人工智能与专家系统外文文献译文和原文
人工智能英文参考文献(最新120个)
人工智能是一门新兴的具有挑战力的学科。
自人工智能诞生以来,发展迅速,产生了许多分支。
诸如强化学习、模拟环境、智能硬件、机器学习等。
但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。
下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。
人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? 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Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. 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Telematics and Informatics,2020,51.[62]Ru-Xi Ding,Iván Palomares,Xueqing Wang,Guo-Rui Yang,Bingsheng Liu,Yucheng Dong,Enrique Herrera-Viedma,Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective[J]. Information Fusion,2020,59.[63]Abdulrhman H. Al-Jebrni,Brendan Chwyl,Xiao Yu Wang,Alexander Wong,Bechara J. Saab. AI-enabled remote and objective quantification of stress at scale[J]. Biomedical Signal Processing and Control,2020,59.[64]Gillian Thomas,Elizabeth Eisenhauer,Robert G. Bristow,Cai Grau,Coen Hurkmans,Piet Ost,Matthias Guckenberger,Eric Deutsch,Denis Lacombe,Damien C. Weber. The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report[J]. European Journal of Cancer,2020,131.[65]Muhammad Asif. Are QM models aligned with Industry 4.0? A perspective on current practices[J]. Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. 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大学人工智能英语教材翻译
大学人工智能英语教材翻译IntroductionIn recent years, artificial intelligence (AI) has become a ubiquitous presence in our lives, revolutionizing various industries and fields. To meet the growing demand for AI professionals, universities have started offering courses and developing textbooks on the subject. This article aims to translate key contents of a university-level AI English textbook into Chinese, providing students with a comprehensive resource to enhance their understanding of this rapidly evolving field.Chapter 1: Introduction to Artificial Intelligence人工智能简介Artificial intelligence, often referred to as AI, is a branch of computer science that focuses on the creation of intelligent machines capable of performing tasks that typically require human intelligence. AI can be divided into two categories: narrow AI, which is designed to perform a specific task, and general AI, which aims to replicate human-level intelligence across a wide range of domains.Chapter 2: Machine Learning机器学习Machine learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. It involves the development of algorithms and models that allow computers to analyze and interpret data, identify patterns, and make predictions or decisions basedon the observed information. Supervised learning, unsupervised learning, and reinforcement learning are the three main types of machine learning techniques.Chapter 3: Neural Networks神经网络Neural networks are a fundamental concept in AI. Inspired by the structure and function of the human brain, neural networks consist of interconnected nodes or artificial neurons. These networks learn from training data by adjusting the connections between nodes to optimize their performance. Deep learning, a subfield of AI, utilizes neural networks with multiple layers to solve complex problems and achieve higher accuracy in tasks such as image recognition and natural language processing.Chapter 4: Natural Language Processing自然语言处理Natural language processing (NLP) focuses on enabling computers to interact and understand human language in a natural and meaningful way. It involves the development of algorithms and models that can process, analyze, and generate human language, enabling tasks such as machine translation, sentiment analysis, and chatbot development. NLP plays a crucial role in bridging the gap between humans and AI systems.Chapter 5: Computer Vision计算机视觉Computer vision is an interdisciplinary field that deals with the extraction, analysis, and understanding of visual information from images or videos. Through the use of AI techniques, computers can recognize objects, detect and track motion, and perform tasks such as facial recognition and image classification. Computer vision has various applications, including autonomous vehicles, surveillance systems, and augmented reality.Chapter 6: Robotics and Artificial Intelligence机器人与人工智能The integration of AI and robotics has led to significant advancements in the field of robotics. AI-powered robots can perceive their environment, make autonomous decisions, and interact with humans and other robots effectively. This chapter explores the role of AI in robotics, discussing topics such as robot perception, robot control, and human-robot interaction.Chapter 7: Ethical and Social Implications of AI人工智能的伦理和社会影响As AI continues to advance, ethical considerations and potential societal impact become increasingly important. This chapter delves into the ethical dilemmas surrounding AI, including privacy concerns, biases in AI systems, and the impact of AI on employment and workforce. It emphasizes the need for responsible development and deployment of AI technologies, ensuring that they benefit humanity and uphold ethical standards.ConclusionIn conclusion, this article has provided a translated overview of key topics in a university-level AI English textbook. By familiarizing themselves with these concepts, students can deepen their understanding of artificial intelligence and its various applications. Moreover, this translation serves as a valuable resource for educators and researchers in the Chinese-speaking community who seek to expand their knowledge in this rapidly advancing field. With the continued development of AI, it is imperative to bridge language barriers and foster global collaboration in order to drive innovation and ensure responsible AI implementation.。
人工智能英文文献原文及译文
附件四英文文献原文Artificial Intelligence"Artificial intelligence" is a word was originally Dartmouth in 1956 to put forward. From then on, researchers have developed many theories and principles, the concept of artificial intelligence is also expands. Artificial intelligence is a challenging job of science, the person must know computer knowledge, psychology and philosophy. Artificial intelligence is included a wide range of science, it is composed of different fields, such as machine learning, computer vision, etc, on the whole, the research on artificial intelligence is one of the main goals of the machine can do some usually need to perform complex human intelligence. But in different times and different people in the "complex" understanding is different. Such as heavy science and engineering calculation was supposed to be the brain to undertake, now computer can not only complete this calculation, and faster than the human brain can more accurately, and thus the people no longer put this calculation is regarded as "the need to perform complex human intelligence, complex tasks" work is defined as the development of The Times and the progress of technology, artificial intelligence is the science of specific target and nature as The Times change and development. On the one hand it continues to gain new progress on the one hand, and turning to more meaningful, the more difficult the target. Current can be used to study the main material of artificial intelligence and artificial intelligence technology to realize the machine is a computer, the development history of artificial intelligence is computer science and technology and the development together. Besides the computer science and artificial intelligence also involves information, cybernetics, automation, bionics, biology, psychology, logic, linguistics, medicine and philosophy and multi-discipline. Artificial