macky

2006-09-01T06:17:00.000-07:00

Expert system

An expert system is a class of computer programs developed by researchers in artificial intelligence during the 1970s and applied commercially throughout the 1980s. In essence, they are programs made up of a set of rules that analyze information (usually supplied by the user
An expert system also known as a knowledge based system, is a computer program that contains some of the subject-specific knowledge of one or more human experts. This class of program was first developed by researchers in artificial intelligence during the 1960s and 1970s and applied commercially throughout the 1980s. The most common form of expert systems is a program made up of a set of rules that analyze information (usually supplied by the user of the system) about a specific class of problems, as well as providing analysis of the problem(s), and, depending upon their design, recommend a course of user action in order to implement corrections. It is a system that utilizes reasoning capabilities to reach conclusions.
A related term is wizard. A wizard is an interactive computer program that helps a user solve a problem. Originally the term wizard was used for programs that construct a database search query based on criteria supplied by the user. However some rule-based expert systems are also called wizards. Other "Wizards" are a sequence of online forms that guide users through a series of choices, such as the ones which manage the installation of new software on computers, and these are not Expert Systems.

Types of problems solved by expert systems
Expert systems are most valuable to organizations that have a high-level of know-how experience and expertise that cannot be easily transferred to other members. They are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem-solving purposes.
Typically, the problems to be solved are of the sort that would normally be tackled by a medical or other professional. Real experts in the problem domain (which will typically be very narrow, for instance "diagnosing skin in human teenagers") are asked to provide "rules of thumb" on how they evaluate the problems, either explicitly with the aid of experienced systems developers, or sometimes implicitly, by getting such experts to evaluate test cases and using computer programs to examine the test data and (in a strictly limited manner) derive rules from that. Generally expert systems are used for problems for which there is no single "correct" solution which can be encoded in a conventional algorithm — one would not write an expert system to find shortest paths through graphs, or sort data, as there are simply easier ways to do these tasks.
Simple systems use simple true/false logic to evaluate data, but more sophisticated systems are capable of performing at least some evaluation taking into account real-world uncertainties, using such methods as fuzzy logic. Such sophistication is difficult to develop and still highly imperfect.

Application
Expert systems are designed and created to facilitate tasks in the fields of accounting, medicine, process control, financial service, production, human resources etc. Indeed, the foundation of a successful expert system depends on a series of technical procedures and development that may be designed by certain technicians and related experts. When a corporation begins to develop and implement an expert system project, it will use selfsourcing, insourcing and / or outsourcing techniques.
While expert systems have distinguished themselves in AI research in finding practical application, their application has been limited. Expert systems are notoriously narrow in their domain of knowledge—as an amusing example, a researcher used the "skin disease" expert system to diagnose his rustbucket car as likely to have developed measles—and the systems were thus prone to making errors that humans would easily spot. Additionally, once some of the mystique had worn off, most programmers realized that simple expert systems were essentially just slightly more elaborate versions of the decision logic they had already been using. Therefore, some of the techniques of expert systems can now be found in most complex programs without any fuss about them.
An example of an expert system used by many people is the Microsoft Windows operating system troubleshooting software located in the "help" section in the taskbar menu. Obtaining expert / technical operating system support is often difficult for individuals not closely involved with the development of the operating system. Microsoft has designed their expert system to provide solutions, advice, and suggestions to common errors encountered throughout using the operating systems.
Another 1970s and 1980s application of expert systems — which we today would simply call AI — was in computer games. For example, the computer baseball games Earl Weaver Baseball and Tony La Russa Baseball each had highly detailed simulations of the game strategies of those two baseball managers. When a human played the game against the computer, the computer queried the Earl Weaver or Tony La Russa Expert System for a decision on what strategy to follow. Even those choices where some randomness was part of the natural system (such as when to throw a surprise pitch-out to try to trick a runner trying to steal a base) were decided based on probabilities supplied by Weaver or La Russa. Today we would simply say that "the game's AI provided the opposing manager's strategy."

Expert systems versus problem-solving systems
The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. In traditional applications, problem expertise is encoded in both program and data structures.
In the expert system approach all of the problem related expertise is encoded in data structures only; none is in programs. Several benefits immediately follow from this organization.
An example may help contrast the traditional problem solving program with the expert system approach. The example is the problem of tax advice. In the traditional approach data structures describe the taxpayer and tax tables, and a program in which there are statements representing an expert tax consultant's knowledge, such as statements which relate information about the taxpayer to tax table choices. It is this representation of the tax expert's knowledge that is difficult for the tax expert to understand or modify.
In the expert system approach, the information about taxpayers and tax computations is again found in data structures, but now the knowledge describing the relationships between them is encoded in data structures as well. The programs of an expert system are independent of the problem domain (taxes) and serve to process the data structures without regard to the nature of the problem area they describe. For example, there are programs to acquire the described data values through user interaction, programs to represent and process special organizations of description, and programs to process the declarations that represent semantic relationships within the problem domain and an algorithm to control the processing sequence and focus.
The general architecture of an expert system involves two principal components: a problem dependent set of data declarations called the knowledge base or rule base, and a problem independent (although highly data structure dependent) program which is called the inference engine.

Individuals involved with expert systems
There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. In the building and maintenance of the system there are two other roles: the problem domain expert who builds and supplies the knowledge base providing the domain expertise, and a knowledge engineer who assists the experts in determining the representation of their knowledge, enters this knowledge into an explanation module and who defines the inference technique required to obtain useful problem solving activity. Usually, the knowledge engineer will represent the problem solving activity in the form of rules which is referred to as a rule-based expert system. When these rules are created from the domain expertise, the knowledge base stores the rules of the expert system.

The end user
The end-user usually sees an expert system through an interactive dialog, an example of which follows:
Q. Do you know to which restaurant you want to go?
A. No
Q. Is there any kind of food you would particularly like?
A. Unknown
Q. Do you like spicy food?
A. No
Q. Do you usually drink wine with meals?
A. Yes
Q. When you drink wine, is it French wine?
A. Why
As can be seen from this dialog, the system is leading the user through a set of questions, the purpose of which is to determine a suitable set of restaurants to recommend. This dialog begins with the system asking if the user already knows the restaurant choice (a common feature of expert systems) and immediately illustrates a characteristic of expert systems; users may choose not to respond to any question. In expert systems, dialogs are not pre-planned. There is no fixed control structure. Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular questions does not stop the consultation.
Another major distinction between expert systems and traditional systems is illustrated by the following answer given by the system when the user answers a question with another question, "Why", as occurred in the above example. The answer is:
A. I am trying to determine the type of restaurant to suggest. So far Chinese is not a likely choice. It is possible that French is a likely choice. I know that if the diner is a wine drinker, and the preferred wine is French, then there is strong evidence that the restaurant choice should include French.
It is very difficult to implement a general explanation system (answering questions like Why and How) in traditional systems. The response of the expert system to the question WHY is an exposure of the underlying knowledge structure. It is a rule; a set of antecedent conditions which, if true, allow the assertion of a consequent. The rule references values, and tests them against various constraints or asserts constraints onto them. This, in fact, is a significant part of the knowledge structure. There are values, which may be associated with some organizing entity. For example, the individual diner is an entity with various attributes (values) including whether they drink wine and the kind of wine. There are also rules, which associate the currently known values of some attributes with assertions that can be made about other attributes. It is the orderly processing of these rules that dictates the dialog itself.

