Management nformation Systems

[turkmmo]

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I.Introduction

Artificial Intelligence is an expanding field of science, which will be one of the most important sciences in the future. Artificial Intelligence is used in various fields, which Manufacturing is one it. Artificial Intelligence in Manufacturing is one of the gifts and curses of Technology.
Artificial Intelligence is of growing interdisciplinary of interest and practical importance. People with widely varying backgrounds and professions are discovering new ideas and new tools in this young science...Artificial Intelligence is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit the characteristics we associate with intelligence in human behavior – understanding language, learning, reasoning, solving problems, and so on. Many believe that insights into the nature of the mind can be gained by studying the operation of such programs. Since the field first evolved in the mid – 1950s, AI researchers have invented dozens of programming techniques that support some sort of intelligent behavior....
Whether or not they lead to a better understanding of the mind, there is every evidence that these developments will lead to a new, intelligent technology that may have dramatic effects on our society. Experimental AI systems have already generated interest and enthusiasm in industry and enthusiasm in industry and are being developed commercially. These experimental systems include programs that
1.Solve some hard problems in chemistry, biology, geology, engineering, and medicine at human-expert levels of performance,
2.Manipulate robotic devices to perform some useful, repetitive, sensory-motor tasks, and
3.Answer questions posed in simple dialects of English (or French, Japanese, or any other natural language, as they are called).
There is every indication that useful AI programs will play an important part in the evolving role of computers in our lives – a role that has changed, in our lifetimes, from remote to commonplace and that, if current expectations about computing cost and power are correct, is likely to evolve further from useful to essential. (Barr and Feigenbaum, 1981, The Handbook of Artificial Intelligence, PITMAN BOOKS, US)
There are many opportunities to introduce artificial intelligence and expert systems into process control and management. Most of these opportunities are best realized by implementing the functionality on the supervisory host computer, which is tightly integrated into the control layer. Examples of important areas that are ideal candidates for intelligent software for process control and management in many industries are:
1.Supervisory control and optimization
2.Simulation
3.Statistical process control
4.Scheduling
5.Alarm management
6.Operator decision support
7.Computer- aided instruction and training
8.Maintained
9.Configuration
10.Plant planning optimization
11.Order entry
12.Design
All of the application areas are pervasive in the sense that there are some 50.000 raw materials processing plants in the United States and to a greater or a lesser degree they are all confronted with the same types of problems. By architecting, designing, and implementing an intelligent application you can meaningfully address a large collection of these problems in a natural and easy-to-install manner. (Anonym, 1987)

Kusiak states, “ The rapid development of manufacturing and computer technologies has generated new problems. To solve these problems modern tools and techniques are required. Artificial Intelligence is one of the most appropriate techniques for solving complex industrial problems.”

II.Artificial Intelligence:
Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. The ability to create intelligent machines has intrigued humans since ancient times, and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems which can mimic human thought, understand speech, beat the best human chess player, and countless other feats never before possible. Find out how the military is applying AI logic to its hi-tech systems, and how in the near future Artificial Intelligence may impact our lives. (15 January 2001,

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In 1941 the development of the electronic computer and to create machine intelligence the technology became finally available. In 1956 the term Artificial Intelligence was first used at the Dartmouth conference. Because of the theories and principles developed by its dedicated researchers, Artificial Intelligence has expanded. Through its short history, advancement in the fields of Artificial Intelligence have been slower than first estimated, progress continues to be made. From its birth 4 decades ago, there have been a variety of Artificial Intelligence programs, and they have impacted other technological advancements.
When the question “What is Artificial Intelligence?”is asked and there are various answers but there is no unique definition. Webster’s Dictionary interprets intelligence as, “ the ability to understand new or trying situations.”
“Artificial Intelligence is the part of computer science concerned with designing intelligent computer systems, that is, systems exhibit the characteristics we associate with intelligence in human behavior – understanding language, learning, reasoning, solving problems, and so on.” ( Kumara, Kashyap and Soyster, 1988,Artificial Intelligence and Manufacturing: An Introduction, Kumara, Soundar T, p 2 Norcross, Ga)

