AI推理:从沉寂走向新生

推理在人工智能的创立中扮演了关键角色.哥德尔在他的不完全性定理证明中,建立了 一个对AI发端具有重大意义的中间结果:KN可表示的推理可以通过递归函数的计算实现.根据车赤-图灵论题,递归函数是图灵机可计算的.于是上述中间结果隐含着一个猜想:任何推理都是图灵机可计算的.在此基础上,1950年图灵提出了图灵假说:推理、决策、...     

图灵机:停机问题,0型语言

7.1 非形式的讨论 在这章,我们证明当为任意图灵机T提供一种编码并为句子x提供一种编码时,存在有这样的一种图灵机,它将模拟在以x提供给T时T的行为。我们称这种图灵机为通用图灵     

图灵:计算理论的奠基人

广义的信息理论包含计算理论和通信理论两大部分。计算理论指经典的可计算性理论,代表人物是英国科学家图灵;通信理论指经典的信息论,代表人物是美国科学家香农。本文介绍图灵的杰出贡献,一是建立了图灵机模型,奠定了可计算理论的基础;二是提出了图灵测试,阐述了机器智能的概念。他当之无愧地被誉为“计算机科学之父”。但是在他生活的时代,却完全没有这些赞誉。当时认为他不过是一位古怪的数学家、超前的哲学家、神秘的密码破译专家,没有人会想到他的思维能燃起信息时代的烈焰。童年的艰辛艾兰·图灵(Alan MathisonTuring,1912-1954年)于…     

数字孪生驱动的煤矿多元业务全局动态协同管控

煤矿作为典型的多业务协同的复杂生产系统,在动态化销量需求及不确定性生产环境的作用下,存在安全、生产、经营等业务协同性和联动性较差等问题。数字孪生技术可为实现煤矿全局业务系统数据融合、协同管控和智能联动提供技术保障。从专业业务内部的融合、专业业务之间的融合2个方面分析了煤矿多元业务的融合及协同管控,其中专业业务内部的融合包括安全监测类业务融合、生产协同类业务融合、经营类业务融合3个部分。构建了基于数字孪生的业务动态协同管控架构,该架构包括物理对象感知层、虚拟空间仿真层、协同管控决策层。提出了“决策算法前摄性静态规划+孪生模型预测性协同管控+实时数据动态性协同管控”三重驱动的煤矿全局业务动态协同管控模式：在孪生世界构建煤矿“安全-生产-经营”多元业务虚拟模型,进行安全保障、生产联动、经营管理等前摄性静态规划,制订最优的业务协同管控初始工作计划;采用数值仿真模拟方式,在信息世界中实现虚拟模型的预测性运行,当管控措施效果得到验证后,及时将决策指令下发到物理世界中,从而将扰动由事件发生后的被动处理变成事件发生前的主动管控,提升管控决策的有效性;当煤矿企业业务运行过程中出现孪生世界未预测到的扰动时...     

图灵机和图灵测试

阿兰?图灵为人工智能学的诞生做出了重大的贡献,本文介绍了图灵机和图灵测试,图灵机对计算机的结构、可实现性和局限性都产生了深远的影响,而图灵测试为机器能否思考的争论双方找到了一种公认的判决准则。     

协同智能计算系统——理论模型及其应用

本研究工作目的是揭示计算机数据与自然人知识两类信息处理方式基础之上派生的协同智能观及其指导下的协同智能计算系统的理论模型及其应用。它涉及的可验证方法,一方面,在n2矩阵范围内,以等价于2n的发散方式枚举和以等价于1/2n的收敛方式搜索两类基本算法可用作处理纯数字计算的任务,其特征是满足n的取值不影响计算效率的间接计算任务;另一方面,在与n2矩阵各个格子一一对应的范围对单音节汉字进行间接形式化处理,其特征不仅在于单音节字,即言,可间接计算,而且,还在于双音节和多音节的字组,即语,也可间接计算,同时,言和语的复用频率均可以且便于间接计算和统计。其结果是：不仅中文的自然语言理解的双重技术路线被揭示,而且,支配这类间接计算模型与间接形式化方法的信息基本定律假说也可被验证。最终可得出这样的结论,即：在前述两方面可验证的两种实证方法,远不仅仅是计算机数据信息处理方式与自然人知识信息处理方式这两类信息处理方式的简单相加,而是这两者合理分工、高度协作所产生的协同智能计算系统的理论模型或第三类信息处理方式及其应用,例如：国内外学术前沿的各类期刊及会议论文摘要、各种软件的常用问题解答以及帮助文件、协同智能计算系统用户个性化记录、自然语言的有限符号及其多样化组合或重复使用过程中蕴含的有限规则、等等各类双语信息的计算机辅助分析,该类云端计算主要服务对象是在创造性合作型生产式教研产学用各类活动中需要计算机辅助双语知识信息数据处理服务的客户     