The knowledge engineer
Knowledge engineers are concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge. He / she can use the knowledge acquisition component of the expert system to input the several characteristics known to be appropriate to a good inference technique including:
1. A good inference technique is independent of the problem domain.
In order to realize the benefits of explanation, knowledge transparency, and reusability of the programs in a new problem domain, the inference engine must not contain domain specific expertise.
2. Inference techniques may be specific to a particular task, such as diagnosis of hardware configuration. Other techniques may be committed only to a particular processing technique.
3. Inference techniques are always specific to the knowledge structures.
4. Successful examples of rule processing techniques include:
(a) Forward chaining
(b) Backward chaining

The inference rule
An understanding of the "inference rule" concept is important to understand expert systems. An inference rule is a statement that has two parts, an if-clause and a then-clause. This rule is what gives expert systems the ability to find solutions to diagnostic and prescriptive problems. An example of an inference rule is:
If the restaurant choice includes French, and the occasion is romantic,
Then the restaurant choice is definitely Paul Bocuse.
An expert system's rulebase is made up of many such inference rules. They are entered as separate rules and it is the inference engine that uses them together to draw conclusions. Because each rule is a unit, rules may be deleted or added without affecting other rules (though it should affect which conclusions are reached). One advantage of inference rules over traditional programming is that inference rules use reasoning which more closely resemble human reasoning.
Thus, when a conclusion is drawn, it is possible to understand how this conclusion was reached. Furthermore, because the expert system uses knowledge in a form similar to the expert, it may be easier to retrieve this information from the expert.

Chaining
There are two main methods of reasoning when using inference rules: backward chaining and forward chaining.
Forward chaining starts with the data available and uses the inference rules to conclude more data until a desired goal is reached. An inference engine using forward chaining searches the inference rules until it finds one in which the if-clause is known to be true. It then concludes the then-clause and adds this information to its data. It would continue to do this until a goal is reached. Because the data available determines which inference rules are used, this method is also called data driven.
Backward chaining starts with a list of goals and works backwards to see if there is data which will allow it to conclude any of these goals. An inference engine using backward chaining would search the inference rules until it finds one which has a then-clause that matches a desired goal. If the if-clause of that inference rule is not known to be true, then it is added to the list of goals. For example, suppose a rulebase contains two rules:
(1) If Fritz is green then Fritz is a frog.
(2) If Fritz is a frog then Fritz hops.
Suppose a goal is to conclude that Fritz hops.The rulebase would be searched and rule (2) would be selected because its conclusion (the then clause) matches the goal. It is not known that Fritz is a frog, so this "if" statement is added to the goal list. The rulebase is again searched and this time rule (1) is selected because its then clause matches the new goal just added to the list. This time, the if-clause (Fritz is green) is known to be true and the goal that Fritz hops is concluded. Because the list of goals determines which rules are selected and used, this method is called goal driven.

Confidences
Another advantage of expert systems over traditional methods of programming is that they allow the use of confidences. When a human reasons he does not always conclude things with 100% confidence. He might say, "If Fritz is green, then he is probably a frog" (after all, he might be a chameleon). This type of reasoning can be imitated by using numeric values called confidences. For example, if it is known that Fritz is green, it might be concluded with 0.85 confidence that he is a frog; or, if it is known that he is a frog, it might be concluded with 0.95 Confidence that he hops. These numbers are similar in nature to probabilities, but they are not the same. They are meant to imitate the confidences humans use in reasoning rather than to follow the mathematical definitions used in calculating probabilities.
The following general points about expert systems and their architecture have been illustrated.
1. The sequence of steps taken to reach a conclusion is dynamically synthesized with each new case. It is not explicitly programmed when the system is built.
2. Expert systems can process multiple values for any problem parameter. This permits more than one line of reasoning to be pursued and the results of incomplete (not fully determined) reasoning to be presented.
3. Problem solving is accomplished by applying specific knowledge rather than specific technique. This is a key idea in expert systems technology. It reflects the belief that human experts do not process their knowledge differently from others, but they do possess different knowledge. With this philosophy, when one finds that their expert system does not produce the desired results, work begins to expand the knowledge base, not to re-program the procedures.
There are various expert systems in which a "rulebase" and an "inference engine" cooperate to simulate the reasoning process that a human expert pursues in analyzing a problem and arriving at a conclusion. In these systems, in order to simulate the human reasoning process, a vast amount of knowledge needed to be stored in the knowledge base. Generally, the knowledge base of such an expert system consisted of a relatively large number of "if then" type of statements that were interrelated in a manner that, in theory at least, resembled the sequence of mental steps that were involved in the human reasoning process.
Because of the need for large storage capacities and related programs to store the rulebase, most expert systems have, in the past, been run only on large information handling systems. Recently, the storage capacity of personal computers has increased to a point where it is becoming possible to consider running some types of simple expert systems on personal computers.
In some applications of expert systems, the nature of the application and the amount of stored information necessary to simulate the human reasoning process for that application is just too vast to store in the active memory of a computer. In other applications of expert systems, the nature of the application is such that not all of the information is always needed in the reasoning process. An example of this latter type application would be the use of an expert system to diagnose a data processing system comprising many separate components, some of which are optional. When that type of expert system employs a single integrated rulebase to diagnose the minimum system configuration of the data processing system, much of the rulebase is not required since many of the components which are optional units of the system will not be present in the system. Nevertheless, earlier expert systems require the entire rulebase to be stored since all the rules were, in effect, chained or linked together by the structure of the rulebase.
When the rulebase is segmented, preferably into contextual segments or units, it is then possible to eliminate portions of the Rulebase containing data or knowledge that is not needed in a particular application. The segmenting of the rulebase also allows the expert system to be run with systems or on systems having much smaller memory capacities than was possible with earlier arrangements since each segment of the rulebase can be paged into and out of the system as needed. The segmenting of the rulebase into contextual segments requires that the expert system manage various intersegment relationships as segments are paged into and out of memory during execution of the program. Since the system permits a rulebase segment to be called and executed at any time during the processing of the first rulebase, provision must be made to store the data that has been accumulated up to that point so that at some time later in the process, when the system returns to the first segment, it can proceed from the last point or rule node that was processed. Also, provision must be made so that data that has been collected by the system up to that point can be passed to the second segment of the rulebase after it has been paged into the system and data collected during the processing of the second segment can be passed to the first segment when the system returns to complete processing that segment.
The user interface and the procedure interface are two important functions in the information collection process.

The user interface
The function of the user interface is to present questions and information to the operator and supply the operator's responses to the inference engine.
Any values entered by the user must be received and interpreted by the user interface. Some responses are restricted to a set of possible legal answers, others are not. The user interface checks all responses to insure that they are of the correct data type. Any responses that are restricted to a legal set of answers are compared against these legal answers. Whenever the user enters an illegal answer, the user interface informs the user that his answer was invalid and prompts him to correct it. As explained in the cross referenced application, communication between the user interface and the inference engine is performed through the use of a User Interface Control Block (UICB) which is passed between the two.