intelligence research include: knowledge representation, automatic reasoning and search method, machine learning and knowledge acquisition and processing of knowledge system, natural language processing, computer vision, intelligent robot, automatic program design, etc.Practical application of machine vision: fingerprint identification,face recognition, retina identification, iris identification, palm, expert system, intelligent identification, search, theorem proving game, automatic programming, and aerospace applications.Artificial intelligence is a subject categories, belong to the door edge discipline of natural science and social science.Involving scientific philosophy and cognitive science, mathematics, neurophysiological, psychology, computer science, information theory, cybernetics, not qualitative theory, bionics.The research category of natural language processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combined scheduling problem, perception, pattern recognition, logic design program, soft calculation, inaccurate and uncertainty, the management of artificial life, neural network, and complex system, human thinking mode of genetic algorithm.Applications of intelligent control, robotics, language and image understanding, genetic programming robot factory.Safety problemsArtificial intelligence is currently in the study, but some scholars think that letting computers have IQ is very dangerous, it may be against humanity. The hidden danger in many movie happened.The definition of artificial intelligenceDefinition of artificial intelligence can be divided into two parts, namely "artificial" or "intelligent". "Artificial" better understanding, also is controversial. Sometimes we will consider what people can make, or people have high degree of intelligence to create artificial intelligence, etc. But generally speaking, "artificial system" is usually significance of artificial system.What is the "smart", with many problems. This involves other such as consciousness, ego, thinking (including the unconscious thoughts etc. People only know of intelligence is one intelligent, this is the universal view of our own. But we are very limited understanding of the intelligence of the intelligent people constitute elements are necessary to find, so it is difficult to define what is "artificial" manufacturing "intelligent". So the artificial intelligence research often involved in the study of intelligent itself. Other about animal or other artificial intelligence system is widely considered to be related to the study of artificial intelligence.Artificial intelligence is currently in the computer field, the moreextensive attention. And in the robot, economic and political decisions, control system, simulation system application. In other areas, it also played an indispensable role.The famous American Stanford university professor nelson artificial intelligence research center of artificial intelligence under such a definition: "artificial intelligence about the knowledge of the subject is and how to represent knowledge -- how to gain knowledge and use of scientific knowledge. But another American MIT professor Winston thought: "artificial intelligence is how to make the computer to do what only can do intelligent work." These comments reflect the artificial intelligence discipline basic ideas and basic content. Namely artificial intelligence is the study of human intelligence activities, has certain law, research of artificial intelligence system, how to make the computer to complete before the intelligence needs to do work, also is to study how the application of computer hardware and software to simulate human some intelligent behavior of the basic theory, methods and techniques.Artificial intelligence is a branch of computer science, since the 1970s, known as one of the three technologies (space technology, energy technology, artificial intelligence). Also considered the 21st century (genetic engineering, nano science, artificial intelligence) is one of the three technologies. It is nearly three years it has been developed rapidly, and in many fields are widely applied, and have made great achievements, artificial intelligence has gradually become an independent branch, both in theory and practice are already becomes a system. Its research results are gradually integrated into people's lives, and create more happiness for mankind.Artificial intelligence is that the computer simulation research of some thinking process and intelligent behavior (such as study, reasoning, thinking, planning, etc.), including computer to realize intelligent principle, make similar to that of human intelligence, computer can achieve higher level of computer application. Artificial intelligence will involve the computer science, philosophy and linguistics, psychology, etc. That was almost natural science and social science disciplines, the scope of all already far beyond the scope of computer science and artificial intelligence and thinking science is the relationship between theory and practice, artificial intelligence is in the mode of thinking science technology application level, is one of its application. From the view of thinking, artificial intelligence is not limited to logicalthinking, want to consider the thinking in image, the inspiration of thought of artificial intelligence can promote the development of the breakthrough, mathematics are often thought of as a variety of basic science, mathematics and language, thought into fields, artificial intelligence subject also must not use mathematical tool, mathematical logic, the fuzzy mathematics in standard etc, mathematics into the scope of artificial intelligence discipline, they will promote each other and develop faster.A brief history of artificial intelligenceArtificial intelligence can be traced back to ancient Egypt's legend, but with 1941, since the development of computer technology has finally can create machine intelligence, "artificial intelligence" is a word in 1956 was first proposed, Dartmouth learned since then, researchers have developed many theories and principles, the concept of artificial intelligence, it expands and not in the long history of the development of artificial intelligence, the slower than expected, but has been in advance, from 40 years ago, now appears to have many AI programs, and they also affected the development of other technologies. The emergence of AI programs, creating immeasurable wealth for the community, promoting the development of human civilization.The computer era1941 an invention that information storage and handling all aspects of the revolution happened. This also appeared in the U.S. and Germany's invention is the first electronic computer. Take a few big pack of air conditioning room, the programmer's nightmare: just run a program for thousands of lines to set the 1949. After improvement can be stored procedure computer programs that make it easier to input, and the development of the theory of computer science, and ultimately computer ai. This in electronic computer processing methods of data, for the invention of artificial intelligence could provide a kind of media.The beginning of AIAlthough the computer AI provides necessary for technical basis, but until the early 1950s, people noticed between machine and human intelligence. Norbert Wiener is the study of the theory of American feedback. Most familiar feedback control example is the thermostat. It will be collected room temperature and hope, and reaction temperature compared to open or close small heater, thus