人工智能和专家系统技术在性能评价中的应用

本文介绍了人工智能和专家系统技术在计算机性能评价领域的应用,尤其是模拟、配置两个方面部分实验系统的主要特征。这些实践表明人工智能和专家系统技术在辅助问题求解和系统模型构造等方面的价值和优越性。人工智能和专家系统技术必将对性能评价的发展产生巨大的影响。     

基于Prolog的专家系统研究与应用

随着计算机与网络通信技术的迅猛发展,特别是互联网的大规模普及,围绕人工智能与专家系统的研究和应用开发也迎来一个蓬勃发展的新时期。Prolog语言是人工智能与专家系统领域最著名的逻辑程序设计语言。本文基于Prolog平台上,探讨了专家系统建造的原理和应用。     

基于Prolog的专家系统研究与应用

随着计算机与网络通信技术的迅猛发展,特别是互联网的大规模普及,围绕人工智能与专家系统的研究和应用开发也迎来一个蓬勃发展的新时期。Prolog语言是人工智能与专家系统领域最著名的逻辑程序设计语言。本文基于Prolog平台上,探讨了专家系统建造的原理和应用。     

工程管理中专家系统的应用与发展

本文结合工程管理领域,从可用性和可达性的观点出发,在论述工程管理发展中的专家系统应用、专家系统开发中的人工智能技术以及专家系统中人工智能的应用与发展的基础上,探讨了工程管理中专家系统的应用与发展。     

基于Prolog的专家系统研究与应用

随着计算机与网络通信技术的迅猛发展,特别是互联网的大规模普及,围绕人工智能与专家系统的研究和应用开发也迎来一个蓬勃发展的新时期.Prolog语言是人工智能与专家系统领域最著名的逻辑程序设计语言.本文基于Prolog平台上,探讨了专家系统建造的原理和应用.     

专家系统开发平台的对比

在人工智能领域中,专家系统是应用最广泛、最成功的领域之一。专家系统的开发技术水平直接影响专家系统的开发效率,甚至决定着一个专家系统的最终命运。本文首先介绍了专家系统的各种软件环境,同时分析用各种平台开发专家系统的优点和特点,最后提出未来的软件发展方向。     

浅谈专家系统现状与开发

专家系统是人工智能领域最重要的应用之一。介绍了专家系统的含义与结构,对专家系统的研究与应用现状、开发方法进行了论述,并提出了新型专家系统的发展趋势与特点,指出专家系统重大的社会和经济价值。     

专家系统开发平台的对比

在人工智能领域中，专家系统是应用最广泛、最成功的领域之一。专家系统的开发技术水平直接影响专家系统的开发效率，甚至决定着一个专家系统的最终命运。本文首先介绍了专家系统的各种软件环境，同时分析用各种平台开发专家系统的优点和特点，最后提出未来的软件发展方向。     

基于规则的鸟类识别专家系统的设计与实现

本文首先介绍人工智能领域中专家系统的概念及特点,以及基于规则的专家系统的模型与结构,其次以建立一个鸟奚识别专家系统为例来说明基于规则的专家系统的详细设计与实现。旨在说明构造专家系统的步骤与方法,软件设计采用人工智能语言Prolog编程实现。     

一个沉淀反应智能数据库

关于人工智能在化学领域中的应用,国外有用 LISP 语言编成专家系统的报导,国内学者提出了 FORTRAN 语言设计的定性分析专家系统,但将人工智能新型高级PROLOG 语言用于分析化学的工作尚未见报导。     

人工智能伦理体系:基础架构与关键问题

全球范围内针对人工智能伦理准则的讨论已达成基本共识。在此基础上，本文进一步研究4个关键问题:人工智能伦理体系的运行机制问题、人工智能伦理准则的场景落地问题、人工智能伦理风险的预测判别问题，以及人工智能伦理对重大社会问题综合创新的支撑机制问题。这些问题超越了人工智能伦理准则的范围，却是一种完整、有效的人工智能伦理体系所必须解答的。本文的主要贡献是对这4个问题提出一套建议方案。     

人工智能框架技术趋势研究

作为人工智能开发环节中的基础工具,人工智能框架承担着AI技术生态中操作系统的角色,是AI学术创新与产业商业化的重要载体。随着其重要性的不断突显,人工智能框架已经成为人工智能产业创新的焦点之一,引起了学术界、产业界的高度重视。在此背景下,本文从人工智能框架的概念内涵、价值意义入手,梳理人工智能框架演进历程,总结当前人工智能框架技术体系,最后研判得出人工智能框架技术发展的“四泛”趋势,即泛开发、泛设备、泛场景、泛工程。     