Procedure node interface
The function of the procedure node interface is to receive information from the procedures coordinator and create the appropriate procedure call. The ability to call a procedure and receive information from that procedure can be viewed as simply a generalization of input from the external world. While in some earlier expert systems external information has been obtained, that information was obtained only in a predetermined manner so only certain information could actually be acquired. This expert system, disclosed in the cross-referenced application, through the knowledge base, is permitted to invoke any procedure allowed on its host system. This makes the expert system useful in a much wider class of knowledge domains than if it had no external access or only limited external access.
In the area of machine diagnostics using expert systems, particularly self-diagnostic applications, it is not possible to conclude the current state of "health" of a machine without some information. The best source of information is the machine itself, for it contains much detailed information that could not reasonably be provided by the operator.
The knowledge that is represented in the system appears in the rulebase. In the rulebase described in the cross-referenced applications, there are basically four different types of objects, with associated information present.
1. Classes--these are questions asked to the user.
2. Parameters--a parameter is a place holder for a character string which may be a variable that can be inserted into a class question at the point in the question where the parameter is positioned.
3. Procedures--these are definitions of calls to external procedures.
4. Rule Nodes--The inferencing in the system is done by a tree structure which indicates the rules or logic which mimics human reasoning. The nodes of these trees are called rule nodes. There are several different types of rule nodes.
The rulebase comprises a forest of many trees. The top node of the tree is called the goal node, in that it contains the conclusion. Each tree in the forest has a different goal node. The leaves of the tree are also referred to as rule nodes, or one of the types of rule nodes. A leaf may be an evidence node, an external node, or a reference node.
An evidence node functions to obtain information from the operator by asking a specific question. In responding to a question presented by an evidence node, the operator is generally instructed to answer "yes" or "no" represented by numeric values 1 and 0 or provide a value of between 0 and 1, represented by a "maybe."
Questions which require a response from the operator other than yes or no or a value between 0 and 1 are handled in a different manner.
A leaf that is an external node indicates that data will be used which was obtained from a procedure call.
A reference node functions to refer to another tree or subtree.
A tree may also contain intermediate or minor nodes between the goal node and the leaf node. An intermediate node can represent logical operations like And or Or.
The inference logic has two functions. It selects a tree to trace and then it traces that tree. Once a tree has been selected, that tree is traced, depth-first, left to right.
The word "tracing" refers to the action the system takes as it traverses the tree, asking classes (questions), calling procedures, and calculating confidences as it proceeds.
As explained in the cross-referenced applications, the selection of a tree depends on the ordering of the trees. The original ordering of the trees is the order in which they appear in the rulebase. This order can be changed, however, by assigning an evidence node an attribute "initial" which is described in detail in these applications. The first action taken is to obtain values for all evidence nodes which have been assigned an "initial" attribute. Using only the answers to these initial evidences, the rules are ordered so that the most likely to succeed is evaluated first. The trees can be further re-ordered since they are constantly being updated as a selected tree is being traced.
It has been found that the type of information that is solicited by the system from the user by means of questions or classes should be tailored to the level of knowledge of the user. In many applications, the group of prospective uses is nicely defined and the knowledge level can be estimated so that the questions can be presented at a level which corresponds generally to the average user. However, in other applications, knowledge of the specific domain of the expert system might vary considerably among the group of prospective users.
One application where this is particularly true involves the use of an expert system, operating in a self-diagnostic mode on a personal computer to assist the operator of the personal computer to diagnose the cause of a fault or error in either the hardware or software. In general, asking the operator for information is the most straightforward way for the expert system to gather information assuming, of course, that the information is or should be within the operator's understanding. For example, in diagnosing a personal computer, the expert system must know the major functional components of the system. It could ask the operator, for instance, if the display is a monochrome or color display. The operator should, in all probability, be able to provide the correct answer 100% of the time. The expert system could, on the other hand, cause a test unit to be run to determine the type of display. The accuracy of the data collected by either approach in this instance probably would not be that different so the knowledge engineer could employ either approach without affecting the accuracy of the diagnosis. However, in many instances, because of the nature of the information being solicited, it is better to obtain the information from the system rather than asking the operator, because the accuracy of the data supplied by the operator is so low that the system could not effectively process it to a meaningful conclusion.
In many situations the information is already in the system, in a form of which permits the correct answer to a question to be obtained through a process of inductive or deductive reasoning. The data previously collected by the system could be answers provided by the user to less complex questions that were asked for a different reason or results returned from test units that were previously run.

Advantages and disadvantages
Expert systems exercise information technology to acquire and utilize human expertise. It can be beneficial for organizations that have clear objectives, rules and procedures. Expert systems can:
Provide consistent answers for repetitive decisions, processes and tasks
Hold and maintain significant levels of information
Reduce employee training costs
Centralize the decision making process
Create efficiencies and reduce time needed to solve problems
Combine multiple human expert intelligences
Reduce the amount of human errors
Give strategic and comparative advantages creating entry barriers to competitors
Review transactions that human experts may overlook
Although significantly advantageous to many entities, limitations of expert systems may arise through:
The lack of human common sense needed in some decision makings
The creative responses human experts can respond to in unusual circumstances
Domain experts not always being able to explain their logic and reasoning
The challenges of automating complex processes
The lack of flexibility and ability to adapt to changing environments
Not being able to recognize when no answer is available

How it works
Expert Systems consist of:
knowledge base (facts)
production rules ("if.., then..")
inference engine (controls how "if.., then.." rules are applied towards facts)
Actually there are two methods to make conclusions.
Method name
short explanation
use
example systems
Forward chaining
facts driven
can find new ideas
CLIPS, Jess
Backward chaining
hypothesis driven
usually used for diagnosis
Prolog, Mycin
Nice & simple tutorial about backward and forward chaining

Prominent expert systems
Dendral analyse mass spectra
Dipmeter Advisor analysis of data gathered during oil exploration
Mycin diagnose infectious blood diseases and recommend antibiotics (by Standford University)
CADUCEUS (expert system) blood-borne infectious bacteria
R1 (expert system)/XCon order processing
CLIPS programming language
Prolog programming language
Jess: CLIPS using Java with more features
ART programming language

Bibliography
James Ignizio, Introduction to Expert Systems (1991), ISBN 0-07-909785-5
Joseph C. Giarratano, Gary Riley Expert Systems, Principles and Programming (2005), ISBN 0-534-38447-1
Peter Jackson Introduction to Expert Systems (1998), ISBN 0-20-187686-8

2006-09-01T05:41:00.000-07:00

ROBOTS ARE USED

Why Use Robots?
There are many benefits to using robots instead of humans. Can you imagine working in a factory all day, every day, doing the exact same thing over and over again? The good thing about robots is that they will never get bored, and they will do things more efficiently than people. Also, robots never get sick, or need to rest. This means they can work for 24 hours a day, 7 days a week. They will never need time off, or lunch breaks. Sometimes, when a task is too dangerous or difficult for a human, a robot will be able to do it without any risks or problems.

Introduction Robots effect everyone, even if a person never comes in contact with one. Robots build cars, sort mail, and manufacture millions of products. The focus of this research is to explore the expansion of robots in companies today and to explore the future of robotics in the production industry. Robots have taken dangerous, unpleasant jobs away from the human work force and robots increase speed, accuracy, and repeatability.