controlling environmental temperature. The importance of the study lies in the feedback loop Wiener:all theoretically the intelligence activities are a result of feedback mechanism and feedback mechanism is. Can use machine. The findings of the simulation of early development of AI.1955, Simon and end Newell called "a logical experts" program. This program is considered by many to be the first AI programs. It will each problem is expressed as a tree, then choose the model may be correct conclusion that a problem to solve. "logic" to the public and the AI expert research field effect makes it AI developing an important milestone in 1956, is considered to be the father of artificial intelligence of John McCarthy organized a society, will be a lot of interest machine intelligence experts and scholars together for a month. He asked them to Vermont Dartmouth in "artificial intelligence research in summer." since then, this area was named "artificial intelligence" although Dartmouth learn not very successful, but it was the founder of the centralized and AI AI research for later laid a foundation.After the meeting of Dartmouth, AI research started seven years. Although the rapid development of field haven't define some of the ideas, meeting has been reconsidered and Carnegie Mellon university. And MIT began to build AI research center is confronted with new challenges. Research needs to establish the: more effective to solve the problem of the system, such as "logic" in reducing search; expert There is the establishment of the system can be self learning.In 1957, "a new program general problem-solving machine" first version was tested. This program is by the same logic "experts" group development. The GPS expanded Wiener feedback principle, can solve many common problem. Two years later, IBM has established a grind investigate group Herbert AI. Gelerneter spent three years to make a geometric theorem of solutions of the program. This achievement was a sensation.When more and more programs, McCarthy busy emerge in the history of an AI. 1958 McCarthy announced his new fruit: LISP until today still LISP language. In. "" mean" LISP list processing ", it quickly adopted for most AI developers.In 1963 MIT from the United States government got a pen is 22millions dollars funding for research funding. The machine auxiliary recognition from the defense advanced research program, have guaranteed in the technological progress on this plan ahead of the Soviet union. Attracted worldwide computer scientists, accelerate the pace of development of AI research.Large programAfter years of program. It appeared a famous called "SHRDLU." SHRDLU "is" the tiny part of the world "project, including the world (for example, only limited quantity of geometrical form of research and programming). In the MIT leadership of Minsky Marvin by researchers found, facing the object, the small computer programs can solve the problem space and logic. Other as in the late 1960's STUDENT", "can solve algebraic problems," SIR "can understand the simple English sentence. These procedures for handling the language understanding and logic.In the 1970s another expert system. An expert system is a intelligent computer program system, and its internal contains a lot of certain areas of experience and knowledge with expert level, can use the human experts' knowledge and methods to solve the problems to deal with this problem domain. That is, the expert system is a specialized knowledge and experience of the program system. Progress is the expert system could predict under certain conditions, the probability of a solution for the computer already has. Great capacity, expert systems possible from the data of expert system. It is widely used in the market. Ten years, expert system used in stock, advance help doctors diagnose diseases, and determine the position of mineral instructions miners. All of this because of expert system of law and information storage capacity and become possible.In the 1970s, a new method was used for many developing, famous as AI Minsky tectonic theory put forward David Marr. Another new theory of machine vision square, for example, how a pair of image by shadow, shape, color, texture and basic information border. Through the analysis of these images distinguish letter, can infer what might be the image in the same period. PROLOGE result is another language, in 1972. In the 1980s, the more rapid progress during the AI, and more to go into business. 1986, the AI related software and hardware sales $4.25 billion dollars. Expert system for its utility, especially by demand. Like digital electric company with such company XCON expert system for the VAX mainframe programming. Dupont, general motors and Boeing has lots of dependence of expert system for computer expert. Some production expert system of manufacture software auxiliary, such as Teknowledge and Intellicorp established. In order to find and correct the mistakes, existing expert system and some other experts system was designed，such as teach users learn TVC expert system of the operating system.From the lab to daily lifePeople began to feel the computer technique and artificial intelligence. No influence of computer technology belong to a group of researchers in the lab. Personal computers and computer technology to numerous technical magazine now before a people. Like the United States artificial intelligence association foundation. Because of the need to develop, AI had a private company researchers into the boom. More than 150 a DEC (it employs more than 700 employees engaged in AI research) that have spent 10 billion dollars in internal AI team.Some other AI areas in the 1980s to enter the market. One is the machine vision Marr and achievements of Minsky. Now use the camera and production, quality control computer. Although still very humble, these systems have been able to distinguish the objects and through the different shape. Until 1985 America has more than 100 companies producing machine vision systems, sales were us $8 million.But the 1980s to AI and industrial all is not a good year for years. 1986-87 AI system requirements, the loss of industry nearly five hundred million dollars. Teknowledge like Intellicorp and two loss of more than $6 million, about one-third of the profits of the huge losses forced many research funding cuts the guide led. Another disappointing is the defense advanced research programme support of so-called "intelligent" this project truck purpose is to develop a can finish the task in many battlefield robot. Since the defects and successful hopeless, Pentagon stopped project funding.Despite these setbacks, AI is still in development of new technology slowly. In Japan were developed in the United States, such as the fuzzy logic, it can never determine the conditions of decision making, And neural network, regarded as the possible approaches to realizing artificial intelligence. Anyhow, the eighties was introduced into the market, the AI and shows the practical value. Sure, it will be the key to the 21st century. "artificial intelligence technology acceptance inspection in desert storm" action of military intelligence test equipment through war. Artificial intelligence technology is used to display the missile system and warning and other advanced weapons. AI technology has also entered family. Intelligent computer increase attracting public interest. The emergence of network game, enriching people's life.Some of the main Macintosh and IBM for application software such as voice and character recognition has can buy, Using fuzzy logic,AI technology to simplify the camera equipment. The artificial intelligence technology related to promote greater demand for new progress appear constantly. In a word ，Artificial intelligence has and will continue to inevitably changed our life.附件三英文文献译文人工智能“人工智能”一词最初是在1956 年Dartmouth在学会上提出来的。