III.History of Artificial Intelligence:

Evidence of Artificial Intelligence folklore can be traced back to ancient Egypt, but with the development of the electronic computer in 1941, the technology finally became available to create machine intelligence. The term artificial intelligence was first coined in 1956, at the Dartmouth conference, and since then Artificial Intelligence has expanded because of the theories and principles developed by its dedicated researchers. Through its short modern history, advancement in the fields of AI have been slower than first estimated, progress continues to be made. From its birth 4 decades ago, there have been a variety of AI programs, and they have impacted other technological advancements.
In 1941 an invention revolutionized every aspect of the storage and processing of information. That invention, developed in both the US and Germany was the electronic computer. The first computers required large, separate air-conditioned rooms, and were a programmers nightmare, involving the separate configuration of thousands of wires to even get a program running.
The 1949 innovation, the stored program computer, made the job of entering a program easier, and advancements in computer theory leads to computer science, and eventually Artificial intelligence. With the invention of an electronic means of processing data, came a medium that made AI possible.

I.The Beginnings of AI:

Although the computer provided the technology necessary for AI, it was not until the early 1950's that the link between human intelligence and machines was really observed. Norbert Wiener was one of the first Americans to make observations on the principle of feedback theory feedback theory. The most familiar example of feedback theory is the thermostat: It controls the temperature of an environment by gathering the actual temperature of the house, comparing it to the desired temperature, and responding by turning the heat up or down. What was so important about his research into feedback loops was that Wiener theorized that all intelligent behavior was the result of feedback mechanisms. Mechanisms that could possibly be simulated by machines. This discovery influenced much of early development of AI.
In late 1955, Newell and Simon developed The Logic Theorist, considered by many to be the first AI program. The program, representing each problem as a tree model, would attempt to solve it by selecting the branch that would most likely result in the correct conclusion. The impact that the logic theorist made on both the public and the field of AI has made it a crucial stepping-stone in developing the AI field.
In 1956 John McCarthy regarded as the father of AI, organized a conference to draw the talent and expertise of others interested in machine intelligence for a month of brainstorming. He invited them to Vermont for "The Dartmouth summer research project on artificial intelligence." From that point on, because of McCarthy, the field would be known as Artificial intelligence. Although not a huge success, (explain) the Dartmouth conference did bring together the founders in AI, and served to lay the groundwork for the future of AI research.

II.Knowledge Expansion

In the seven years after the conference, AI began to pick up momentum. Although the field was still undefined, ideas formed at the conference were re-examined, and built upon. Centers for AI research began forming at Carnegie Mellon and MIT, and a new challenges were faced: further research was placed upon creating systems that could efficiently solve problems, by limiting the search, such as the Logic Theorist. And second, making systems that could learn by themselves.
In 1957, the first version of a new program The General Problem Solver(GPS) was tested. The program developed by the same pair, which developed the Logic Theorist. The GPS was an extension of Wiener's feedback principle, and was capable of solving a greater extent of common sense problems. A couple of years after the GPS, IBM contracted a team to research artificial intelligence. Herbert Gelerneter spent 3 years working on a program for solving geometry theorems.
While more programs were being produced, McCarthy was busy developing a major breakthrough in AI history. In 1958 McCarthy announced his new development; the LISP language, which is still used today. LISP stands for LISt Processing, and was soon adopted as the language of choice among most AI developers.
In 1963 MIT received a 2.2 million dollar grant from the United States government to be used in researching Machine-Aided Cognition (artificial intelligence). The grant by the Department of Defense's advanced research projects Agency (ARPA), to ensure that the US would stay ahead of the Soviet Union in technological advancements. The project served to increase the pace of development in AI research, by drawing computer scientists from around the world, and continues funding.