Companies are purchasing robots at a record rate because robots increase the quality and production of a product. However only ten percent of the manufacturing companies in North America use robots while other companies rely on the human labor force. The automotive industry uses robots to produce cars at a faster rate and ensure quality. Cycle time optimisation is the time allowed for a robot to complete a task. Manufacturing companies develop plans based on cycle time optimisation to figure how long the production will take for an individual product. The body of a car travels down an assembly line to a station where several robots work on different parts of the car at the same time. Robots on the line can complete certain task like applying adhesive at a hotter temperature where as a human couldn’t without being burned. The research shows a strong correlation between software and robots. The software works as the instructions and the robot carries out the task. Using software to program a robot could lead to one robot doing a various arrangement tasks. Robots with a vision system can locate and decide if a part in the inventory is out. Robots with vision are used to locate and retrieve parts needed for the production process. The robot would then refill the inventory and log in the computer system the ID number for inventory control purposes. Robotic development has lead to a lighter but stronger robot. The robots have increased motion that allow it to do different tasks. Finally, robotics technology has developed robots to combine several tasks of different types of robots into one robot system. This reduces the cost of purchasing robots because one robot can do the tasks of two robots.
The Robot Expansion
Industrial robot sales reached record numbers in North America for 1999. North America purchased 17,591 robots worth 1.4 billion dollars in 1999. In the article “Is This The Age of the Robot?”, by George Weimer (2000), reports record sales and increasing development in robotics leads to companies using more robots on production lines. The automakers purchased the most robots in 1999 by increasing robot growth by 24 percent and the manufacturing sector increased by 14 percent. Last year robot companies delivered over 15,000 robots to North America bringing the total number of robots in North America to 100,000. The cost of robots and maintenance costs decreased making robots affordable. The United States Postal Service ordered 60 million dollars of robotics and automation equipment. Many companies use simulation before purchasing robots to predict the cost of mistakes and capital.

Robot companies design new robots to complete difficult tasks like metal cutting, plastic injection modeling, and die casting extraction operations. These new robots combine several key elements of different types of robots into just one robot. The new robots like the M-710i uses mobile ability and can service multiple machines or stations. Companies such as ABB of Sweden use state of the art robots to make their plant into a transformer plant. Robot systems allow the plant operators to program the robots to do various tasks through computer software and can do dangerous work like welding that humans cannot do.

The Addition of Robots in the Automotive Industry
In the auto industry robots help improve productivity and quality. The article “Ford adds to its robot family”, by Wolfgang Klinker (1989), introduces the effectiveness of using robotics on the assembly line. Ford added 100 robots to the plant in Cologne, West Germany. Ford uses this plant to assemble the Festiva model. The large majority of the robots work in the body pressing and body shell assembly section. With replacing humans in these particular sections this allows them to solve a problem in case of an error or bad parts in a shorter amount of time than if they use robots. A robot can be replaced or repaired in about 30 minutes but a human could need a replacement tool that could take up to hours to find.

Cycle time optimisation helps direct the assembly line and keeps from the bottlenecking in production. Four robots work together to assemble the floor of the car and then 21 robots at 15 stations weld and connect the engine to the floor panel. Then 90 robots spot-weld the panels and the doors to the frame of the car. Robots even apply adhesive to the inner and outer surfaces of the vehicle during this process. Robots apply adhesive at hotter temperatures than humans can and the hotter the adhesive the better the melting element it makes. The robots next weld the sub shell of the body to the frame and the floor panel. Robots re-spot weld the body with 300 spot welds and next the robots weld the hinges to the door. The body then goes through a test station, the computer stores the results of the test for production and assembly and then stored. Integrated planning with robotics helps Ford achieve a productive and quality assembly line.

The Future Factories use of Robots
The increase in dexterity, tasks, and development of software allows robotics to reduce more human jobs. “Digital Factories foster new vision of manufacturing” by Paul Dvork (2000) states companies like Ford and General Motors will use software to run entire factories and include writing programs for their robots on completing certain tasks. Factories trust the robots more than they do the humans to transport certain materials. For instance, a company purchases 300-mm wafers used in processors valued at 300,000 dollars a wafer. The wafers weigh more than most humans could lift so to ensure the waffers aren’t damaged they program the robots to do that task of transporting. Not all materials that arrive at a plant weight the same or even come in the same size of boxes but with using a robot with reconfigure ability allows them to write a program that helps the robot pick up different types of objects.

The robot developing companies now include a software black box called Realistic Robot Simulation. The software made up of algorithms describes the movement and motion planning of a robot. The software tells the robot to take into account the effects of gravity, friction, and bounces. This increases the safety of a material while transported to the assembly line. Another advantage of the software allows a plant manager to check the operations of any robot anywhere within the factory system. Using a digital factory an automaker can produce more than one type of models at the same plant. Plant A makes trucks and plant B makes cars but programming the software to tell the robot to do multiple tasks could lead to plant A making cars and trucks.

Robots with Vision
Robots with a vision system can complete more tasks and save the company more money than robots without a vision system. Samantha Hoover’s article, “Ford Develops Robots with Vision” discusses benefits from robots with a vision system. An ABB IRB 6000 can locate and retrieve a heavy part from the storage bins. These robots prevent physical injury to humans and protects the quality of the part. Ford uses the Cognex Checkpoint machine vision system instead of the pixel based vision system due to the poor lighting. The Cognex system uses shapes to decide what part it needs. The lighting does not effect the system. Now the system locates the part it transfers coordinates to the robot and the robot uses grippers to remove the part. The robot has + or – 0.2mm measure of accuracy. A robot with the vision system determines if the bin is empty. The robot then refills the bin and this system record the ID tag for inventory control. The robots design consists of a RS-170 camera attached to a ABB IRB 6000 pedestal robot. The system uses a human to work as the operator but the system essentially doesn’t use the operator. The advantages of using this new vision system allows robots to move heavy parts humans can’t move without physical injury, + or – 0.2mm measure of accuracy, and no special lighting is required.

Faster and more Dextrous Robots
The article “New Robots are faster and more Dextrous” reports SK series replaced by the UP Series. The UP series of articulated-arm robot differs from SK series robot by the UP series eliminates trailing hoses and cables by using a flexible conduit which sends all services to the upper arm instead of the base used by the SK series. The UP models posses greater speed, accuracy and reliability. This increases the tasks this robot can accomplish in the fields of welding, handling, and machining. The UP series offers eight possible models ranging from 6 to 400 kg. The UP-130 can carry a load that weighs130 kg compared to the SK-130 can only carry 30 kg. The UP series improved accuracy to + or – 0.2mm. The improvement of the reach of the arm tested at +240 degrees and –130 degrees compared to the SK at +35 degrees and –115 degrees. The roll of the wrists for the UP tested at 360 degrees which the SK did not complete a full turning of a circle. The UP robot can turn on its axis at 130 degrees per second and the wrist roll was timed at 215 degrees per second. The improvement of the UP series based on the fact that it weighs 1,300 kg, which weighed 200 kg less than the SK series.
The R.I.S.C. System

John Canny and Kenneth Goldberg introduce computational planning algorithms and simple hardware as the future of industrial manufacturing. In their report “Recent Results and Open Problems”, they support their ideas by evaluating the difference between the RISC robot systems and general-purpose robots. General-purpose robots are manipulators or sensors but the RISC system combines two. This combination in theory will lower cost to manufacture a product, make production reliable, and it would be easier to reconfigure.

The RISC robots use beam sensors and actuators. Actuators are defined as robots that put the product into motion. The RISC system has increased speed because of the actuators, and the actuators allow for the production to increase with exact precision. This robot system is affordable because the parts can be purchased off the shelf, which would make repairing easier because it wouldn’t have to be specially ordered. The most important quality of this system is the reconfigurability. The RISC self-calibrates, and its easy to adjust the actuators and the feeders. This system eliminates the work of two robots because it is a combination of sensors and feeders. Canny and Goldberg predict that the RISC robot system will be found in manufacturing companies all over the world in five years.