第7章专家系统人工智能
知识库 以一套规则建立人的长期存储器模型 工作存储器 建立人的短期存储器模型，存放问题事实和由规则激发而推断出的新事实。 推理机 借助于把存放在工作存储器内的问题事实和存放在知识库内的规则结合起来，建立人的 推理模型，以推断出新的信息 。
2. 基于规则专家系统的结构 推理机
工作存储器
解释器
知识库
用户界面
基于框架专家系统的主要设计步骤与基于规则的专家系统相似,主要差别在于如何看待和使用知识 在设计基于框架的专家系统时，把整个问题和每件事想像为编织起来的事物 在辨识事物之后，寻找把这些事物组织起来的方法 对于任何类型的专家系统，其设计是个高度交互的过程
开发基于框架专家系统的主要任务
定义问题，包括对问题和结论考察与综述 分析领域，包括定义事物、事物特征、事件和框架结构 定义类及其特征 定义例及其框架结构 确定模式匹配规则 规定事物通信方法 设计系统界面 对系统进行评价 对系统进行扩展，深化和扩宽知识
6.4.1 基于模型专家系统的提出
❖ 关于人工智能的一个观点 ❖ 综合各种模型的专家系统比基于逻辑心理模型的系统具有更强的功能，从而有可能显著改
进专家系统的设计 ❖ 在诸多模型中，人工神经网络模型的应用最为广泛
6.4 Model-based Expert System 基于模型的专家系统
The contribution & limitation of Rule-based ES Use various qualitative models to AI An expert system integrated with various models
英语论文素材(人工智能)
摘要:人工智能(Artificial Intelligence) ，英文缩写为AI。
它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。
人工智能是计算机科学的一个分支，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器，该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。
专家系统是人工智能应用研究的主要领域。
70年代中期，专家系统的开发获得成功。
正如专家系统的先驱费根鲍姆(Feigenbaum)所说：专家系统的力量是从它处理的知识中产生的，而不是从某种形式主义及其使用的参考模式中产生的。
这正符合一句名言：知识就是力量。
80年代，专家系统在全世界得到迅速发展和广泛应用。
本文中介绍了人工智能的概念，分类，特点以及人工智能的研究的发展及其现状。
由此引出专家系统的基本概念及主要特点。
最后，通过查阅各种资料以及自己的理解分析，对专家系统的主要应用做具体分析。
阐述了将计算机人工智能的专家系统理念与审计实务相结合并提出审计工具智能化的设想，同时，具体分析了构建审计专家系统可供利用的计算机应用技术，并初步建立了审计专家系统的模块体系。
关键词：人工智能，专家系统，审计专家系统Expert system outline and applicationAbstract ：The artificial intelligence (Artificial Intelligence), English abbreviation is AI.It is the research, the development uses in simulating, extending and expands human's intelligence theory, the method, technical and an application system new technical science. The artificial intelligence is a computer science branch, it attempts the understanding intelligence the essence, the parallel intergrowth delivers one kind newly to be able to make the response by the human intelligence similar way the intelligent machine, this domain research including robot, language recognition, pattern recognition, natural language processing and expert system and so on.The expert system is the artificial intelligence applied research main domain.The 70's intermediate stages, the expert system development obtains successfully.Just like expert system pioneer Fei Genbao mho (Feigenbaum) said that,The expert system strength is in the knowledge which processes from it produces, but is not produces from some formalism and in the use reference pattern.This is conforming to a famous saying: The knowledge is a strength.The 80's, the expert system obtains the rapid development and the widespread application in the world.In this article introduced the artificial intelligence concept, the classification, the characteristic as well as the artificial intelligence research development and the present situation.From this draws out the expert system the basic concept and the main characteristic.Finally, through consults each kind of material as well as own understanding analysis, makes the concrete elaboration to the expert system main application.Introduced unifies the computer artificial intelligence expert system idea and the audit practice and proposed the audit tool intellectualization tentative plan, simultaneously, analyzed the construction to audit the expert system specifically to be possible to supply the use the computer application technology, and established initially has audited the expert system the module system.Key word: Artificial intelligence，Expert system，Auditing Expert System人工智能的发展作者XXX南京XXX大学XXX学院南京摘要计算机科学和人工智能将是21世纪逻辑学发展的主要动力源泉，并且在很大程度上将决定21世纪逻辑学的面貌。
人工智能与专家系统-1(1)
表1-2、三种产品的边际贡献率
单位边际贡献 边际贡献率 销售额 销售比重
I型 7
20% 35000
35%
II 型 4
40% 15000
15%
III型 9
36% 50000
50%
单位：元
合计
— — 100000 100%
根据表1-2，计算加权平均边际贡献率和综合保本销
售额。
24
加权平均边际贡献率= 10 7 0 10 5 4 0 20 0 9 0 1% 0 0 3 0 % 1
因此，本例中三种产品的保本点分别为： I型产品保本销售额=50000×35%=17500元， I型产品保本销售量=17500÷35=500件；
26
II 型产品保本销售额 = 50000×15%=7500元， 保本销售量 = 7500÷10=750件；
III型产品保本销售额 = 50000×50%=25000元， 保本销售量 = 25000÷25=1000件。
方案二：采用高速公路运输，平均每吨每公里运 费为0.6元，损坏率为 2%，售出价格也为每吨2200 元；
19
考虑到还有加工等费用的存在，企业决定只有当销售 收入扣除原材料的采购成本和运输成本后的总收益超过 20000 元时才可以采购。在销售不成问题的情况下，该 企业是否应采购这批原材料？若采购，应采用哪种运输 方式？
100000
综合保本销售额=固定成本总额÷加权平均边际贡 献率=15500÷31%=50000（元）。
综合保本销售额确定后，可按各种产品的销售比 重和单位售价计算各产品的保本点：
某种产品保本销售额 = 各产品综合保本销售额 ×该种产品销售比重；
25
某种产品保本销售量 = 某种产品的保本销售额 ÷某种产品单位售价。
人工智能 英文文献译文
人工智能英文文献译文在计算机科学里许多现代研究都致于两个方面:一是怎样制造智能计算机,二是怎样制造超高速计算机.硬件成本的降低,大规模集成电路技术(VLSI)不可思议的进步以及人工智能(AI)所取得的成绩使得设计面向AI应用的计算机结构极为可行,这使制造智能计算机成了近年来最”热门”的方向.AI 提供了一个崭新的方法,即用计算技术的概念和方法对智能进行研究,因此,它从根本上提供了一个全新的不同的理论基础.作为一门科学,特别是科学最重要的部分,AI的上的是了解使智能得以实现的原理.作为一种技术和科学的一部分,AI的最终目的是设计出能完全与人类智能相媲美的智能计算机系统.尽管科学家们目前尚未彀这个目的,但使计算机更加智能化已取得了很大的进展,计算机已可用来下出极高水平的象棋,用来诊断某种疾病,用来发现数学概念,实际上在许多领域已超出了高水平的人类技艺.许多AI计算机应用系统已成功地投入了实用领域.AI是一个正在发展的包括许多学科在内的领域,AI的分支领域包括:知识表达,学习,定理证明,搜索,问题的求解以及规划,专家系统,自然语言(文本或语音)理解,计算机视觉,机器人和一些其它方面/(例如自动编程,AI教育,游戏,等等).AI是使技术适应于人类的钥匙,将在下一代自动化系统中扮演极为关键的角色.据称AI应用已从实验室进入到实用领域,但是传统的冯·诺依曼计算机中,有更大的存储容量与处理能力之比,但最终效率也不是很高.无论使处理器的速度多快也无法解决这个问题,这是因为计算机所花费的时间主要取决于数据的处理器和存储器之间传送所需的时间,这被称之为冯·诺依曼瓶颈.制造的计算机越大,这个问题就越严重.解决的方法是为AI应用设计出不同于传统计算机的特殊结构.在未来AI结构的研究中,我们可以在计算机结构中许多已有的和刚刚出现的新要领的优势,比如数据流计算,栈式计算机,特征,流水线,收缩阵列,多处理器,分布式处理,数据库计算机和推理计算机.无需置疑,并行处理对于AI应用是至关重要的.根据AI中处理问题的特点,任何程序,哪怕只模拟智能的一小部分都将是非常复杂的.因此,AI仍然要面对科学技术的限制,并且继续需要更快更廉价的计算机.AI的发展能否成为主流在很大程度上取决于VLSI技术的发展.另一方面,并行提供了一个在更高性能的范围内使用廉价设备的方法.只要使简单的处理单元完全构成标准模式,构成一个大的并行处理系统就变得轻而易举,由此而产生的并行处理器应该是成本低廉的.在计算机领域和AI中,研究和设计人员已投入大量精力来考查和开发有效的并行AI结构,它也越来越成为吸引人的项目.目前,AI在表达和使用大量知识以及处理识别问题方面仍然没有取得大的进展,然而人脑在并行处理中用大量相对慢的(与目前的微电子器件比较)神经元却可十分出色地完成这些任务.这启发了人们或许需要某种并行结构来完成这些任务.将极大地影响我们进行编程的方法.也许,一旦有了正确的结构,用程序对感觉和知识表达进行处理将变得简单自然.研究人员因此投入大量努力来寻求并行结构.AI中的并行方法不仅在廉价和快速计算机方面,而且在新型计算方法方面充满希望.两种流行的AI语言是函数型编程语言,即基于λ算子的和逻辑编程语言,即基于逻辑的.此外,面向对象的编程正在引起人们的兴趣.新型计算机结构采用了这些语言并开始设计支持一种或多种编程形式的结构.一般认为结合了这三种编程方式可为AI应用提供更好的编程语言,在这方面人们已经作了大量的研究并取得了某些成就.人工智能的发展1 经典时期:游戏和定理证明人工智能比一般的计算机科学更年轻,二战后不久出现的游戏程序和解迷宫程序可以看作是人工智能的开始,游戏和解迷宫看起来距专家系统甚远,也不能为实际应用提供理论基础.但是,基于计算机的问题的最基本概念可以追溯到早期计算机完成这些任务的程序设计方法.(1)状态空间搜索早期研究提出的基本叫做状态空间搜索,实质非常简单.很多问题都可以用以下三个组成部分表述:1. 初始状态,如棋盘的初始态;2. 检查最终状态或问题解的终止测试;3. 可用于改变问题当前状态的一组操作,如象棋的合法下法.这种概念性状态空间的一种思路是图,图中节点表示状态, 弧表示操作.这种空间随着思路的发展而产生,例如,可以从棋盘的初始状态开始构成图的第一个节,白子每走一步都产生连向新状态的一条弧,黑子对白子每步棋的走法,可以认为是改变了棋盘状态的情况下连向这些新节点的操作,等等.(2)启发式搜索如果除小范围搜索空间以外,彻底的搜索不可能的话,就需要某些指导搜索的方法.用一个或多项域专门知识去遍历状态空间图的搜索叫做启发式搜索.启发是凭经验的想法,它不像算法或决策程序那样保证成功,它是一种算法或过程,但大多数情况下是有用的.2 现代时期:技术与应用所谓现代时期是从70年代半期延续到现在,其特征是日益发展的自意识和自批判能力以及对于技术和应用的更强的定位.与理解的心理学概念相联系似已不占据核心地位.人们也渐渐不再对一般问题方法(如启发式搜索)心存幻想,研究者们已经认识到,这种方法过高估计了”一般智能”的概念,这一概念一向为心理学家喜欢,其代价是未考虑人类专家所具有的某一领域内的能力.这种方法也过低地估计了人的简单常识,特别是人能够避免,认识和纠正错误的能力.解决问题的启发能力程序能够处理的相关知识的清晰表达,而非某些复杂的推理机制或某些复杂的求值函数,这一观点已被证实并接受.研究者已经研制出以模块形式对人的知识进行编码的技术,此种编码可用模式启动.这些模式可以代表原始的或处理过的数据,问题说明或问题的部分解.早期模拟人们解决问题的努力试图达到知识编码的一致性和推理机制的简单性.