III.The Multitude of Programs

The next few years showed a multitude of programs, one notably was SHRDLU. SHRDLU was part of the micro worlds project, which consisted of research and programming in small worlds (such as with a limited number of geometric shapes). The MIT researchers headed by Marvin Minsky, demonstrated that when confined to a small subject matter, computer programs could solve spatial problems and logic problems. Other programs, which appeared during the late 1960’s, were STUDENT, which could solve algebra story problems, and SIR, which could understand simple English sentences. The result of these programs was a refinement in language comprehension and logic.
Another advancement in the 1970's was the advent of the expert system. Expert systems predict the probability of a solution under set conditions. For example:
Because of the large storage capacity of computers at the time, expert systems had the potential to interpret statistics, to formulate rules. And the applications in the market place were extensive, and over the course of ten years, expert systems had been introduced to forecast the stock market, aiding doctors with the ability to diagnose disease, and instruct miners to promising mineral locations. This was made possible because of the systems ability to store conditional rules, and a storage of information.
During the 1970's Many new methods in the development of AI were tested, notably Minsky's frames theory. Also David Marr proposed new theories about machine vision, for example, how it would be possible to distinguish an image based on the shading of an image, basic information on shapes, color, edges, and texture. With analysis of this information, frames of what an image might be could then be referenced. another development during this time was the PROLOGUE language. The language was proposed for in 1972,
During the 1980's AI was moving at a faster pace, and further into the corporate sector. In 1986, US sales of AI-related hardware and software surged to $425 million. Expert systems in particular demand because of their efficiency. Companies such as Digital Electronics were using XCON, an expert system designed to program the large VAX computers. DuPont, General Motors, and Boeing relied heavily on expert systems Indeed to keep up with the demand for the computer experts, companies such as Teknowledge and Intellicorp specializing in creating software to aid in producing expert systems formed. Other expert systems were designed to find and correct flaws in existing expert systems.

IV.The Transition from Lab to Life

The impact of the computer technology, AI included was felt. No longer was the computer technology just part of a select few researchers in laboratories. The personal computer made its debut along with many technological magazines. Such foundations as the American Association for Artificial Intelligence also started. There was also, with the demand for AI development, a push for researchers to join private companies. 150 companies such as DEC, which employed its AI research group of 700 personnel, spend $1 billion on internal AI groups.
[FONT=&quot]Other fields of AI also made there way into the marketplace during the 1980's. One in particular was the machine vision field. The work by Minsky and Marr were now the foundation for the cameras and computers on assembly lines, performing quality control. Although crude, these systems could distinguish differences shapes in objects using black and white differences. By 1985 over a hundred companies offered machine vision systems in the US, and sales totaled $80 million. [/FONT]
The 1980's were not totally good for the AI industry. In 1986-87 the demand in AI systems decreased, and the industry lost almost a half of a billion dollars. Companies such as Teknowledge and Intellicorp together lost more than $6 million, about a third of their total earnings. The large losses convinced many research leaders to cut back funding. Another disappointment was the so-called "smart truck" financed by the Defense Advanced Research Projects Agency. The projects goal was to develop a robot that could perform many battlefield tasks. In 1989, due to project setbacks and unlikely success, the Pentagon cut funding for the project.
Despite these discouraging events, AI slowly recovered. New technology in Japan was being developed. Fuzzy logic, first pioneered in the US has the unique ability to make decisions under uncertain conditions. Also neural networks were being reconsidered as possible ways of achieving Artificial Intelligence. The 1980's introduced to its place in the corporate marketplace, and showed the technology had real life uses, ensuring it would be a key in the 21st century.

V.AI put to the Test

The military put AI based hardware to the test of war during Desert Storm. AI-based technologies were used in missile systems, heads-up-displays, and other advancements. AI has also made the transition to the home. With the popularity of the AI computer growing, the interest of the public has also grown. Applications for the Apple Macintosh and IBM compatible computer, such as voice and character recognition have become available. Also AI technology has made steadying camcorders simple using fuzzy logic. With a greater demand for AI-related technology, new advancements are becoming available. Inevitably Artificial Intelligence has, and will continue to affecting our lives. (15 January 2001,