Conclusion
In conclusion, robots take difficult task and generate results with exact precision and faster speeds than humans could do. The development of software increases the development of robot technology. Eventually an entire plant will be under the control of a software program that will also program the robots on what task needs to be done. The development of robotics will lead to more products on the market and a greater quality. The mobility and strengths of a robot are increasing and the robots can accomplish more tasks.

2006-09-01T04:13:00.000-07:00

ARTIFICIAL INTELLIGENCES

INTRODUCTION
The intellectual roots of AI, and the concept of intelligent machines, may be found in Greek mythology. Intelligent artifacts appear in literature since then, with real (and fraudulent) mechanical devices actually demonstrated to behave with some degree of intelligence. Some of these conceptual achievements are listed below under "Ancient History."
After modern computers became available, following World War II, it has become possible to create programs that perform difficult intellectual tasks. From these programs, general tools are constructed which have applications in a wide variety of everday problems. Some of these computational milestones are listed below under "Modern History."
ANCIENT HISTORY
Greek myths of Hephaestus and Pygmalion incorporate the idea of intelligent robots. Many other myths in antiquity involve human-like artifacts. Many mechanical toys and models were actually constructed, e.g., by Hero, Daedalus and other real persons.
5th century B.C.
Aristotle invented syllogistic logic, the first formal deductive reasoning system.
13th century
Talking heads were said to have been created, Roger Bacon and Albert the Great reputedly among the owners.
Ramon Llull, Spanish theologian, invented machines for discovering nonmathematical truths through combinatories.
15th century
Invention of printing using moveable type. Gutenberg Bible printed (1456).
15th-16th century
Clocks, the first modern measuring machines, were first produced using lathes.
16th century
Clockmakers extended their craft to creating mechanical animals and other novelties.
Rabbi Loew of Prague is said to have invented the Golem, a clay man brought to life (1580).
17th century
Early in the century, Descartes proposed that bodies of animals are nothing more than complex machines. Many other 17th century thinkers offered variations and elaborations of Cartesian mechanism.
Hobbes published The Leviathan, containing a material and combinatorial theory of thinking.
Pascal created the first mechanical digital calculating machine (1642).
Leibniz improved Pascal's machine to do multiplication & division (1673) and evisioned a universal calculus of reasoning by which arguments could be decided mechanically.
18th century
The 18th century saw a profusion of mechanical toys, including the celebrated mechanical duck of Vaucanson and von Kempelen's phony mechanical chess player, The Turk (1769).
19th century
Luddites (led by Ned Ludd) destroyed machinery in England (1811-1816).
Mary Shelley published the story of Frankenstein's monster (1818).
George Boole developed a binary algebra representing (some) "laws of thought."
Charles Babbage & Ada Byron (Lady Lovelace) worked on programmable mechanical calculating machines.
20th century - First Half
Bertrand Russell and Alfred North Whitehead published Principia Mathematica, which revolutionaized formal logic. Russell, Ludwig Wittgenstein, and Rudolf Carnap lead philosophy into logical analysis of knowledge.
Karel Capek's play "R.U.R." (Rossum's Universal Robots) opens in London (1923). - First use of the word 'robot' in English.
Warren McCulloch & Walter Pitts publish "A Logical Calculus of the Ideas Immanent in Nervous Activity" (1943), laying foundations for neural networks.
Arturo Rosenblueth, Norbert Wiener & Julian Bigelow coin the term "cybernetics" in a 1943 paper. Wiener's popular book by that name published in 1948.
Vannevar Bush published As We May Think (Atlantic Monthly, July 1945) a prescient vision of the future in which computers assist humans in many activities.
A.M. Turing published "Computing Machinery and Intelligence" (1950). - Introduction of Turing's Test as a way of operationalizing a test of intelligent behavior.
Claude Shannon published detailed analysis of chess playing as search (1950).
Isaac Asimov published his three laws of robotics (1950).
MODERN HISTORY
1956
John McCarthy coined the term "artificial intelligence" as the topic of the Dartmouth Conference, the first conference devoted to the subject.

Demonstration of the first running AI program, the Logic Theorist (LT) written by Allen Newell, J.C. Shaw and Herbert Simon (Carnegie Institute of Technology, now Carnegie Mellon University).
1957
The General Problem Solver (GPS) demonstrated by Newell, Shaw & Simon.
1952-62
Arthur Samuel (IBM) wrote the first game-playing program, for checkers, to achieve sufficient skill to challenge a world champion. Samuel's machine learning programs were responsible for the high performance of the checkers player.
1958
John McCarthy (MIT) invented the Lisp language.

Herb Gelernter & Nathan Rochester (IBM) described a theorem prover in geometry that exploits a semantic model of the domain in the form of diagrams of "typical" cases.
Teddington Conference on the Mechanization of Thought Processes was held in the UK and among the papers presented were John McCarthy's "Programs with Common Sense," Oliver Selfridge's "Pandemonium," and Marvin Minsky's "Some Methods of Heuristic Programming and Artificial Intelligence."
Late 50's & Early 60's
Margaret Masterman & colleagues at Cambridge design semantic nets for machine translation.
1961
James Slagle (PhD dissertation, MIT) wrote (in Lisp) the first symbolic integration program, SAINT, which solved calculus problems at the college freshman level.
1962
First industrial robot company, Unimation, founded.
1963
Thomas Evans' program, ANALOGY, written as part of his PhD work at MIT, demonstrated that computers can solve the same analogy problems as are given on IQ tests.

Ivan Sutherland's MIT dissertation on Sketchpad introduced the idea of interactive graphics into computing.

Edward A. Feigenbaum & Julian Feldman published Computers and Thought, the first collection of articles about artificial intelligence.
1964
Danny Bobrow's dissertation at MIT (tech.report #1 from MIT's AI group, Project MAC), shows that computers can understand natural language well enough to solve algebra word problems correctly.
Bert Raphael's MIT dissertation on the SIR program demonstrates the power of a logical representation of knowledge for question-answering systems
1965
J. Alan Robinson invented a mechanical proof procedure, the Resolution Method, which allowed programs to work efficiently with formal logic as a representation language.

Joseph Weizenbaum (MIT) built ELIZA, an interactive program that carries on a dialogue in English on any topic. It was a popular toy at AI centers on the ARPA-net when a version that "simulated" the dialogue of a psychotherapist was programmed.
1966
Ross Quillian (PhD dissertation, Carnegie Inst. of Technology; now CMU) demonstrated semantic nets.

First Machine Intelligence workshop at Edinburgh - the first of an influential annual series organized by Donald Michie and others.

Negative report on machine translation kills much work in Natural Language Processing (NLP) for many years.
1967
Dendral program (Edward Feigenbaum, Joshua Lederberg, Bruce Buchanan, Georgia Sutherland at Stanford) demonstrated to interpret mass spectra on organic chemical compounds. First successful knowledge-based program for scientific reasoning.

Joel Moses (PhD work at MIT) demonstrated the power of symbolic reasoning for integration problems in the Macsyma program. First successful knowledge-based program in mathematics.

Richard Greenblatt at MIT built a knowledge-based chess-playing program, MacHack, that was good enough to achieve a class-C rating in tournament play.
Late 60s
Doug Engelbart invented the mouse at SRI.
1968
Marvin Minsky & Seymour Papert publish Perceptrons, demonstrating limits of simple neural nets.
1969
SRI robot, Shakey, demonstrated combining locomotion, perception and problem solving.

