Advanced Idea ,Anticipating Incomparability on Artificial Intelligence.
Artificial intelligence(AI) is the field of engineering that builds systems ,primarily computer systems ,to perform tasks requiring intelligence .This field of research has often set itself ambitious goals, seeking to build machines that can outlook humans in particular domains of skill and knowledge ,and has achieved some success in this.The key aspects of intelligence around which AI research is usually focused include expert system ,industrial robotics,systems and languages language understanding ,learning ,and game playing,etc.
An expert system is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally requires human expertise . Typically,the user interacts with an expert system in a consultation dialogue,just as he would interact with a human who had some type of expertise,explaining his problem,performing suggested tests,and asking questions about proposed solutions. Current experimental systems have achieved high levels of performance in consultation tasks like chemical and geological data analysis,computer system configuration,structural engineering,and even medical diagnosis.Expert systems can be viewed as intermediaries between human experts,who interact with the systems in knowledge acquisition mode ,and human users who interact with the systems in consultation mode. Furthermore ,much research in this area of AI has focused on endowing these systems with the ability to explain their reasoning,both to make the consultation more acceptable to the user and to help the human expert find errors in the system´s reasoning when they occur.Here are the features of expert systems:
① Expert systems use knowledge rather than data to control the solution process.
② The know is encoded and maintained as an entity separated from
the control program.Furthermore,it is possible in some cases to use different knowledge bases with the same control programs to produce
different types of expert system.Such system are known as expert system
shells.
③ Expert systems are capable of explaining how a particular concl-
usion is reached,and why requested information is needed during a consultation.
④ Expert systems use symbolic representations for knowledge and
perform their inference through symbolic computations.
⑤ Expert systems often reason with metaknowledge.
An industrial robot is a general-purpose computer-controlled manipulator consisting of several rigid links connected in series by revolute or prismatic joints.Research in this field has looked at everything from the optimal movement of robot arms to methods of planning a sequence of actions to achieve a robot´s goals.Although more complex systems have been built,the thousands of robots that are being used today in industrial applications are simple devices that have been programmed to some repetitive task.Robots,when compared to humans,yield more consistent quality,more predictable output,and are more reliable.Robots has been used in industry since 1965.They are usually characterized by the design of the mechanical system.There are six recognizable robot configurations:
① Cartesian Robots:A robot whose main frame consist of three Linear axes.
② Gantry Robots:A Gantry robot is a type of artesian robot whose structure resembles a gantry.This structure is used to minimize deflection along each axis.
③ Cylindrical Robots:A cylindrical robot has two linear axes and one rotary axis.
④ Spherical Robots:A spherical robot has one linear axis and two rotary axes.Spherical Robots are used in a variety of industrial tasks such as welding and material handling.
⑤ Articulated Robots:An articulated robot has three rotational axes connecting three rigid links and a base.
⑥ Scara Robots:One style of robot that has recently become quite popular is a combination of the articulated arm and the cylindrical robot.The robot has more than three axes and is widely used in electronic assembly.
Computer-systems ideas like time-sharing,list processing,and interactive debugging were developed in the AI research environment.Specialized programming languages and systems,with features designed to facilitate deduction,robot manipulation,cognitive modeling,and so on, have often been rich sources of new ideas.Most recently,reveral knowledge-representation languages,computer languages for encoding knowledge and reasoning methods as data structure and procedures,which have been developed in the last few years to explore a variety of ideas about how to build reasoning programs.
The first big success in AI was programs that could solve puzzles and play games like chess.Techniques like looking ahead several moves and dividing difficult problems into easier sub-problems evolved into the fundamental AI techniques of search and problem reduction.Today´s programs play championship-level checkers and backgammon,as well as very
good chess.Another problem-solving program that integrates mathematical formulates symbolically has attained very high levels of performance and is being used by scientists and engineers.Some programs can even improve their performance with experience.