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IV.Fields of Artificial Intelligence:
Artificial Intelligence consists of various fields, which are created simultaneously. Those, which are known, are:

i.Natural Language Processing:
Natural language processing (NLP) is the engineering of systems that process or analyze written or spoken natural language. It is a field in artificial intelligence, which attempts to use computers to process information contained in ordinary language such as English. Since most of human knowledge is recorded in linguistic form, enabling the computer to understand human language would be immensely useful in facilitating the access of these knowledge, especially with the convenience provided by the Internet. However, even though much work has been put in this field, successes are few and limited. The main problem with NLP is the acquisition of a large amount of information of the world. Human language is ambiguous by nature and each word can have many different interpretations and usage. In order for the computer to understand and solve the ambiguities between words, it has to know a lot about the world. This vast amount of knowledge is simply too large to be handled by present day technology. Thus NLP is seldom used in general applications but are limited to restricted domains.
There are different levels and approaches to natural language analysis including phonology (phonetics and sounds), morphology (forming words from more basic meaning units), syntax (forming sentences out of words), semantics (sentence meanings obtained from words), and pragmatic (understanding of how sentences are used). Most work has been done on syntax and semantics. In syntactic analysis, the structure of the input sentence is checked to make sure that it is syntactically correct and legal. Here, grammars are used to establish the relationship between words and parsing algorithms
One of the most common applications of NLP is information retrieval, when a question posed by the user in human language could be understood and answered by the computer. Here, knowledge of specific commands is no longer necessary. The computer can decipher questions posed in human language and the required information fetched from the databases available, possibly on the net. Other applications include language translation and text summarization. In the following section, we will take a look at some examples of applications in these areas. (15 January 2001,

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ii.Robotics and Vision:
The field of Artificial Intelligence programs computers to see and hear and react to other sensory stimuli.
“The field of industrial robots began in 1951 with a patent by George C. DEVOL . The first industrial robot, a Unimate from Unimation, Inc., was installed in 1961 at General Motors plant….In 1968 a robot called Shakey was built at Stanford Research Institute International.”( Anonym, 1991)
Research in this field has looked at anything from the optimal movement of robot arms to method of planning a sequence of actions to achieve a robot’s goals.... Most robots are “blind”, but some see through a TV camera that transmits an array of information back to the computer. (Barr and Feigenbaum,1981, p 10)
The construction of surface models from sensor data is an important part of perceptive robotics. When the sensor data are obtained from fixed sensors, the problem of occlusion arises. To overcome occlusion, sensors may be mounted on a robot that moves the sensors over the surface. In this thesis the sensors are single-point range finders. The range finders provide a set of sensor points, that is, the surface points detected by the sensors. The sets of sensor points obtained during the robot's motion are used to construct a surface model. The surface model is used in turn in the computation of the robot's motion, so surface modeling is performed on-line, that is, the surface model is constructed incrementally from the sensor points as they are obtained.
A planar polyhedral surface model is used that is amenable to incremental surface modeling. The surface model consists of a set of model segments, where a neighbor relation allows model segments to share edges. Also sets of adjacent shared edges may form corner vertices. Techniques are presented for incrementally updating the surface model using sets of sensor points. Various model segment operations are employed to do this: model segments may be merged, fissures in model segment perimeters are filled, and shared edges and corner vertices may be formed. Details of these model segment operations are presented.
The robot's control point is moved over the surface model at a fixed distance. This keeps the sensors around the control point within sensing range of the surface, and keeps the control point from colliding with the surface. The remainder of the robot body is kept from colliding with the surface by using redundant degrees-of-freedom. The goal of surface modeling and surface following is to model as much of the surface as possible. The incomplete parts of the surface model (non-shared edges) indicate where sections of surface that have not been exposed to the robot's sensors lie. The direction of the robot's motion is chosen such that the robot's control point is directed to non-shared edges, and then over the unexposed surface near the edge.
These techniques have been implemented and results are presented for a variety of simulated robots combined with real range sensor data. (15 January 2001,

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Kumara, Kashyap, defines the three major application areas with and Soyster (1988) is:
a)Manipulating robotic devices;
b)Planning optimal paths;
c)Sequencing tasks for goal accomplishment.

iii.Problem Solving and Games Playing:
The first great success of Artificial Intelligence. They could calculate, solve problems and play chess. The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match. ( 20 December 2001,

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iv.The Field of Neural Networks:
Neural Networks are systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains. ( 20 December 2001,