后来将该结果应用于万家系统的尝试主要是允许自身的多样性.INTRODCTION TO ARTIFICIALMuch modern research effort in computer science goes along two directions. One is how to make intelligent computers,the other how to make ultraly high-speed computers. The former has become the newest “hot ” direction in recent years because the decreasing hardware costs, the marvelous progress in VLSI technology,and the results achieved in Artificial Intelligence(AI) have made it feasible to design AI applications oriented computer architectures.AI,which offers a mew methodology, is the study of intelligence using the idead and methods of computation, thus offering a radically new and different basis for theory formation. As a science, essentially part of Cognitive Science, the goal of AI is to understand the principles thatmake intelligence possible. As a technology and as a part of computer science,the final goal of AI is to design intelligent computer systems that behave with the complete intelligence of human mind.although scientists are far from achieving this goal, great progress dose hae been made in making computers more intelligent . computers can be made to play excellint chess, to diagnose certain types of diseases, to discover mathematical comcepts, and if fact , to excel in many other areas requiring a high level of human expertise. Many Aiapplication computer systems have been successfully put into practical usages.AI is a growing field that covers many disciplines. Subareas of AI include knowledge representation ,learning, theorem proving,search,problem solving, and planning, expert systems, natural-language(text or speech)understanding,computer vision,robotics, and several others (such as automatic programming ,AI education,game playing, etc.) .AI is the key for making techmology adaptable to people. It will play a crucial role in the next generation of automated systems.It is a growing field that covers many disciplines.subbareas of AI include knowledge representation,learing,theorem proving,search,prroblem solving, and planning,expert systems,natural_language(text or speech ) understanding,computer vision,robotics , and severalothers (such as automatic programming, AI education, game playing,etc.).AI is the key for making technology adaptable to people. It will play a crucial role in the next generation of automated systems.It is claimed that AI applications have moved from laboratories to the real wortld. However ,conventional von Neumann computers are unsuitable for AI applications,because they are designed mainly for numerical processing. In a larger von Neumann computer, there is a larger tatio of memory to processing power and consequently it is even less efficient. This inefficiency remains no matter how fast we make the processor because the length of the computation becomes dominated by the time required to move data between processor and memory. This is called the von Neumann bottleneck. The bigger we build machines, the worse it gets. The way to solve the problem is to diverse from the traditional architectures and to design special ones for AI applications. In the research of future AI architectures, we can take advantages of many existing or currentlyemerging concepts in computer architecture, such as dataflow computation, stack machines, tagging,pipelining, systolic array,multiprocessing,distrbuted processing,database machines ,and inference machines.No doubt, parallel processing is of crucial importance for AI applications.due to the nature of problems dealt with in AI, any program that will successfully simulate even a small part of intelligence will be very complicated. Therefor,AI continuously confronts the limits of computer science technology,and there id an instatiable demand for fastert and cheaper computers.the movement of AI into mainstream is largely owned to the addevent of VLSI technology.parallel architectures,on the other han,provide a way of using the inexpensive device technology at much higher performance ranges.it ix becoming easier and cheaper to construc large parallel processing systems as long as they are made of fairly regular patterns of simpl processwing elements,and thus parallel processors should become cost effective.a great amount of effort has been devoted to inverstigating and developing effictive parallel AI architectures,ans this topic id becoming more and more attractive for reaseachers and designersin the areas of computers and AI.Currently, very little success has been achieved in AI in representing and using large bodies of knowledge and in dealing with recognition problems. Whereas human brain can perform these tasks temarkably well using a large number of relatively slow (in comparison with todays microelectronic devices) neurons in parallel. This suggests that for these tasks some kind of parllel architecture may be needed. Architectures can significantly influence the way we programming it for perception and knowledge representation would be easy and natural. This has led researchers to look into massively parallel architectures. Parallelism holds great promise for AI not only in terms of cheaper and faster computers. But also as a novel way of viewingcomputation.Two kinds of popular AI languages are functoional programming languages, which are lambda-based ,and logic programming is attracting a growing interest. Novel computer architects have considered these languages seriously and begun to design architectures supporting one or more of the programming styles. It has been recognized that a combination of the three programming styles mingt provide a better language for AI applications. There have already been a lot of research effort and achievements on this topic.Development of AI1 the classical period: game playing and theorem provingartificial inteligence is scarcely younger than conventional computer science;the bebinnings of AI can be seen in the first game-playing and puzzle-solving programs written shortly after World War Ⅱ. Gameplaying and puzzle-solving may seem somewhat remote from espert systems, and insufficiently serious to provide a theoretical basis for real applications. However, a rather basic notion about computer-based problem solving can be traced back to early attempts to program computers to perform shuch tasks.(1)state space searchThe fundamental idea that came out of early research is called state space search,and it is essentially very simple. Many kinds of problem can be formulated in terms of three important ingredients:（1）a starting state,such as the initial state of the chess board;（2）a termination test for detecing final states or sulutions to the problem,such as the simple rule for detecting checkmate in chess;（3）a set of operations that can be applied to change the current state of theproblem,such as the legal moves of chess.One way of thinking of this conceptual space of states is as a graph in which the states are nodes and the operations are arcs. Such spaces can be generated as you go . gor exampe, you coule gegin with the starting state of the chess board and make it the first node in the graph. Each of White’s possilbe first moves would then be an arc connecting this node to a new state of the board. Each of Black’s legal replies to each of these f irst moves could then be considered as operations which connect each of these new nodes to a changed statd of the board , and so on .(2)Heuristic searchHiven that exhaustive search is mot feasible for anything other than small search spaces, some means of guiding the search is required. A search that uses one or more items of domain-specific knowledge to traverse a state space graphy is called a heuristic search. Aheuristic is best thought of as a rule of thumb;it is not guaranteed to succeed,in the way that an algorithm or decision procedure is ,but it is useful in the majority of cases .2 the romantic period: computer understandingthe mid-1960s to the mid-1970s represents what I call the romantic period in artificial intelligence reserch. Atthis time, people were very concerned with making machines “understand”, by which they usually meant the understanding of natural language, especially stories and dialogue. Winograd’s (1972)SHRDLU system was arguably the climax of this epoch : a program which was capable of understanding a quite substantial subset of english by representing and reasoning about a very restricted domain ( a world consisting of children’s toy blocks).The program exhibited understanding by modifying its “blocksworld” represent ation in respinse to commands , and by responding to questions about both the configuration of blocks and its “actions” upon them. Thus is could answer questions like:What is the colour of the block supporting the red pyramid?And derive plans for obeying commands such as :Place the blue pyramid on the green block.Other researchers attempted to model human problem-solving behaviour on simple tasks ,such as puzzles, word games and memory tests. The aim war to make the knowledge and strategy used by the program resemble the knowledge and strategy of the human subject as closely as possible. Empirical studies compared the performance of progran and subject in an attempt to see how successful the simulation had been.。
