely, is a classic example of such a problem. Understanding these limitations is essential for computer scientists as it informs the development of algorithms and software systems.As technology continues to advance, the implications of computability become increasingly relevant. With the rise of quantum computing, researchers are exploring new frontiers of computation that challenge traditional notions of computability. Quantum computers have the potential to solve certain problems much faster than classical computers, raising questions about the future landscape of computational capabilities.In conclusion, computability is a vital concept in computer science that encompasses both theoretical and practical dimensions. It helps define the limits of what can be computed and guides the development of algorithms and technologies. As we navigate the complexities of computation and artificial intelligence, a deep understanding of computability will remain essential for addressing the challenges and opportunities that lie ahead. The ongoing exploration of this concept not only enhances our technical knowledge but also prompts us to reflect on the very nature of intelligence and computation itself.

在计算机科学领域，可计算性的概念在理解算法和机器能够计算什么方面起着基础性作用。可计算性是指一个问题可以通过计算过程解决的能力，通常涉及一组明确定义的规则或程序。这一概念至关重要，因为它划定了可以有效解决的问题与无法解决的问题之间的界限，从而指导研究人员和从业者的工作。在可计算性研究中，阿兰·图灵是一个关键人物，他引入了图灵机的概念。图灵机是一种理论构造，帮助我们理解什么可以被计算。它在一条分成多个单元的无限长带子上操作，每个单元可以保存一个符号。机器读取这些符号，并遵循一组规则来操纵它们，从而执行计算。图灵的工作为现代计算机科学奠定了基础，并建立了算法理论领域。可计算性的重要性超越了理论意义；它在各个领域都有实际应用。例如，在人工智能领域，理解哪些问题是可计算的使开发人员能够设计出能够有效解决现实问题的算法。如果一个问题被证明是不可计算的，那么可以将精力转向更易处理的问题，从而优化资源分配和时间管理。此外，可计算性的研究引发了关于计算和智能本质的有趣哲学问题。机器能否真正复制人类思维？尽管机器可以以惊人的速度执行复杂的计算和数据处理，但它们仍然在可计算性的限制内运作。人类认知的某些方面，例如创造力和情感理解，可能仍然超出计算过程的范围。此外，可计算性与可判定性密切相关，后者指的是一个问题是否可以通过计算程序明确解决。一些问题，被称为不可判定问题，无法通过任何算法解决。例如，停机问题，即询问给定程序是否会最终停止或无限运行，是这样一个经典问题的例子。理解这些限制对计算机科学家至关重要，因为它为算法和软件系统的发展提供了信息。随着技术的不断进步，可计算性的影响变得越来越相关。随着量子计算的兴起，研究人员正在探索挑战传统可计算性概念的新计算前沿。量子计算机有潜力比经典计算机更快地解决某些问题，这引发了关于计算能力未来格局的问题。总之，可计算性是计算机科学中的一个重要概念，涵盖了理论和实践两个维度。它帮助定义了可以计算的界限，并指导算法和技术的发展。在我们应对计算和人工智能的复杂性时，深入理解可计算性将继续对解决未来面临的挑战和机遇至关重要。对这一概念的持续探索不仅增强了我们的技术知识，还促使我们反思智能和计算本身的本质。