图灵，电脑的思想领袖

图灵提出的图灵机概念,为研制通用计算机奠定了思想和理论基础。1954年6月,图灵因为吃了有毒的苹果而在家中死去,年仅42岁。图灵一生虽短,但是他可谓是电脑的先驱,正是他提出的图灵机,成为计算机的思想和理论基础。后人为纪念这位"计算机科学之父",在英国曼彻斯特的Sackville公园为他建造了一尊真人大小的青铜坐像,这尊塑像是2001年6月23日,也就是图灵89岁诞辰那天揭幕的。图灵去世     

企业离散式智能设备预测性维护综述

针对智能设备的大量使用且缺乏根据监测大数据进行故障自动分析、判断与处理的问题,研究了基于物联网技术、大数据技术、边云协同技术的智能设备预测性维护框架和模式.提出针对非智能设备安装传感器实现设备智能化的方法.指出边缘计算负责设备工况数据的实时采集、分析,可快速甄别设备故障并实时报警;云计算聚焦同类设备运行海量历史数据的挖掘和分析,形成故障自动预测分析和诊断模式并下载至智能边缘设备.在研究了模型驱动、数据驱动、概率统计驱动、数字孪生和概率数字孪生驱动等故障预测模式后,提出了采用数据驱动的多层级数据融合模式,为制定企业性智能设备维保方案提供借鉴作用.     

图灵机的行为学习*

图灵机计算时读写头在带子上的活动,包括移动及改写字符，称为圈灵机的行为．本文证明，图灵机的行为是可学习的．即存在一个学习过程，它能根据一个图灵机的行为序列，学到另一个也能产生同样行为序列的图灵机．     

间接计算模型和间接形式化方法

本文旨在从人机交互界面与协同计算程序结合而构成协同智能计算系统的角度,论述间接计算模型和间接形式化方法结合所支持的优化云计算技术原理。本文在系统回顾图灵可计算理论、克莱尼小字符串形式理论、冯诺依曼数字计算机体系结构和图灵人工智能判定假设等前人理论研究成果对主流数字计算机通用范式影响的基础之上,着重介绍了笔者设计的间接计算模型和大、小字符串兼容的问接形式化理论,并以中文信息数据为例介绍了协同智能计算系统原型的设计构想。其意义是该成果有利于从数据中心到知识中心优化云计算。     

A Note on Maxwell's Demon and Universal Computation

Abstract

An analogy between a Maxwellian Demon capable of regulating the passage of particles between two chambers and a Turing machine capable of manipulating a tape and its symbols, is made explicit. It is shown that a slightly modified Maxwell's Demon can simulate a Universal Turing Machine.     

Pi演算对图灵机的表达

为了研究Pi演算的表达能力,我们试着用它来表达图灵机。研究结果表明,只要对Pi演算进行一定的的扩展,即引入某些函数符号,允许对发送的名字作一定的运算,就可以用Pi演算的规约来表达图灵机的运算过程。     

Parallel Turing Machine,a Proposal

We have witnessed the tremendous momentum of the second spring of parallel computing in recent years. But, we should remember the low points of the field more than 20 years ago and review the lesson that has led to the question at that point whether “parallel computing will soon be relegated to the trash heap reserved for promising technologies that never quite make it” in an article entitled “the death of parallel computing” written by the late Ken Kennedy — a prominent leader of parallel computing in the world. Facing the new era of parallel computing, we should learn from the robust history of sequential computation in the past 60 years. We should study the foundation established by the model of Turing machine (1936) and its profound impact in this history. To this end, this paper examines the disappointing state of the work in parallel Turing machine models in the past 50 years of parallel computing research. Lacking a solid yet intuitive parallel Turing machine model will continue to be a serious challenge in the future parallel computing. Our paper presents an attempt to address this challenge by presenting a proposal of a parallel Turing machine model. We also discuss why we start our work in this paper from a parallel Turing machine model instead of other choices.     