人工智能英文文献原文及译文
人工智能英文文献原文及译文附件四英文文献原文Artificial Intelligence"Artificial intelligence" is a word was originally Dartmouth in 1956 to put forward. From then on, researchers have developed many theories and principles, the concept of artificial intelligence is also expands. Artificial intelligence is a challenging job of science, the person must know computer knowledge, psychology and philosophy. Artificial intelligence is included a wide range of science, it is composed of different fields, such as machine learning, computer vision, etc, on the whole, the research on artificial intelligence is one of the main goals of the machine can do some usually need to perform complex human intelligence. But in different times and different people in the "complex" understanding is different. Such as heavy science and engineering calculation was supposed to be the brain to undertake, now computer can not only complete this calculation, and faster than the human brain can more accurately, and thus the people no longer put this calculation is regarded as "the need to perform complex human intelligence, complex tasks" work is defined as the development of The Times and the progress of technology, artificial intelligence is the science of specific target and nature as The Times change and development. On the one hand it continues to gain new progress on the one hand, and turning to more meaningful, the more difficult the target. Current can be used to study the main material of artificial intelligence and artificial intelligence technology to realize the machine is a computer, the development history of artificial intelligence is computer science and technology and the development together. Besides the computer science and artificial intelligence also involves information, cybernetics, automation, bionics, biology, psychology, logic, linguistics, medicine and philosophy and multi-discipline. Artificial intelligence research include: knowledge representation, automatic reasoning and search method, machine learning and knowledge acquisition and processing of knowledge system, natural language processing, computer vision, intelligent robot, automatic program design, etc.Practical application of machine vision: fingerprint identification,face recognition, retina identification, iris identification, palm, expert system, intelligent identification, search, theorem proving game, automatic programming, and aerospace applications.Artificial intelligence is a subject categories, belong to the door edge discipline of natural science and social science.Involving scientific philosophy and cognitive science, mathematics, neurophysiological, psychology, computer science, information theory, cybernetics, not qualitative theory, bionics.The research category of natural language processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combined scheduling problem, perception, pattern recognition, logic design program, soft calculation, inaccurate and uncertainty, the management of artificial life, neural network, and complex system, human thinking mode of genetic algorithm.Applications of intelligent control, robotics, language and image understanding, genetic programming robot factory.Safety problemsArtificial intelligence is currently in the study, but some scholars think that letting computers have IQ is very dangerous, it may be against humanity. The hidden danger in many movie happened.The definition of artificial intelligenceDefinition of artificial intelligence can be divided into two parts, namely "artificial" or "intelligent". "Artificial" better understanding, also is controversial. Sometimes we will consider what people can make, or people have high degree of intelligence to create artificial intelligence, etc. But generally speaking, "artificial system" is usually significance of artificial system.What is the "smart", with many problems. This involves other such as consciousness, ego, thinking (including the unconscious thoughts etc. People only know of intelligence is one intelligent, this is the universal view of our own. But we are very limited understanding of the intelligence of the intelligent people constitute elements are necessary to find, so it is difficult to define what is "artificial" manufacturing "intelligent". So the artificial intelligence research often involved in the study of intelligent itself. Other about animal or other artificial intelligence system is widely considered to be related to the study of artificial intelligence.Artificial intelligence is currently in the computer field, the moreextensive attention. And in the robot, economic and political decisions, control system, simulation system application. In other areas, it also played an indispensable role.The famous American Stanford university professor nelson artificial intelligence research center of artificial intelligence under such a definition: "artificial intelligence about the knowledge of the subject is and how to represent knowledge -- how to gain knowledge and use of scientific knowledge. But another American MIT professor Winston thought: "artificial intelligence is how to make the computer to do what only can do intelligent work." These comments reflect the artificial intelligence discipline basic ideas and basic content. Namely artificial intelligence is the study of human intelligence activities, has certain law, research of artificial intelligence system, how to make the computer to complete before the intelligence needs to do work, also is to study how the application of computer hardware and software to simulate human some intelligent behavior of the basic theory, methods and techniques.Artificial intelligence is a branch of computer science, since the 1970s, known as one of the three technologies (space technology, energy technology, artificial intelligence). Also considered the 21st century (genetic engineering, nano science, artificial intelligence) is one of the three technologies. It is nearly three years it has been developed rapidly, and in many fields are widely applied, and have made great achievements, artificial intelligence has gradually become an independent branch, both in theory and practice are already becomes a system. Its research results are gradually integrated into people's lives, and create more happiness for mankind.Artificial intelligence is that the computer simulation research of some thinking process and intelligent behavior (such as study, reasoning, thinking, planning, etc.), including computer to realize intelligent principle, make similar to that of human intelligence, computer can achieve higher level of computer application. Artificial intelligence will involve the