Roger Schank (Stanford) defined conceptual dependency model for natural language understanding. Later developed (in PhD dissertations at Yale) for use in story understanding by Robert Wilensky and Wendy Lehnert, and for use in understanding memory by Janet Kolodner.

First International Joint Conference on Artificial Intelligence (IJCAI) held in Washington, D.C.
1970
Jaime Carbonell (Sr.) developed SCHOLAR, an interactive program for computer-aided instruction based on semantic nets as the representation of knowledge.

Bill Woods described Augmented Transition Networks (ATN's) as a representation for natural language understanding.

Patrick Winston's PhD program, ARCH, at MIT learned concepts from examples in the world of children's blocks.
Early 70's
Jane Robinson & Don Walker established influential Natural Language Processing group at SRI.
1971
Terry Winograd's PhD thesis (MIT) demonstrated the ability of computers to understand English sentences in a restricted world of children's blocks, in a coupling of his language understanding program, SHRDLU, with a robot arm that carried out instructions typed in English.
1972
Prolog developed by Alain Colmerauer.
1973
The Assembly Robotics group at Edinburgh University builds Freddy, the Famous Scottish Robot, capable of using vision to locate and assemble models.
1974
Ted Shortliffe's PhD dissertation on MYCIN (Stanford) demonstrated the power of rule-based systems for knowledge representation and inference in the domain of medical diagnosis and therapy. Sometimes called the first expert system.

Earl Sacerdoti developed one of the first planning programs, ABSTRIPS, and developed techniques of hierarchical planning.
1975
Marvin Minsky published his widely-read and influential article on Frames as a representation of knowledge, in which many ideas about schemas and semantic links are brought together.

The Meta-Dendral learning program produced new results in chemistry (some rules of mass spectrometry) the first scientific discoveries by a computer to be published in a refereed journal.
Mid 70's
Barbara Grosz (SRI) established limits to traditional AI approaches to discourse modeling. Subsequent work by Grosz, Bonnie Webber and Candace Sidner developed the notion of "centering", used in establishing focus of discourse and anaphoric references in NLP.

Alan Kay and Adele Goldberg (Xerox PARC) developed the Smalltalk language, establishing the power of object-oriented programming and of icon-oriented interfaces.
David Marr and MIT colleagues describe the "primal sketch" and its role in visual perception.
1976
Doug Lenat's AM program (Stanford PhD dissertation) demonstrated the discovery model (loosely-guided search for interesting conjectures).

Randall Davis demonstrated the power of meta-level reasoning in his PhD dissertation at Stanford.
Late 70's
Stanford's SUMEX-AIM resource, headed by Ed Feigenbaum and Joshua Lederberg, demonstrates the power of the ARPAnet for scientific collaboration.
1978
Tom Mitchell, at Stanford, invented the concept of Version Spaces for describing the search space of a concept formation program.
Herb Simon wins the Nobel Prize in Economics for his theory of bounded rationality, one of the cornerstones of AI known as "satisficing".

The MOLGEN program, written at Stanford by Mark Stefik and Peter Friedland, demonstrated that an object-oriented representation of knowledge can be used to plan gene-cloning experiments.
1979
Bill VanMelle's PhD dissertation at Stanford demonstrated the generality of MYCIN's representation of knowledge and style of reasoning in his EMYCIN program, the model for many commercial expert system "shells".

Jack Myers and Harry Pople at University of Pittsburgh developed INTERNIST, a knowledge-based medical diagnosis program based on Dr.Myers' clinical knowledge.

Cordell Green, David Barstow, Elaine Kant and others at Stanford demonstrated the CHI system for automatic programming.
The Stanford Cart, built by Hans Moravec, becomes the first computer-controlled, autonomous vehicle when it successfully traverses a chair-filled room and circumnavigates the Stanford AI Lab.
Drew McDermott & Jon Doyle at MIT, and John McCarthy at Stanford begin publishing work on non-monotonic logics and formal aspects of truth maintenance.
1980's
Lisp Machines developed and marketed.
First expert system shells and commercial applications.
1980
Lee Erman, Rick Hayes-Roth, Victor Lesser and Raj Reddy published the first description of the blackboard model, as the framework for the HEARSAY-II speech understanding system.

First National Conference of the American Association of Artificial Intelligence (AAAI) held at Stanford.
1981
Danny Hillis designs the connection machine, a massively parallel architecture that brings new power to AI, and to computation in general. (Later founds Thinking Machines, Inc.)
1983

John Laird & Paul Rosenbloom, working with Allen Newell, complete CMU dissertations on SOAR.
James Allen invents the Interval Calculus, the first widely used formalization of temporal events.
Mid 80's
Neural Networks become widely used with the Backpropagation algorithm (first described by Werbos in 1974).
1985
The autonomous drawing program, Aaron, created by Harold Cohen, is demonstrated at the AAAI National Conference (based on more than a decade of work, and with subsequent work showing major developments).
1987
Marvin Minsky publishes The Society of Mind, a theoretical description of the mind as a collection of cooperating agents.
1989
Dean Pomerleau at CMU creates ALVINN (An Autonomous Land Vehicle in a Neural Network), which grew into the system that drove a car coast-to-coast under computer control for all but about 50 of the 2850 miles.
1990's
Major advances in all areas of AI, with significant demonstrations in machine learning, intelligent tutoring, case-based reasoning, multi-agent planning, scheduling, uncertain reasoning, data mining, natural language understanding and translation, vision, virtual reality, games, and other topics.

Rod Brooks' COG Project at MIT, with numerous collaborators, makes significant progress in building a humanoid robot
Early 90's
TD-Gammon, a backgammon program written by Gerry Tesauro, demonstrates that reinforcement learning is powerful enough to create a championship-level game-playing program by competing favorably with world-class players.
1997
The Deep Blue chess program beats the current world chess champion, Garry Kasparov, in a widely followed match.

First official Robo-Cup soccer match featuring table-top matches with 40 teams of interacting robots and over 5000 spectators.
Late 90's
Web crawlers and other AI-based information extraction programs become essential in widespread use of the world-wide-web.

Demonstration of an Intelligent Room and Emotional Agents at MIT's AI Lab. Initiation of work on the Oxygen Architecture, which connects mobile and stationary computers in an adaptive network.
2000
Interactive robot pets (a.k.a. "smart toys") become commercially available, realizing the vision of the 18th cen. novelty toy makers.

Cynthia Breazeal at MIT publishes her dissertation on Sociable Machines, describing KISMET, a robot with a face that expresses emotions.

The Nomad robot explores remote regions of Antarctica looking for meteorite samples.