As discussed above,the open questions in this area involve capabilities that human players have but cannot articulate,like the chess master´s ability to see the board configuration in terms of meaningful patterns.Another basic open question involves the original conceptualization of a problem,called in AI the choice of problem representation.Humans often solve a problem by finding a way of thinking about it that makes the solution easy-AI problems,so far,must be told how to think about the problems they solve.
Closely related to problem and puzzle solving was early work on logical deduction.Programs were developed that could prove assertions by manipulating a database of facts,each represented by discrete data structures just as they are represented by discrete formulas in mathematical logic.These methods,unlike many other AI techniques,could be shown
to be complete and consistent.That is,so long as the original facts were correct,the programs could prove all theorems that followed from the facts,and only those theorems.
Logical reasoning has been one of the most persistently investigated subareas of AI research.Of particular interest are the problems of finding ways of focusing on only the relevant facts of a large database and of keeping track of the justifications for beliefs and updating them when new information arrives.
The domain of language understanding was also investigated by early AI researchers and has consistently attracted interest.Programs have been written that answer questions posed in English from an internal database,that translate sentences from one language to another,that follow instruction given in English,and that acquire knowledge by reading textual material and building an internal database.Some programs have even achieved limited success in interpreting instructions spoken into a microphone instead of typed into the computer.Although these language systems are not nearly as good as people are at any of these tasks,they are adequate for some applications.Early successes with programs that answered simple queries and followed simple directions,and early failures at machine translation,have resulted in a sweeping change in the whole AI approach to language.The principal themes of current language-understanding research are the importance of vase amounts of general,commonsense world knowledge and the role of expectations,based on the subject matter and the conversational situation,in interpreting sentences.
Learning has remained a challenging area for AI.Certainly one of the most salient and significant aspects of human intelligence is the ability to learn.This is a good example of cognitive behavior that is so poorly understood that vary little progress has been made in achieving it in AI system.There have been several interesting attempts,including programs learn from examples,form their own performance,and from being told.An expert system may perform extensive and costly computations to solve a problem.Most expert systems are hindered by the inflexibility of their problem-solving strategies and the difficulty of modifying large amounts of code.The obvious solution to these problems is for programs to learn on their own,either from experience,analogy,and examples or by being told what to do.
Much of the early research in state space search was done using common board games such as checkers,chess,and the 15-puzzle.In addition to their inherent intellectual appeal,board games have certain properties that make them ideal subjects for this early work.Most games are played using a well-defined set of rules,this makes it easy to generate the search space and frees the researcher from many of the ambiguities and complexities inherent in less structured problems.The board configurations used in playing these games are easily represented on a computer,requiring none of the complex formalisms.
We have attempted to define artificial intelligence through discussion of its major areas of research and application.In spite of the variety of problems addressed in artificial intelligence research,a number of important features emerge that seem common to all divisions of the field,these include:
① The use of computers to do reasoning,learning,or some other form of intelligence.
② A focus on problems that do not respond to algorithmic solutions.This underlies the reliance on heuristic search as an AI problem-solving technique.
③ Reasoning about the significant qualitative features of a situation.
④ An attempt to deal with issues of semantic meaning as well as syntactic form.
⑤ The use of large amounts of domain-specific knowledge in solving problems.This is the basis of expert systems.
Artificial intelligence(AI) is the field of engineering that builds systems,primarily computer systems,to perform tasks requiring intelligence .This field of research has often set itself ambitious goals,seeking to build machines that can outlook humans in particular domains of skill and knowledge,and has achieved some success in this.The key aspects of intelligence around which AI research is usually focused include expert systems,industrial robotics,systems and languages,language understanding,learning,and game playing,machine translation,etc.