On parallel Turing machines with multi-head control units

This paper deals with parallel Turing machines with multi-head control units on one or more tapes which can be considered as a generalization of cellular automata. We discuss the problem of finding an appropriate measure of space complexity. A definition is suggested which implies that the model is in the first machine class. It is shown that without loss of generality it suffices to consider only parallel Turing machines of certain normal forms.     

Design and simulation of quantum-dot cellular automata serial decimal pipelined processor based on Turing machine model

The quantum-dot cellular automata (QCA) nanoscale computer technology is promising to overcome the limits of the microelectronic CMOS technology. Because the leading role of QCA wires, the serial data transfer/processing is preferable. The financial, Internet of Things, and control computer applications require direct processing of decimal information without representation and conversion errors. Because a QCA wire can be considered as a virtual tape with written binary symbols, a special version of Turing machine model can be used for a QCA computer implementation. Design of a novel QCA serial decimal pipelined processor based on the Turing machine model is presented. The processor uses the run-time tape reconfiguration for arithmetic processing of decimal operands encoded in the 5-bit Johnson-Mobius code. The proposed design demonstrates significant hardware simplification.     

Some properties of four-dimensional parallel Turing machines

Informally, the parallel Turing machine (PTM) proposed by Wiedermann is a set of identical usual sequential Turing machines (STMs) cooperating on two common tapes: a storage tape and an input tape. Moreover, STMs which represent the individual processors of a parallel computer can multiply themselves in the course of computation. On the other hand, during the past 7 years or so, automata on a four-dimensional tape have been proposed as computational models of four-dimensional pattern processing, and several properties of such automata have been obtained. We proposed a four-dimensional parallel Turing machine (4-PTM), and dealt with a hardware-bounded 4-PTM in which each side-length of each input tape is equivalent. We believe that this machine is useful in measuring the parallel computational complexity of three-dimensional images. In this work, we continued the study of the 3-PTM, in which each side-length of each input tape is equivalent, and investigated some of its accepting powers.     

Other bodies,other minds: A machine incarnation of an old philosophical problem

Any attempt to explain the mind by building machines with minds must confront the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is “everything” a body with a mind can do? Turing's original “pen-pal” version of the Turing Test (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls instead for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body.     

From Symbol to ‘Symbol’, to Abstract Symbol: Response to Copeland and Shagrir on Turing-Machine Realism Versus Turing-Machine Purism

In their recent paper “Do Accelerating Turing Machines Compute the Uncomputable?” Copeland and Shagrir (Minds Mach 21:221–239, 2011) draw a distinction between a purist conception of Turing machines, according to which these machines are purely abstract, and Turing machine realism according to which Turing machines are spatio-temporal and causal “notional" machines. In the present response to that paper we concede the realistic aspects of Turing’s own presentation of his machines, pointed out by Copeland and Shagrir, but argue that Turing's treatment of symbols in the course of that presentation opens the door for later purist conceptions. Also, we argue that a purist conception of Turing machines (as well as other computational models) plays an important role not only in the analysis of the computational properties of Turing machines, but also in the philosophical debates over the nature of their realization.     

Reversibility in space-bounded computation

In this paper, we investigate how computing power of a space-bounded Turing machine (TM) is affected by reversibility and determinism. We show an irreversible deterministic TM (IDTM), and a reversible non-deterministic TM (RNTM) can be simulated by a reversible and deterministic TM (RDTM) that uses exactly the same numbers of storage tape symbols and storage tape squares. Thus, an RDTM has relatively high capability in spite of the constraints of reversibility and determinism. Here, we also discuss a space-bounded symmetric TM.     

Complexity Theory and Genetics: The Computational Power of Crossing Over

We study the computational power of systems where information is stored in independent strings and each computational step consists of exchanging information between randomly chosen pairs. To this end we introduce a population genetics model in which the operators of selection and inheritance are effectively computable (in polynomial time on probabilistic Turing machines). We show that such systems are as powerful as the usual models of parallel computations, namely they can simulate polynomial space computations in polynomially many steps. We also show that the model has the same power if the recombination rules for strings are very simple (context sensitive crossing over).