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Also referred to as connectionist architectures, parallel distributed processing, and neuromorphic systems, an artificial neural network (ANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information. Artificial neural networks are collections of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. The key element of the ANN paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements that are analogous to neurons and are tied together with weighted connections that are analogous to synapses.
Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. Learning typically occurs by example through training, or exposure to a truthed set of input/output data where the training algorithm iteratively adjusts the connection weights (synapses). These connection weights store the knowledge necessary to solve specific problems.
Although ANNs have been around since the late 1950's, it wasn't until the mid-1980 that algorithms became sophisticated enough for general applications. Today ANNs are being applied to an increasing number of real- world problems of considerable complexity. They are good pattern recognition engines and robust classifiers, with the ability to generalize in making decisions about imprecise input data. They offer ideal solutions to a variety of classification problems such as speech, character and signal recognition, as well as functional prediction and system modeling where the physical processes are not understood or are highly complex. ANNs may also be applied to control problems, where the input variables are measurements used to drive an output actuator, and the network learns the control function. The advantage of ANNs lies in their resilience against distortions in the input data and their capability of learning. They are often good at solving problems that are too complex for conventional technologies (e.g., problems that do not have an algorithmic solution or for which an algorithmic solution is too complex to be found) and are often well suited to problems that people are good at solving, but for which traditional methods are not.
There are multitudes of different types of ANNs. Some of the more popular include the multilayer perception which is generally trained with the back propagation of error algorithm, learning vector quantization, radial basis function, Hopfield, and Kohonen, to name a few. Some ANNs are classified as feed forward while others are recurrent (i.e., implement feedback) depending on how data is processed through the network. Another way of classifying ANN types is by their method of learning (or training), as some ANNs employ supervised training while others are referred to as unsupervised or self-organizing. Supervised training is analogous to a student guided by an instructor. Unsupervised algorithms essentially perform clustering of the data into similar groups based on the measured attributes or features serving as inputs to the algorithms. This is analogous to a student who derives the lesson totally on his or her own. ANNs can be implemented in software or in specialized hardware. ( 15 January 2001,

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With todays ever accelerating advances in science and technology it is becoming increasingly feasible that we may soon gain a complete understanding of human intelligence and consciousness. With this understanding it seems reasonable to assume that it will then be possible to build artificial machines whose intelligence matches, and possibly even exceeds, that of humans. Is this really possible, and if so, how?
It is generally accepted that if we are to build such machines, then they will evolve through the development of autonomous robots whose "brains" have been closely modeled on the human brain. That is, that like biological brains, these artificial brains will be based on a neural network architecture containing billions of neurons. And importantly, that these neural networks will be implemented directly in hardware, i.e. not in a simulation running on top of today's conventional von Neumann computer architecture. These neural networks will also be capable of self-configuration and learning without any kind of external computer control.( 15 January 2001,

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v.Expert Systems:
Expert Systems are programming computers to make decisions in real-life situations. ( 20 December 2001 ,

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Expert Systems are Conventional-programming languages, such as FORTRAN and C, are designed and optimized for the procedural manipulation of data (such as numbers and arrays). Humans, however, often solve complex problems using very abstract, symbolic approaches, which are not well suited for implementation in conventional languages. Although abstract information can be modeled in these languages, considerable programming effort is required to transform the information to a format usable with procedural programming paradigms.
One of the results of research in the area of artificial intelligence has been the development of techniques, which allow the modeling of information at higher levels of abstraction. These techniques are embodied in languages or tools, which allow programs to be built that closely, resemble human logic in their implementation and are therefore easier to develop and maintain. These programs, which emulate human expertise in well-defined problem domains, are called expert systems. The availability of expert system tools, such as CLIPS, has greatly reduced the effort and cost involved in developing an expert system.
Rule-based programming is one of the most commonly used techniques for developing expert systems. In this programming paradigm, rules are used to represent heuristics, or "rules of thumb," which specify a set of actions to be performed for a given situation. A rule is composed of an if portion and a then portion. The if portion of a rule is a series of patterns which specify the facts (or data) which cause the rule to be applicable. The process of matching facts to patterns is called pattern matching. The expert system tool provides a mechanism, called the inference engine, which automatically matches facts against patterns and determines which rules are applicable. The if portion of a rule can actually be thought of as the whenever portion of a rule since pattern matching always occurs whenever changes are made to facts. The then portion of a rule is the set of actions to be executed when the rule is applicable. The actions of applicable rules are executed when the inference engine is instructed to begin execution. The inference engine selects a rule and then the actions of the selected rule are executed (which may affect the list of applicable rules by adding or removing facts). The inference engine then selects another rule and executes its actions. This process continues until no applicable rules remain.(15 January 2001,