人工智能
先进的想法不断注入到人工智能的发展过程中，使其最新理念无与伦比。
人工智能是一个构建系统的工程领域，主要用来构筑计算机系统，从而完成那些智能化工作。这个研究领域常常树立野心勃勃的目标，以寻觅来制造出一些拥有人类特定技能和知识的机器，并且已经获得了一些成功的案例。人工智能研究常常聚焦于专家系统，工业机器人，系统与语言，语言理解，自学习，智能游戏等等。
专家系统
专家系统是这样一组程序，它们操纵那些表示为代码的知识来解决一些需要人类专长的某些特定领域的问题。典型地，用户在向专家系统请教时，就像是在请教一个有某方面专长的人，这个专家能够解释问题，对建议进行检测，并对解决方案进行质疑。在化学和地址学的数据分析，计算机系统结构，结构工程，甚至在医疗诊断方面，当前实验性的专家系统都达到了高水平。专家系统可以看作一些专家们的仲裁者，以知识获取模式工作，而用户是以请教模式同系统进行交互。并且，在人工智能领域的研究已经聚焦于展现系统进行推理的过程，从而让用户心悦诚服接受建议，或者帮助用户专家发现系统推理时的错误。以下是专家系统的特性。
①专家系统是利用知识而并非数据来控制解决进程。
②知识转化成了代码，并被作为一个区别于控制程序的实体。而且，在一些情况下将不同的知识运用于同一个控制程序会产生不同类型的专家系统。这些系统被誉为专家系统外壳。
③专家系统有能力解释一些特定的结论是如何形成的，并且在推理过程中需要哪些信息。
④专家系统利用符号代表知识，并利用符号计算来进行推理论证。
⑤专家系统经常利用元知识进行推理。
工业机器人
工业机器人是广泛使用的由计算机控制的通过外卷的，或棱镜似的连接结合起来的操作员。为了达到一个工业机器人的目标，这个领域的研究集中于设计一系列的运动来达到最佳的行动方案。虽然工业机器人需要更复杂的系统，成千上万的机器人已经应用于工业领域，它们都是一些简单的经过编程的装置，主要从事一些重复性工作。机器人和人类相比，工作质量好，稳定性强，可靠性高。机器人从1965年进入工业领域，它们具有机械系统的设计特征。以下是6种公认的工业机器人结构：
①笛卡儿式机器人：一种主框架由三根直线轴组成的机器人。
②桶架式机器人：桶架式机器人是一种喷水井机器人，它的结构组成了一个桶架。这个结构用来减少每个轴的倾斜度。
③柱面机器人：柱面坐标式机器人有两根直线轴和一个旋转轴。
④球式机器人：球式机器人有一根直线轴和两个旋转轴。球式机器人被应用于定位焊接和材料搬运之类的工业应用上。
⑤挂接式机器人：挂接式机器人有三根直线轴连着三个节点和一个基座。
⑥斯凯瑞机器人:一种最近变的非常流行的机器人，它是由有关节的手臂组成的圆柱体机器人。这种机器人有多于三根的直线轴，并被广泛应用于电子组装行业。
系统与语言
人工智能发展了计算机系统方面的一些理念，如：时间分配，编目处理，交互式调试，等等。专用于编程的系统与语言已经成为丰富思想的源泉，因为其包含了优化演绎，机器人操作，认知模型等等的新特性。特别是最近以来，一些具备知识表示能力的计算机语言已经得到进一步的发展，它们能够将知识转化为代码，将推理方法表示为数据结构。这些计算机语言的发展已经促进了关于如何构建推理机的新思想的萌发。
问题求解
人工智能所取得的首次成功是解决了迷宫和棋类游戏的问题。能提前预料几步的前瞻技术和将复杂问题划分为容易解决的子问题的技术已经卷入并促进了人工智能中最基本的搜索与问题优化技术的发展。当今的智能程序已经能够在西洋双陆棋等一些很好的棋类游戏中发挥世界冠军级的水平。另外一个整合
数学理论的问题求解领域也已经达到了很高的水准，并被科学家和工程师广泛使用。其中有些程序甚至能够通过经验积累来不断提高水平。
像上面所讨论的那样，在此领域都涉及到了人类的本领，但是却不能进行关联，比如有些老练的棋手有根据丰富的前景模式通观全局的本领。另外一个开放式的问题涉及到将一个待求解的问题概念化，在人工智能领域被称为问题表现的选择。人类经常利用求解问题中简单的方法来处理问题，因此，人工智能程序，到目前为止，应该被告知怎样去思考它们所要解决的问题。
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