Basic Questions
Q. What is artificial intelligence?
A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Q. Yes, but what is intelligence?
A. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.
Q. Isn't there a solid definition of intelligence that doesn't depend on relating it to human intelligence?
A. Not yet. The problem is that we cannot yet characterize in general what kinds of computational procedures we want to call intelligent. We understand some of the mechanisms of intelligence and not others.
Q. Is intelligence a single thing so that one can ask a yes or no question ``Is this machine intelligent or not?''?
A. No. Intelligence involves mechanisms, and AI research has discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give very impressive performances on these tasks. Such programs should be considered ``somewhat intelligent''.
Q. Isn't AI about simulating human intelligence?
A. Sometimes but not always or even usually. On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals. AI researchers are free to use methods that are not observed in people or that involve much more computing than people can do.
Q. What about IQ? Do computer programs have IQs?
A. No. IQ is based on the rates at which intelligence develops in children. It is the ratio of the age at which a child normally makes a certain score to the child's age. The scale is extended to adults in a suitable way. IQ correlates well with various measures of success or failure in life, but making computers that can score high on IQ tests would be weakly correlated with their usefulness. For example, the ability of a child to repeat back a long sequence of digits correlates well with other intellectual abilities, perhaps because it measures how much information the child can compute with at once. However, ``digit span'' is trivial for even extremely limited computers.
However, some of the problems on IQ tests are useful challenges for AI.
Q. What about other comparisons between human and computer intelligence?
A.Arthur R. Jensen [Jen98], a leading researcher in human intelligence, suggests ``as a heuristic hypothesis'' that all normal humans have the same intellectual mechanisms and that differences in intelligence are related to ``quantitative biochemical and physiological conditions''. I see them as speed, short term memory, and the ability to form accurate and retrievable long term memories.
Whether or not Jensen is right about human intelligence, the situation in AI today is the reverse.
Computer programs have plenty of speed and memory but their abilities correspond to the intellectual mechanisms that program designers understand well enough to put in programs. Some abilities that children normally don't develop till they are teenagers may be in, and some abilities possessed by two year olds are still out. The matter is further complicated by the fact that the cognitive sciences still have not succeeded in determining exactly what the human abilities are. Very likely the organization of the intellectual mechanisms for AI can usefully be different from that in people.
Whenever people do better than computers on some task or computers use a lot of computation to do as well as people, this demonstrates that the program designers lack understanding of the intellectual mechanisms required to do the task efficiently.
Q. When did AI research start?
A. After WWII, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing may have been the first. He gave a lecture on it in 1947. He also may have been the first to decide that AI was best researched by programming computers rather than by building machines. By the late 1950s, there were many researchers on AI, and most of them were basing their work on programming computers.
Q. Does AI aim to put the human mind into the computer?
A. Some researchers say they have that objective, but maybe they are using the phrase metaphorically. The human mind has a lot of peculiarities, and I'm not sure anyone is serious about imitating all of them.
Q. What is the Turing test?
A. Alan Turing's 1950 article Computing Machinery and Intelligence [Tur50] discussed conditions for considering a machine to be intelligent. He argued that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. This test would satisfy most people but not all philosophers. The observer could interact with the machine and a human by teletype (to avoid requiring that the machine imitate the appearance or voice of the person), and the human would try to persuade the observer that it was human and the machine would try to fool the observer.
The Turing test is a one-sided test. A machine that passes the test should certainly be considered intelligent, but a machine could still be considered intelligent without knowing enough about humans to imitate a human.
Daniel Dennett's book Brainchildren [Den98] has an excellent discussion of the Turing test and the various partial Turing tests that have been implemented, i.e. with restrictions on the observer's knowledge of AI and the subject matter of questioning. It turns out that some people are easily led into believing that a rather dumb program is intelligent.
Q. Does AI aim at human-level intelligence?
A. Yes. The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans. However, many people involved in particular research areas are much less ambitious.
Q. How far is AI from reaching human-level intelligence? When will it happen?
A. A few people think that human-level intelligence can be achieved by writing large numbers of programs of the kind people are now writing and assembling vast knowledge bases of facts in the languages now used for expressing knowledge.
However, most AI researchers believe that new fundamental ideas are required, and therefore it cannot be predicted when human level intelligence will be achieved.
Q. Are computers the right kind of machine to be made intelligent?
A. Computers can be programmed to simulate any kind of machine.
Many researchers invented non-computer machines, hoping that they would be intelligent in different ways than the computer programs could be. However, they usually simulate their invented machines on a computer and come to doubt that the new machine is worth building. Because many billions of dollars that have been spent in making computers faster and faster, another kind of machine would have to be very fast to perform better than a program on a computer simulating the machine.
Q. Are computers fast enough to be intelligent?
A. Some people think much faster computers are required as well as new ideas. My own opinion is that the computers of 30 years ago were fast enough if only we knew how to program them. Of course, quite apart from the ambitions of AI researchers, computers will keep getting faster.
Q. What about parallel machines?
A. Machines with many processors are much faster than single processors can be. Parallelism itself presents no advantages, and parallel machines are somewhat awkward to program. When extreme speed is required, it is necessary to face this awkwardness.
experience?
A. This idea has been proposed many times, starting in the 1940s. Eventually, it will be made to work. However, AI programs haven't yet reached the level of being able to learn much of what a child learns fromQ. What about making a ``child machine'' that could improve by reading and by learning from physical experience. Nor do present programs understand language well enough to learn much by reading.
Q. Might an AI system be able to bootstrap itself to higher and higher level intelligence by thinking about AI?
A. I think yes, but we aren't yet at a level of AI at which this process can begin.
Q. What about chess?
A. Alexander Kronrod, a Russian AI researcher, said ``Chess is the Drosophila of AI.'' He was making an analogy with geneticists' use of that fruit fly to study inheritance. Playing chess requires certain intellectual mechanisms and not others. Chess programs now play at grandmaster level, but they do it with limited intellectual mechanisms compared to those used by a human chess player, substituting large amounts of computation for understanding. Once we understand these mechanisms better, we can build human-level chess programs that do far less computation than do present programs.
Unfortunately, the competitive and commercial aspects of making computers play chess have taken precedence over using chess as a scientific domain. It is as if the geneticists after 1910 had organized fruit fly races and concentrated their efforts on breeding fruit flies that could win these races.
Q. What about Go?
A. The Chinese and Japanese game of Go is also a board game in which the players take turns moving. Go exposes the weakness of our present understanding of the intellectual mechanisms involved in human game playing. Go programs are very bad players, in spite of considerable effort (not as much as for chess). The problem seems to be that a position in Go has to be divided mentally into a collection of subpositions which are first analyzed separately followed by an analysis of their interaction. Humans use this in chess also, but chess programs consider the position as a whole. Chess programs compensate for the lack of this intellectual mechanism by doing thousands or, in the case of Deep Blue, many millions of times as much computation.
Sooner or later, AI research will overcome this scandalous weakness.
Q. Don't some people say that AI is a bad idea?
A. The philosopher John Searle says that the idea of a non-biological machine being intelligent is incoherent. He proposes the Chinese room argument www-formal.stanford.edu/jmc/chinese.html The philosopher Hubert Dreyfus says that AI is impossible. The computer scientist Joseph Weizenbaum says the idea is obscene, anti-human and immoral. Various people have said that since artificial intelligence hasn't reached human level by now, it must be impossible. Still other people are disappointed that companies they invested in went bankrupt.
Q. Aren't computability theory and computational complexity the keys to AI? [Note to the layman and beginners in computer science: These are quite technical branches of mathematical logic and computer science, and the answer to the question has to be somewhat technical.]
A. No. These theories are relevant but don't address the fundamental problems of AI.
In the 1930s mathematical logicians, especially Kurt Gödel and Alan Turing, established that there did not exist algorithms that were guaranteed to solve all problems in certain important mathematical domains. Whether a sentence of first order logic is a theorem is one example, and whether a polynomial equations in several variables has integer solutions is another. Humans solve problems in these domains all the time, and this has been offered as an argument (usually with some decorations) that computers are intrinsically incapable of doing what people do. Roger Penrose claims this. However, people can't guarantee to solve arbitrary problems in these domains either. See my Review of The Emperor's New Mind by Roger Penrose. More essays and reviews defending AI research are in In the 1960s computer scientists, especially Steve Cook and Richard Karp developed the theory of NP-complete problem domains. Problems in these domains are solvable, but seem to take time exponential in the size of the problem. Which sentences of propositional calculus are satisfiable is a basic example of an NP-complete problem domain. Humans often solve problems in NP-complete domains in times much shorter than is guaranteed by the general algorithms, but can't solve them quickly in general.
What is important for AI is to have algorithms as capable as people at solving problems. The identification of subdomains for which good algorithms exist is important, but a lot of AI problem solvers are not associated with readily identified subdomains.
The theory of the difficulty of general classes of problems is called computational complexity. So far this theory hasn't interacted with AI as much as might have been hoped. Success in problem solving by humans and by AI programs seems to rely on properties of problems and problem solving methods that the neither the complexity researchers nor the AI community have been able to identify precisely.
Algorithmic complexity theory as developed by Solomonoff, Kolmogorov and Chaitin (independently of one another) is also relevant. It defines the complexity of a symbolic object as the length of the shortest program that will generate it. Proving that a candidate program is the shortest or close to the shortest is an unsolvable problem, but representing objects by short programs that generate them should sometimes be illuminating even when you can't prove that the program is the shortest.