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According to Barr and Feigenbaum (1981) “Expert systems can be viewed as intermediaries between human experts, who interact with the systems in “knowledge acquisition” mode, and human interact with the systems in “ consultation mode””.

vi.Manufacturing:
Manufacturing is a fairly rich domain for applying Artificial Intelligence techniques. However most of the current systems in the literature deal with generalized problems. Manufacturing problems can be characterized into:
a)Planning problems,
b)Design problems,
c)Classification problems,
d)Diagnostic problems
Over the past decade, artificial intelligence concepts and techniques have been applied to many aspects of manufacturing, ranging from product and process development, to production management, to process diagnosis and quality control. New manufacturing concepts such as lean manufacturing, agile manufacturing, virtual manufacturing and holonic manufacturing place increasing emphasis on the need for more intelligent manufacturing systems, and there is general consensus that AI technologies will play a key role in the manufacturing enterprise of the future.... Areas of interest cover the full spectrum of AI as applied to manufacturing problems. All aspects of manufacturing, from enterprise modeling to shop floor control, are of interest. This includes topics such as agile manufacturing, factory modeling, supply chain modeling, scheduling and control, shop floor operations, concurrent engineering, collaborative design, information infrastructure, etc.
Specific topics of interest include (but are not limited to):
·systems engineering, from the design and control of the factory floor to integrated product and process design, process planning, reliability, inspection and quality control, AI and manufacturing standards, etc.;
·manufacturing life-cycle activities, including design, geometric reasoning and intelligent CAD, engineering, production planning, scheduling and control, process diagnosis and control, recycling and re-manufacturing, product and process redesign;
·collective robotics for manufacturing, micro machining, micro assembly, advanced manufacturing for robotics, intelligent machine tools, sensor-based factory control, etc.;

enterprise integration, including enterprise modeling, supply-chain management, architectures for coordination, collaborative and distributed decision-making, the role of AI in supporting new manufacturing concepts such as agility, virtual manufacturing, etc. ( 15 January 2001,

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Process Planning, Facilities Layout and Scheduling fall into the first category. (Kumara, Kashyap and Soyster, 1988,Artificial Intelligence and Manufacturing: An Introduction, Kumara, Soundar T, p 3-4 Norcross, Ga)

V.Artificial Intelligence is used in Manufacturing:
Artificial intelligence (AI) has moved from research laboratories into manufacturing. AI technology as applied to the manufacturing industry has resulted in a substantial number of applications. The past few years have witnessed an increased interest in applied AI in manufacturing. The repertory of AI technologies has evolved and expanded, and applications have been made in the manufacturing domains. Many surveys indicate that AI technologies are slowly but surely moving into manufacturing firms.(20 December 2001,

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Computer systems are used various departments in today’s companies such as:

i. MIS: Management Information Systems
ii.MRP: Manufacturing Resource Planning
iii.CAD: Computer Aided Design
iv.CAM: Computer Aided Manufacturing

