中小学人工智能图灵测试体验式教学活动设计研究

本文梳理了国内外人工智能教育在中小学的应用,以及在人工智能教学中融合图灵测试的现状,指出了将图灵测试引入中小学人工智能教学的价值,并基于此设计了基于图灵测试的中小学人工智能教学活动模式,最后介绍了相关的实施案例,希望能为图灵测试在中小学人工智能课程活动中的应用提供有益参考。     

人工智能赋能无人系统

让机器具备人一样的智能,赋予机器思考和推理的能力,是人类最伟大的梦想之一.早在 1948 年,图灵在题为《智能机器》的论文里,第一次勾勒出了人工智能领域的轮廓.随后,他设计了一个被后人称为图灵测试的思想实验.在很长一段时间内,图灵测试都是较为公认的人工智能判断标准.自图灵测试提出以来,人工智能有了很大发展,我们正处于人...     

镜像图灵测试:古诗的机器识别

古诗伴随着中华文化的历史进程不断发展,有着数千年的灿烂历史,古诗将丰富的情感、有内涵的灵魂和生动的形式完美结合,表现出了中华民族语言的力量."自然语言处理是人工智能皇冠上的明珠",用机器生成语言是机器智慧的核心体现,对机器的语言进行测试是图灵测试的重要内容,用机器生成的中国古代诗词已经可以初步通过图灵测试,在普通人面前...     

人工智能对网络安全及教育影响趋势的研究

人工智能技术方兴未艾,从图灵测试到塞尔的"中文屋理论",人工智能从认知、技术发展、实现路径等方面还在不断变化.人类从自身智能来理解、实现机器智能,从不同角度复制人类智能已经得到初步成果.机器人从事生产、勘测、服务产业等,带来便利和安全性.从人工智能发展趋势,讨论了人工智能时代应该维护网络安全,提出了利用人工智能技术促进...     

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。     

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。     

图灵测试能证实人工智能的存在吗

图灵测试是机器是否具有智能的重要标志之一,它最早被英国科学家、人工智能主要奠基人图灵（1912～1954）提出。在信息技术必修教材中的人工智能部分不但被提及,而且给出了一些可以对话的机器人,让学生通过与计算机的对话并寻找其中的破绽来感受人工智能的存在,     

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、...     

少儿图灵测试回顾

报告了关于少儿图灵测试(CTT)的一项研究工作.研究区别于其他人的主要之处是该测试程序是基于知识的,它依靠一个海量常识知识库的支持.给出了作者研究少儿图灵测试的动机、设计、技术、实验结果和平台(包括一个知识引擎和一个会话引擎).最后给出了关于少儿图灵测试的几点研究结论和思考.     

Java游戏开发Step by Step最终篇——游戏中的人工智能实现

提到人工智能,就不能不说说我非常景仰的人工智能之父——图灵。他相信只要模拟人类大脑的思维就可以做出一台可以思考的机器,他于1950年提出了著名的“图灵测试”,测试是让人类考官通过键盘向一个人和一个机器发问,这个考官不知道他现在问的是人还是机器。如果在经过一定时间的提问以后,这位人类考官不能确定谁是人谁是机器,那这个机器就有智力了。这个测试现在想起来十分简单,可是伟大的思想就源于这种简单的事物之中。     

The Status and Future of the Turing Test

The standard interpretation of the imitation game is defended over the rival gender interpretation though it is noted that Turing himself proposed several variations of his imitation game. The Turing test is then justified as an inductive test not as an operational definition as commonly suggested. Turing's famous prediction about his test being passed at the 70% level is disconfirmed by the results of the Loebner 2000 contest and the absence of any serious Turing test competitors from AI on the horizon. But, reports of the death of the Turing test and AI are premature. AI continues to flourish and the test continues to play an important philosophical role in AI. Intelligence attribution, methodological, and visionary arguments are given in defense of a continuing role for the Turing test. With regard to Turing's predictions one is disconfirmed, one is confirmed, but another is still outstanding.     

Anthropomorphism and AI: Turingʼs much misunderstood imitation game

The widespread tendency, even within AI, to anthropomorphize machines makes it easier to convince us of their intelligence. How can any putative demonstration of intelligence in machines be trusted if the AI researcher readily succumbs to make-believe? This is (what I shall call) the forensic problem of anthropomorphism. I argue that the Turing test provides a solution. This paper illustrates the phenomenon of misplaced anthropomorphism and presents a new perspective on Turing?s imitation game. It also examines the role of the Turing test in relation to the current dispute between human-level AI and ‘mindless intelligence’.     