computer science, philosophy and linguistics, psychology, etc. That was almost natural science and social science disciplines, the scope of all already far beyond the scope of computer science and artificial intelligence and thinking science is the relationship between theory and practice, artificial intelligence is in the mode of thinking science technology application level, is one of its application. From the view of thinking, artificial intelligence is notlimited to logical thinking, want to consider the thinking in image, the inspiration of thought of artificial intelligence can promote the development of the breakthrough, mathematics are often thought of as a variety of basic science, mathematics and language, thought into fields, artificial intelligence subject also must not use mathematical tool, mathematical logic, the fuzzy mathematics in standard etc, mathematics into the scope of artificial intelligence discipline, they will promote each other and develop faster.A brief history of artificial intelligenceArtificial intelligence can be traced back to ancient Egypt's legend, but with 1941, since the development of computer technology has finally can create machine intelligence, "artificial intelligence" is a word in 1956 was first proposed, Dartmouth learned since then, researchers have developed many theories and principles, the concept of artificial intelligence, it expands and not in the long history of the development of artificial intelligence, the slower than expected, but has been in advance, from 40 years ago, now appears to have many AI programs, and they also affected the development of other technologies. The emergence of AI programs, creating immeasurable wealth for the community, promoting the development of human civilization.The computer era1941 an invention that information storage and handling all aspects of the revolution happened. This also appeared in the U.S. and Germany's invention is the first electronic computer. Take a few big pack of air conditioning room, the programmer's nightmare: just run a program for thousands of lines to set the 1949. After improvement can be stored procedure computer programs that make it easier to input, and the development of the theory of computer science, and ultimately computer ai. This in electronic computer processing methods of data, for the invention of artificial intelligence could provide a kind of media.The beginning of AIAlthough the computer AI provides necessary for technical basis, but until the early 1950s, people noticed between machine and human intelligence. Norbert Wiener is the study of the theory of American feedback. Most familiar feedback control example is the thermostat. It will be collected room temperature and hope, and reaction temperature compared to open or close small heater, thus controlling environmental temperature. The importance of the study lies in the feedback loop Wiener:all theoretically the intelligence activities are a result of feedback mechanism and feedback mechanism is. Can use machine. The findings of the simulation of early development of AI.1955, Simon and end Newell called "a logical experts" program. This program is considered by many to be the first AI programs. It will each problem is expressed as a tree, then choose the model may be correct conclusion that a problem to solve. "logic" to the public and the AI expert research field effect makes it AI developing an important milestone in 1956, is considered to be the father of artificial intelligence of John McCarthy organized a society, will be a lot of interest machine intelligence experts and scholars together for a month. He asked them to Vermont Dartmouth in "artificial intelligence research in summer." since then, this area was named "artificial intelligence" although Dartmouth learn not very successful, but it was the founder of the centralized and AI AI research for later laid a foundation.After the meeting of Dartmouth, AI research started seven years. Although the rapid development of field haven't define some of the ideas, meeting has been reconsidered and Carnegie Mellon university. And MIT began to build AI research center is confronted with new challenges. Research needs to establish the: more effective to solve the problem of the system, such as "logic" in reducing search; expert There is the establishment of the system can be self learning.In 1957, "a new program general problem-solving machine" first version was tested. This program is by the same logic "experts" group development. The GPS expanded Wiener feedback principle, can solve many common problem. Two years later, IBM has established a grind investigate group Herbert AI. Gelerneter spent three years to make a geometric theorem of solutions of the program. This achievement was a sensation.When more and more programs, McCarthy busy emerge in the history of an AI. 1958 McCarthy announced his new fruit: LISP until today still LISP language. In. "" mean" LISP list processing ", it quickly adopted for most AI developers.In 1963 MIT from the United States government got a pen is 22millions dollars funding for research funding. The machine auxiliary recognition from the defense advanced research program, have guaranteed in the technological progress on this plan ahead of the Soviet union. Attracted worldwide computer scientists, accelerate the pace of development of AI research.Large programAfter years of program. It appeared a famous called "SHRDLU." SHRDLU "is" the tiny part of the world "project, including the world (for example, only limited quantity of geometrical form of research and programming). In the MIT leadership of Minsky Marvin by researchers found, facing the object, the small computer programs can solve the problem space and logic. Other as in the late 1960's STUDENT", "can solve algebraic problems," SIR "can understand the simple English sentence. These procedures for handling the language understanding and logic.In the 1970s another expert system. An expert system is a intelligent computer program system, and its internal contains a lot of certain areas of experience and knowledge with expert level, can use the human experts' knowledge and methods to solve the problems to deal with this problem domain. That is, the expert system is a specialized knowledge and experience of the program system. Progress is the expert system could predict under certain conditions, the probability of a solution for the computer already has. Great capacity, expert systems possible from the data of expert system. It is widely used in the market. Ten years, expert system used in stock, advance help doctors diagnose diseases, and determine the position of mineral instructions miners. All of this because of expert system of law and information storage capacity and become possible.In the 1970s, a new method was used for many developing, famous as AI Minsky tectonic theory put forward David Marr. Another new theory of machine vision square, for example, how a pair of image by shadow, shape, color, texture and basic information border. Through the analysis of these images distinguish letter, can infer what might be the image in the same period. PROLOGE result is another language, in 1972. In the 1980s, the more rapid progress during the AI, and more to go into business. 