The current state-of-the-art of CAD/CAM techniques is mainly confined in the domain of numerical calculation and computer graphics. It can offer rich numerical and graphic information in the manufacturing process, but is still far away from the stage of manufacturing automation needed for future industry.
The development of industry may be divided into four stages in terms of automation ( Lu, 1989). At the first stage, namely labor-intensive industry, the productivity mainly depends on skills of the human operators using simple machines without automatic control. At the second stage, fully automated equipment, but used in a stand-alone manner, plays a dominant role in the competition for production quality and quantity. Numeric control and computerized numeric control, referred to as unit automation, are the representative technologies for this stage. As a result of more powerful and computing facilities on the factory floor, the industry is now moving into the third stage, which stimulates the development of Flexible Manufacturing Systems for discrete manufacturing, and Distributed Control Systems for continuous process. At this stage, automation is realized at the information processing level with ‘computer aided’ technology, represented by CAD and CAM. The next challenge is decision-making automation for knowledge-intensive industry, such as Computer Integrated Manufacturing Systems, which integrate CAD, CAM, CAPP ( Computer-Aided Production Planning) and CAT ( Computer-Aided Testing) to accomplish various production tasks, such as taking orders, production planning, design, manufacturing, testing, sales and management. This high-performance automation does not exclude human expertise from the operation process, especially the critical decision-making process, but will take most load of decision-making task from the shoulders of human experts to assist them to concentrate on really important decision-making.
Manufacturing activity consists of many decision-making stages, which fall into two major categories:
i.Quantities computation
ii.Qualitative reasoning
(Rao,Cha and Zhou, 1992, Integrated Software System For Intelligent Manufacturing, Famili, A. Fazel, (p.385-386), AAAI Press/MIT Press Menlo Press, CA)
Computer-Integrated Manufacturing (CIM):
CIM is a technology and a concept that is very important to the process industries as well as to general manufacturing industries at large. Its goal is to be able to intelligently integrate, information, control, process, and analysis extending up from the single process, throughout the plant, and into the corporation. The goal is characterized by requirements that can only be fulfilled by important Aı technologies…. these intelligent database facilities should allow a knowledge base or intelligent application to support the following activities:
i.Database design
ii.Database connectivity
iii.Application generation
iv.Query optimization
v.Data management and modeling
vi.Performance analysis and tuning
(Anonym, 1995)

VI.Discussion:
As seen in its history and fields AI is a science, which is, growing simultaneously and which will allow mankind to fulfill his dreams. AI will solve a great deal of problems but it may also create new and bigger problems:
i.Unemployment
ii.War by using robotic technology
iii.Overcrowding
iv.And a revolt of AI

According to management philosophy anything that gives more marginal benefit is worth taking. I believe that only AI will be acceptable if Popular science fiction writer Isaac Asimov created the Three Laws of Robotics:
i.A robot must not injure a human being or, through inaction, allow a human being to come to harm.
ii.A robot must always obey orders given to it by a human being, except where it would conflict with the first law.
iii.A robot must protect it's own existence, except where it would conflict with the first or second law.

Later, Asimov added this "Zeroth Law"

i.A robot must not injure a humanoid or, through inaction, allow a humanoid to come to harm.

VII.References:
(Barr and Feigenbaum,1981, The Handbook of Artificial Intelligence, PITMAN BOOKS, US)
(Anonym, 1987)
( 15 January 2001,

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( Kumara, Kashyap and Soyster, 1988,Artificial Intelligence and Manufacturing: An Introduction, Kumara, Soundar T, p 2 Norcross, Ga)
(15 January 2001,

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(15 January 2001,

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(Anonym, 1991)
(Barr and Feigenbaum,1981, The Handbook of Artificial Intelligence, p 10, PITMAN BOOKS, US)
(15 January 2001,

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(20 December 2001,

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(20 December 2001 ,

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(15 January 2001,

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( 15 January 2001,

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(20 December 2001,

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)
(15 January 2001,

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)
( 15 January 2001,

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(Kumara, Kashyap and Soyster, 1988,Artificial Intelligence and Manufacturing: An Introduction, Kumara, Soundar T, p 3-4 Norcross, Ga)
.(20 December 2001,

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(Anonym, 1995)
Andrew KUSIAK, Artificial Intelligence Implications For CIM.
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are used to apply the grammars. The result is the production of an internal representation of the sentence, usually in the form of a parse tree. This parse tree might be passed to the semantic analysis stage where an initial meaning of the sentence is obtained from the possible parses. what html)
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[Fargonos]

türksen bu ne yabancıysan What?

 

[reymsteri3]

what bencede

 

[Fargonos]

hahaha