Attractor Spaces as Modules: A Semi-Eliminative Reduction of Symbolic AI to Dynamic Systems Theory

I propose a semi-eliminative reduction of Fodors concept of module to the concept of attractor basin which is used in Cognitive Dynamic Systems Theory (DST). I show how attractor basins perform the same explanatory function as modules in several DST based research program. Attractor basins in some organic dynamic systems have even been able to perform cognitive functions which are equivalent to the If/Then/Else loop in the computer language LISP. I suggest directions for future research programs which could find similar equivalencies between organic dynamic systems and other cognitive functions. This type of research could help us discover how (and/or if) it is possible to use Dynamic Systems Theory to more accurately model the cognitive functions that are now being modeled by subroutines in Symbolic AI computer models. If such a reduction of subroutines to basins of attraction is possible, it could free AI from the limitations that prompted Fodor to say that it was impossible to model certain higher level cognitive functions.     

Three fundamental misconceptions of Artificial Intelligence

In discussions on the limitations of Artificial Intelligence (AI), there are three major misconceptions, identifying an AI system with an axiomatic system, a Turing machine, or a system with a model-theoretic semantics. Though these three notions can be used to describe a computer system for certain purposes, they are not always the proper theoretical notions when an AI system is under consideration. These misconceptions are not only the basis of many criticisms of AI from the outside, but also responsible for many problems within AI research. This paper analyses these misconceptions, and points out the common root of them: treating empirical reasoning as mathematical reasoning. Finally, an example intelligent system called NARS is introduced, which is neither an axiomatic system nor a Turing machine in its problem-solving process, and does not use model-theoretic semantics, but is still implementable in an ordinary computer.     

Turing Test Considered Mostly Harmless

Turing’s landmark paper on computing machinery and intelligence is multifaceted and has an underemphasized ethical dimension. Turing’s notion of “intelligence” and “thinking” was far more encompassing than the common anthropocentric view may suggest. We discuss a number of open and underrated problems that the common interpretation of the Turing test as a test of machine intelligence entails. We suggest that a more meaningful question than “Can machines think?” is whether modern computing machinery can amplify human intelligence. We cite examples ranging from traditional silicon-based environments to carbon-based, living organisms in order to illustrate that this kind of intelligence amplification is indeed happening today. We conclude that in its interpretation as a test of machine intelligence, the Turing test may indeed be harmful for artificial intelligence (AI); in its wider interpretation, however, it remains an inspiring source for philosophy and AI alike.     

Artificial Intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters

Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.     

Index sets and universal numberings

This paper studies the Turing degrees of various properties defined for universal numberings, that is, for numberings which list all partial-recursive functions. In particular properties relating to the domain of the corresponding functions are investigated like the set DEQ of all pairs of indices of functions with the same domain, the set DMIN of all minimal indices of sets and DMIN^? of all indices which are minimal with respect to equality of the domain modulo finitely many differences. A partial solution to a question of Schaefer is obtained by showing that for every universal numbering with the Kolmogorov property, the set DMIN^? is Turing equivalent to the double jump of the halting problem. Furthermore, it is shown that the join of DEQ and the halting problem is Turing equivalent to the jump of the halting problem and that there are numberings for which DEQ itself has 1-generic Turing degree.     

On the computational power of BlenX

We present some decidability and undecidability results for subsets of the BlenX Language, a process-calculi-based programming language developed for modelling biological processes. We show that for a core subset of the language (which considers only communication primitives) termination is decidable. Moreover, we prove that by adding either global priorities or events to this core language, we obtain Turing equivalent languages. The proof is through encodings of Random Access Machines (RAMs), a well-known Turing equivalent formalism, into our subsets of BlenX. All the encodings are shown to be correct.     

What Should Default Reasoning be,by Default?

This is a position paper concerning the role of empirical studies of human default reasoning in the formalization of AI theories of default reasoning. We note that AI motivates its theoretical enterprise by reference to human skill at default reasoning, but that the actual research does not make any use of this sort of information and instead relies on intuitions of individual investigators. We discuss two reasons theorists might not consider human performance relevant to formalizing default reasoning: (a) that intuitions are sufficient to describe a model, and (b) that human performance in this arena is irrelevant to a competence model of the phenomenon. We provide arguments against both these reasons. We then bring forward three further considerations against the use of intuitions in this arena: (a) it leads to an unawareness of predicate ambiguity, (b) it presumes an understanding of ordinary language statements of typicality, and (c) it is similar to discredited views in other fields. We advocate empirical investigation of the range of human phenomena that intuitively embody default reasoning. Gathering such information would provide data with which to generate formal default theories and against which to test the claims of proposed theories. Our position is that such data are the very phenomena that default theories are supposed to explain.     

Deploying embodied AI into virtual worlds

Unlike computer games where Non-Player-Character avatars are common, in most virtual worlds they are the exception. Deploying an embodied AI into a virtual world offers a unique opportunity to evaluate embodied AIs, and to develop them within an environment where human and computer are on almost equal terms. This paper presents an architecture being used for the deployment of chatbot driven avatars within the Second Life virtual world, looks at the challenges of deploying an AI within such a virtual world, the possible implications for the Turing Test, and identifies research directions for the future.