1986, the AI related software and hardware sales $4.25 billion dollars. Expert system for its utility, especially by demand. Like digital electric company with such company XCON expert system for the VAX mainframe programming. Dupont, general motors and Boeing has lots of dependence of expert system for computer expert. Some production expert system of manufacture software auxiliary, such as Teknowledge and Intellicorp established. In order to find and correct the mistakes, existing expert system and some other experts system was designed，such as teach users learn TVC expert system of the operating system.From the lab to daily lifePeople began to feel the computer technique and artificial intelligence. No influence of computer technology belong to a group of researchers in the lab. Personal computers and computer technology to numerous technical magazine now before a people. Like the United States artificial intelligence association foundation. Because of the need to develop, AI had a private company researchers into the boom. More than 150 a DEC (it employs more than 700 employees engaged in AI research) that have spent 10 billion dollars in internal AI team.Some other AI areas in the 1980s to enter the market. One is the machine vision Marr and achievements of Minsky. Now use the camera and production, quality control computer. Although still very humble, these systems have been able to distinguish the objects and through the different shape. Until 1985 America has more than 100 companies producing machine vision systems, sales were us $8 million.But the 1980s to AI and industrial all is not a good year for years. 1986-87 AI system requirements, the loss of industry nearly five hundred million dollars. Teknowledge like Intellicorp and two loss of more than $6 million, about one-third of the profits of the huge losses forced many research funding cuts the guide led. Another disappointing is the defense advanced research programme support of so-called "intelligent" this project truck purpose is to develop a can finish the task in many battlefield robot. Since the defects and successful hopeless, Pentagon stopped project funding.Despite these setbacks, AI is still in development of new technology slowly. In Japan were developed in the United States, such as the fuzzy logic, it can never determine the conditions of decision making, And neural network, regarded as the possible approaches to realizing artificial intelligence. Anyhow, the eighties was introduced into the market, the AI and shows the practical value. Sure, it will be the key to the 21st century. "artificial intelligence technology acceptance inspection in desert storm" action of military intelligence test equipment through war. Artificial intelligence technology is used to display the missile system and warning and other advanced weapons. AI technology has also entered family. Intelligent computer increase attracting public interest. The emergence of network game, enriching people's life.Some of the main Macintosh and IBM for application software such as voice and character recognition has can buy, Using fuzzy logic,AI technology to simplify the camera equipment. The artificial intelligence technology related to promote greater demand for new progress appear constantly. In a word ，Artificial intelligence has and will continue to inevitably changed our life.附件三英文文献译文人工智能“人工智能”一词最初是在1956 年Dartmouth在学会上提出来的。
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人工智能与专家系统外文文献译文和原文AI研究仍在继续，但与MIS和DDS等计算机应用相比，研究热情的减弱使人工智能的研究相对落后。
然而，在研究方面的不断努力一定会推动计算机向人工智能化方向发展。
2．AI领域AI现在已经以知识系统的形式应用于商业领域，既利用人类知识来解决问题。
专家系统是最流行的基于知识的系统，他是应用计算机程序以启发方式替代专家知识。
Heuritic术语来自希腊eureka，意思是“探索”。
因此，启发方式是一种良好猜想的规则。
启发式方法并不能保证其结果如同DSS系统中传统的算法那样绝对化。
但是启发式方法提供的结果非常具体，以至于能适应于大部分情况启发式方法允许专家系统能像专家那样工作，建议用户如何解决问题。
因为专家系统被当作顾问，所以，应用专家系统就可以被称为咨询。
除了专家系统外，AI还包括以下领域：神经网络系统、感知系统、学习系统、机器人、AI硬件、自然语言处理。
注意这些领域有交叉，交叉部分也就意味着这个领域可以从另一个领域中收益。
3．专家系统的吸引力专家系统的概念是建立在专家知识能够存储在计算机中并能被其他人应用这一假设的基础上的。
专家系统作为一种决策支持系统提供了独无二的能力。
首先，专家系统为管理者提供了超出其能力的决策机会。
比如，一家新的银行投资公司可以应用先进的专家系统帮助他们进行选择、决策。
其次，专家系统在得到一个解决方案的同时给出一步步的推理。
在很多情况下，推理本身比决策的结果重要的多。
4．专家系统模型专家系统模型主要由4个部分组成：用户界面使得用户能与专家系统对话；推理引擎提供了解释知识库的能力；专家和工程师利用开发引擎建立专家系统。
1．用户界面用户界面能够方便管理者向专家系统中输入命令、信息，并接受专家系统的输出。
命令中有具体化的参数设置，引导专家系统的推理过程。
信息以参数形式赋予某些变量。
（1）专家系统输入现在流行的界面格式是图形化用户界面格式，这种界面与Window有些相同的特征。
有些系统采用了与所要解决问题相称的个性化界面例如，屏幕可能会显示机械装配图。
（2）专家系统输出专家系统一般是提供解决方案的。
这些解决方案都是以如下两种方始输出的：①解决方案解释。
在专家系统提供了问题解决方案后，管理者可能还想知道是如何得到这种方案的。
专家系统就会显示一步步到达结果的推理过程。
②问题解释。
管理者可能希望得到专家系统对问题的推理过程。
专家系统可能还需要管理者输入一些信息。
管理者问为什么需要信息，然后专家系统就会提供解释。
虽然专家系统的内部工作很复杂，但是用户界面相当友好，方便使用。
一个会用计算机的管理者，使用专家系统对他来说也肯定没有问题。
2．知识库知识库即包括描述问题域，也包括以一定的逻辑描述事实的表示技术。
术语“问题域”描述了所解决问题的业务领域。
（1）规则规则是比较常用的表示技术。
规则具体规定了在一种特定的情况下做什么。
他有两部分组成：一是条件，有真和假；二是方法，是指在条件为真的条件下采取的行动。
以下是规则的一个例子：IFECONOMIC.INDE某>1.20ANDSEASONAL.INDE某>1.30THENSALES.OUTLOOK=”E某CELLENT”包含在专家系统里的所有规则叫做规则集每个专家系统；每个专家系统里的规则集数量是不一样的。
一个简单的专家系统有几十条规则，复杂的专家系统有500或1000甚至10000条规则。
最顶层的可能只包含一个结论，这说明只有一个解决方案。
目标变量是用来描述解决方案的。
他可以是一个计算值一个可识目标，一种措施，或者一些建议。
例如，如果一个专家系统是用来给管理者在是否要进入一个新市场决策上提供建议的，那么，单目标变量MARKET.DECISION的值就是Ye或No。
当然，也有可能在最高层得到多个结论，也就意味着有多种解决方案。
例如，在关于提高市场竞争力战略决策中，专家系统可能就会提供所有可能的方案，如提高公司产品质量、增加广告投入量或降低价格。
3．推理引擎推理引擎是专家系统的一部分，他根据特定顺序在知识库内容的基础上进行推理。
在咨询阶段，推理引擎挨个检查知识库规则，当某条规则的条件为真时就采取规定的行动。
在专家系统中，当采取行动时，就称规则被激活。
在检查规则中，一般采用以下两种方法：正向推理和反向推理。
（1）正向推理在正向推理（也称为正向连接）中，规则是按照一定顺序逐个检查的。
这种顺序可能是输入到规则集中的顺序，也可能是由用户自己定义的顺序。
当检查每个规则之后，专家系统开始求值，既为“真”还是为“假”。
规则求值。
当条件为真时，规则就被激活，然后再检查下一个规则。
当然还存在规则的值即非“真”又非“假”的情况。
这种情况下，规则的条件是不知到的，这是，规则不被取消，继续检查下一条规则。
迭代推理过程。
挨个检查规则集中的规则，直到规则集中所有的规则都检查完毕。
有时为了设定一个目标变量值往往要通过好几轮测试。
可能测试这个规则所需要的信息是来自另一个规则测试的结果。
比如，在第11个规则被激活后，第5个规则才进行测试。
只要有规则被激活了，测试就继续，直到规则没有激活推理过程才结束。
（2）反向推理在反向推理（也称为反向连接）中，推理引擎将规则视为一个待解决的问题。
如图20-1所视的规则集中，规则12是一个问题，因为他分配了一个值给目标变量P推理引擎试图得出规则12的值，但是，有图中可知，我们必须先要知道规则10和11的结果。
规则10和11是规则12的子问题。
推理引擎先要对子问题进行求值。
图20-1规则集选择第一条逻辑路径。
我们假设当前规则10是待解决的问题。
推理引擎在解决问题前首先要确定规则7和8的值。
现在规则7和8是子问题，同样要解决这个子问题，先要用之前讲过的方法细分问题域，直到能够求值。
选择下一条逻辑路径。
当专家系统尝试对规则11求值时，规则9成为问题。
利用规则4和5的结果来对其求值。
因为规则4和5都为真，所以规则9的值也为真。
没有必要对规则6进行求值了。
规则9被激活后。
规则11也被激活了。
因为只要规则10或规则11其中一个为真，就可以激活规则12了，目标变量P的值也就可以得知。
（3）正向推理和反向推理的比较反向推理比正向推理要快。
因为反向推理不必考虑所有的规则，也不用一轮一轮在规则中求值。
反向推理尤其适用于以下几种情况：①多个目标变量；②有很多的规则；③在求的问题结的过程中无须将所有的或几乎所有的规则都检查一便。
有些推理引擎即适合正向推理也适合反向推理，视具体情况而定。
4．开发引擎专家系统的第4个重要组件就是开发引擎。
他用来建造专家系统。
当推理引擎包含许多规则时，建造专家系统的过程就涉及到建立规则集。
有两种基本方法：程序语言或专家系统外壳程序。
（1）程序语言你可以应用任何语言创建专家系统，但最适合符号化表示知识库的两种语言是：Lip和Polog。
Lip是在1959年由McCarthy（首届AI会议的成员之一）开发的。
Prolog是在1972年由AlainColmerauer在Mareille大学开发的。
（2）专家系统外壳程序第一个专家系统是Mycin，是由Stanford大学的EdwardShortliffe和StanleyCohen在物理学家StantonA某line的帮助下开发的。
Mycin是用来诊断某种传染病的。
当成功开发第一个专家系统Mycin后，开发者们试图在别的各个领域应用这个成果。
他们发现如果将知识库更换成反映另一个问题的相关知识Mycin推理引擎能够适用于该类型的问题域。
这种发现开创了建立专家系统的新方法：专家系统外壳程序。
他是一段预先编写好的程序，只要增加相应的知识库就能够适用于一个具体的问题域。
如今应用专家系统解决商业问题的焦点在于外壳程序的应用。
桌面帮助问题是如此的普遍，以致于再公司成立了桌面帮助部门以方便对话。
在年会上，最重要的一项活动就是演示专家系统的外壳程序的桌面帮助。
当一个公司应用其中一个外壳程序时，他必须扩充相关生产线的知识库。
比如，信息服务单元应该扩充硬件和应用软件的相关数据，在软件的帮助库中扩充软件描述等。
当桌面帮助专家系统得以应用，用户以及桌面帮助员工就可以直接跟专家系统对话，系统就可以解决问题。
人工智能的智能化程度的一个测试就是用户是否不能判别出他是在跟机器还是在跟人对话，这种测试称为Turing测试。
AlanTuring是计算机学伟大的先驱之一。
桌面帮助专家系统利用不同的信息表示技术。
比较流行的方法是CBR(cae-baedreaoning，基于事实的推理)。
他是根据历史数据作为识别问题的基础，然后提出解决方案。
有些系统是以决策树的形式来表示的。
他是一个网状结构，使用户能够回答与解决相关的问题。
专家系统外壳程序引入了人工智能，使公司没有必要开发他们自己的系统。
在商业领域，公司经常使用专家系统外壳程序来实施基于知识的系统。
5．专家系统的优缺点跟其他计算机应用一样，专家系统提供了一些实际利益，但也有一些不足之处。
管理者和公司都可以从专家系统中收益。
1.家系统为管理者带来得好处管理者应用专家系统改进决策。
这些改进表现如下：（1）提供更多的选择。
在解决问题过程中专家系统能促使管理者考虑到更多的选择。
比如，没有专家系统，由于考虑范围有限，财务经理只能跟踪30种股票的表现。
但是有了专家系统，就可以跟踪300种股票。
考虑的投资范围的扩大，也就增加了选择最佳方案的可能性。
（2）应用更高的逻辑层。
管理者借助于专家系统，能够达到最先进的专家逻辑水平。
（3）倾注更多的时间于评估方案之上。
管理者能够快速的从专家系统中得到建议，给管理者在行动之前留下更多选择和权衡的时间。
（4）决策更加一致。
与管理者相比，计算机不会有搀杂个人情感的波动因素，一旦将推理输入到计算机，管理者就会得到确定的方案。
2.为公司带来得好处专家系统为公司带来如下好处：（1）公司有更好的业绩。
因为管理者是借助于专家系统解决问题的，所以公司的管理机制得到大大的改善公司能够更好的接近目标。
（2）保持对公司知识的控制。
专家系统为老员工传授丰富的经验给新员工创造了机会。
即使员工离开后，也能够使知识自成一体。
6.专家系统的缺点专家系统的两个特征限制了将其作为商务问题解决工具的潜能。
第一，他们不能处理一致性知识的问题。
这是一个实实在在的不足之处，因为在商业中，由于人为因素的可变性，没有事情时时正确。
第二，专家系统不能应用判断和指导，而在解决结构化问题时他们是很重要的因素。
要是多几篇这么细致的范文就好了。