Branches of AI
Q. What are the branches of AI?
A. Here's a list, but some branches are surely missing, because no-one has identified them yet. Some of these may be regarded as concepts or topics rather than full branches.
logical AI
What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals. The first article proposing this was [McC59]. [McC89] is a more recent summary. [McC96b] lists some of the concepts involved in logical aI. [Sha97] is an important text.
search
AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving program. Discoveries are continually made about how to do this more efficiently in various domains.
pattern recognition
When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.
representation
Facts about the world have to be represented in some way. Usually languages of mathematical logic are used.
inference
From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we man infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. Circumscription is another form of non-monotonic reasoning.
common sense knowledge and reasoning
This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed. The Cyc system contains a large but spotty collection of common sense facts.
learning from experience
Programs do that. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic. [Mit97] is a comprehensive undergraduate text on machine learning. Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information.
planning
Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions.
epistemology
This is a study of the kinds of knowledge that are required for solving problems in the world.
ontology
Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990s.
heuristics
A heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, may be more useful.

Applications of AI
Q. What are the applications of AI?
A. Here are some.
game playing
You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.
speech recognition
In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
understanding natural language
Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.
computer vision
The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
expert systems
A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.
heuristic classification
One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).

More questions
Q. How is AI research done?
A. AI research has both theoretical and experimental sides. The experimental side has both basic and applied aspects.
There are two main lines of research. One is biological, based on the idea that since humans are intelligent, AI should study humans and imitate their psychology or physiology. The other is phenomenal, based on studying and formalizing common sense facts about the world and the problems that the world presents to the achievement of goals. The two approaches interact to some extent, and both should eventually succeed. It is a race, but both racers seem to be walking.
Q. What are the relations between AI and philosophy?
A. AI has many relations with philosophy, especially modern analytic philosophy. Both study mind, and both study common sense. The best best reference is [Tho03].
Q. What should I study before or while learning AI?
A. Study mathematics, especially mathematical logic. The more you learn about science in general the better. For the biological approaches to AI, study psychology and the physiology of the nervous system. Learn some programming languages--at least C, Lisp and Prolog. It is also a good idea to learn one basic machine language. Jobs are likely to depend on knowing the languages currently in fashion. In the late 1990s, these include C++ and Java.
Q. What is a good textbook on AI?
A. Artificial Intelligence by Stuart Russell and Peter Norvig, Prentice Hall is the most commonly used textbbook in 1997. The general views expressed there do not exactly correspond to those of this essay. Artificial Intelligence: A New Synthesis by Nils Nilsson, Morgan Kaufman, may be easier to read. Some people prefer Computational Intelligence by David Poole, Alan Mackworth and Randy Goebel, Oxford, 1998.
Q. What organizations and publications are concerned with AI?
A. The American Association for Artificial Intelligence (AAAI), the European Coordinating Committee for Artificial Intelligence (ECCAI) and the Society for Artificial Intelligence and Simulation of Behavior (AISB) are scientific societies concerned with AI research. The Association for Computing Machinery (ACM) has a special interest group on artificial intelligence SIGART.
The International Joint Conference on AI (IJCAI) is the main international conference. The AAAI runs a US National Conference on AI. Electronic Transactions on Artificial Intelligence, Artificial Intelligence, and Journal of Artificial Intelligence Research, and IEEE Transactions on Pattern Analysis and Machine Intelligence are four of the main journals publishing AI research papers. I have not yet found everything that should be in this paragraph.
Page of Positive Reviews lists papers that experts have found important.
Funding a Revolution: Government Support for Computing Research by a committee of the National Research covers support for AI research.

Bibliography
Den98
Daniel Dennett. Brainchildren: Essays on Designing Minds. MIT Press, 1998.
Jen98
Arthur R. Jensen. Does IQ matter? Commentary, pages 20-21, November 1998. The reference is just to Jensen's comment--one of many.
McC59
John McCarthy. Programs with Common Sense. In Mechanisation of Thought Processes, Proceedings of the Symposium of the National Physics Laboratory, pages 77-84, London, U.K., 1959. Her Majesty's Stationery Office. Reprinted in [McC90].
McC89
John McCarthy. Artificial Intelligence, Logic and Formalizing Common Sense. In Richmond Thomason, editor, Philosophical Logic and Artificial Intelligence. Klüver Academic, 1989.
McC90
John McCarthy. Formalizing Common Sense: Papers by John McCarthy. Ablex Publishing Corporation, 1990.
McC96a
John McCarthy. Defending AI research : a collection of essays and reviews. CSLI lecture notes: no. 49. Center for the Study of Language and Information, 1996. distributed by Cambridge University Press.
McC96b
John McCarthy. Concepts of Logical AI, 1996. Web only for now but may be referenced.
Mit97
Tom Mitchell. Machine Learning. McGraw-Hill, 1997.
Sha97
Murray Shanahan. Solving the Frame Problem, a mathematical investigation of the common sense law of inertia. M.I.T. Press, 1997.
Tho03
Richmond Thomason. Logic and artificial intelligence. In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Fall 2003. http://plato.stanford.edu/archives/fall2003/entries/logic-ai/.
Tur50
Alan Turing. Computing machinery and intelligence. Mind, 1950.

An Overview.AI is a result of the merge of philosophy, mathematics, psychology, neurology, linguistics, computer science, and many other fields. Futhermore, the application of AI relates to almost any fields. This variety gives AI an endless potential. A relatively young science, AI has made much progress in 50 years. Though fast-growing, AI has never actually caught up with all the expectation imposed on it. There are two reasons for public's over-confidence in AI. First, AI theories are often ingenious and subtle even fictional, implying much futuristic applications. Second, AI, being incorporated with computer technology, is often expected to progress as fast as the computer technology. Conclusionally, AI is a young, energetic, and attractive science.

EXAMPLES

The branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes
games playing: programming computers to play games such as chess and checkers
expert systems: programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms)
natural language: programming computers to understand natural human languages
neural networks: Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains
robotics: programming computers to see and hear and react to other sensory stimuli Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). 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.
In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily.
Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. Some rudimentary translation systems that translate from one human language to another are in existence, but they are not nearly as good as human translators. There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing; they simply take dictation. Even these systems are quite limited -- you must speak slowly and distinctly.
In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. To date, however, they have not lived up to expectations. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations.
Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing.

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