分布式人工智能与多智能体系统研究

智能体理论是一个发展很快的前沿领域。为了解决复杂问题出现了分布式人工智能,多个智能体的协作正好符合分布式人工智能的要求,因此出现了多智能体系统。文中介绍了分布式人工智能的特点,并着重介绍了分布式人工智能的一种分类———多智能体系统。多智能体系统也是分布式人工智能的一种有效的解决方法,同时分布式人工智能又推动了多智能体的发展。     

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.     

Thinking Inside the Box: Controlling and Using an Oracle AI

There is no strong reason to believe that human-level intelligence represents an upper limit of the capacity of artificial intelligence, should it be realized. This poses serious safety issues, since a superintelligent system would have great power to direct the future according to its possibly flawed motivation system. Solving this issue in general has proven to be considerably harder than expected. This paper looks at one particular approach, Oracle AI. An Oracle AI is an AI that does not act in the world except by answering questions. Even this narrow approach presents considerable challenges. In this paper, we analyse and critique various methods of controlling the AI. In general an Oracle AI might be safer than unrestricted AI, but still remains potentially dangerous.     

机制主义人工智能理论——一种通用的人工智能理论

现行人工智能研究取得了许多进展,但存在“深度上浅层化、广度上碎片化和体系上封闭化”的重要缺陷。这不是改进算法或者提高硬件性能所能解决的问题,而是要在科学观方法论上寻找根源。本文依据“科学观→方法论→研究模型→研究途径→基本概念→基本原理”这个顶天立地的研究纲领,总结了信息科学的科学观,提炼了信息生态方法论;在新的科学观和方法论指导下构筑了体现智能生长全过程的研究模型,发现了智能生长的共性机制,确立了机制主义研究途径,进而澄清和匡正了信息（特别是语义信息）、感知、知识、认知、基础意识、情感、理智、综合决策等一系列基础概念,总结了实现信息－知识－智能转换的一组基本原理,创建了机制主义人工智能理论。而且证明了:长期三分而立的结构主义（人工神经网络）、功能主义（专家系统）、行为主义（感知动作系统）三大人工智能理论可在机制主义人工智能理论框架内实现和谐统一;机制主义是生成基础意识、情感、理智三位一体高等人工智能的科学途径;机制主义人工智能理论是通用型的人工智能理论。     

Development of an Internet-based intelligent design support system for rolling element bearings

This paper presents a novel approach to developing an intelligent agile design system for rolling bearings based on artificial intelligence (AI), Internet and Web technologies and expertise. The underlying philosophy of the approach is to use AI technology and Web-based design support systems as smart tools from which design customers can rapidly and responsively access the systems' built-in design expertise. The approach is described in detail with a novel AI model and system implementation issues. The major issues in implementing the approach are discussed with particular reference to using AI technologies, network programming, client-server technology and open computing of bearing design and manufacturing requirements.     

Review of State-of-the-Art in Deep Learning Artificial Intelligence

The current state-of-the-art in Deep Learning (DL) based artificial intelligence (AI) is reviewed. A special emphasis is made to compare the level of a concrete AI system with human abilities to show what remains to be done to achieve human level AI. Several estimates are proposed for comparison of the current “intellectual level” of AI systems with the human level. Among them is relation of Shannon’s estimate for lower bound on human word perplexity to recent progress in natural language AI modeling. Relations between the operation of DL constructions and principles of live neural information processing are discussed. The problem of AI risks and benefits is also reviewed based on arguments from both sides.     

Principles of intelligent learning systems design

One of the possible system approaches to the construction of artificial intelligence (AI) systems is described in this paper. The approach integrates three well-known AI trends: heuristic programming, structural modelling and simulated evolution. The structure of an experimental learning system ELS Y is described. The system is designed in accordance with the proposed principles. The main feature of the system is a dynamic generation of AIS architectures on the basis of means-ends analysis method. An AIS architecture can be subjected to mutation in order to obtain a more intelligent one. Every AI system is capable of self-organization on two levels: the first one formed with an associative computing memory, and the second one with arrangement of knowledge structures.