... ed in a more efficient manner then making a whole new system from scratch (O'Shea 162). Expert systems have proven effective in a number of problem domains that usually require human intelligence (Patterson 326). They were developed in the research labs of universities in the 1960's and 1970's. Expert systems are primarily used as specialized problem solvers. The areas that this can cover are almost endless.
This can include law, chemistry, biology, engineering, manufacturing, aerospace, military operations, finance, banking, meteorology, geology, and more. Expert systems use knowledge instead of data to control the solution process. 'In knowledge lies the power' is a theme repeated when building such systems. These systems are capable of explaining the answer to the problem and why any requested knowledge was necessary. Expert systems use symbolic representations for knowledge and perform computations through manipulations of the different symbols (Patterson 329). But perhaps the greatest advantage to expert systems is their ability to realize their limits and capabilities.
Case-based reasoning (CBR) is similar to expert system because theoretically they could use they same set of data. CBR has been proposed as a more psychologically plausible model of the reasoning used by an expert while expert systems use more fashionable rule-based reasoning systems (Ries beck 9). This type of system uses a different computational element that decides the outcome of a given input. Instead of rules in an expert system, CBR uses cases to evaluate each input uniquely.
Each case would be matched to what a human expert would do in a specific case. Additionally this system knows no right answers, just those that were used in former cases to match. A case library is set up and each decision is stored. The input question is characterized to appropriate features that are recognizable and is matched to a similar past problem and its solution is then applied. Now that each type of implementation of AI has been discussed, how do we use all this technology? Foremost, neural networks are used mainly for internal corporate applications in various types of problems. For example, Troy Nolen was hired by a major defense contractor to design programs for guiding flight and battle patterns of the YF-22 fighter.
His software runs on five on-board computers and makes split-second decisions based on data from ground stations, radar, and other sources. Additionally it predicts what the enemy planes would do, guiding the jet's actions consequently (Schwartz 136). Now he and many others design financial software based on their experience with neural networks. Nolen works for Merrill Lynch & Co.
to develop software that will predict the prices of many stocks and bonds. Murry Ruggiero also designs software, but his forecasts the future of the Standard & Poors index. Ruggiero's program, called Brain Cel, is capable of giving an annual return of 292%. Another major application of neural networks is detecting credit card fraud. Mellon Bank, First Bank, and Colonial National Bank all use neural networks that can determine the difference between fraud and regular transactions (Bylinsky 98). Mellon Bank states the new neural network allows them to eliminate 90% of the false alarms that occur under traditional detection systems (Bylinsky 99).
Secondly, fuzzy logic has many applications that hit close to home. Home appliances win most of the ground with AI enhanced washing machines, vacuum cleaners, and air-conditioners. Hitachi and Matsu censored a manufacture washing machines that automatically adjust for load size and how dirty the articles are (Shine 57). This machine washes until clean, not just for ten minutes. Matsu censored a also manufactures vacuum cleaners that adjust the suction power according to the volume of dust and the nature of the floor. Lastly, Mitsubishi uses fuzzy logic to slow air-conditioners gradually to the desired temperature.
The power consumption is reduced by 20% using this system (Schmuller 27). The chaos theory is limited in scope at this time mainly because of lack of interest and resources to experiment with. However, Wall Street will be hearing more about it for a long time to come. Also, the medical field has an interest because of its ability to distinguish between natural and non-natural patterns. The chaos theory has a foot in the door, but a breakthrough in design will have to come around first before any major moves toward the chaos theory will happen.
Expert systems are prevalent all over the world. This proven technology has made its way into almost everywhere that human experts live. Expert systems even can show an employee how to be an expert in a particular occupation. A Massachusetts company specializes in teaching good judgment to new employees or trainees. Called Wisdom Simulators, this company sells software that simulates nasty job situations in the business world. The ability to learn before the need arises attracts many customers to this type of software (Nad is 8).
Expert systems have also been applied in medical facilities, diagnosis of mechanical devices, planning scientific experiments, military operations, and teaching students specialized tasks. Knowledge-based systems and case-based reasoning will be on the rise for a long time to come. These systems are souped-up expert systems that provide more powerful searching and decision-making strategies. KBS is finding its home at help desks by working with telephone operators to direct calls. CBR will have close ties to law with its ability to use past precedents to determine a sentence and prison term. KBS is already being used by the Tennessee Department of Corrections for determining which inmates are eligible for parole (Peterson 37).
Making recommendations on which AI systems work the best almost requires AI itself. However, I believe that some are definitely better than others. Neural networks, unfortunately, have performance spectrum's that continue to dwell at both extremes. While there are some very good networks that perform their designed task beautifully, there are others that perform miserably. Furthermore, these networks require massive amounts of computing resources that restrict their use to those who can afford it. On the other hand, fuzzy logic is practically a win-win situation.
Although some are rather simple, these systems perform their duties quickly and accurately without expensive equipment. They can easily replace many mundane tasks that others computer systems would have trouble with. Fuzzy logic has enabled computers to calculate such terms as 'large' or 'several' that would not be possible without it (Schmuller 14). On the other hand, the chaos theory has potential for handling an infinite amount of variables. This gives it the ability to be a huge success in the financial world. It's high learning curve and its primitive nature, however, limits it to testing purposes only for the time being.
It will be a rocky road for chaos theory and chaos engineering for several years. Finally, expert systems, knowledge-based systems, and cased-based reasoning systems are here to stay for a long time. They provide an efficient, easy to use program that yields results that no one can argue with. Designed correctly, they are can be easily updated and modernized.
While the massive surge into the information age has ushered some old practices out of style, the better ones have taken over with great success. The rate of advancement may seem fast to the average person, but the technology is being put to good use and is not out of control. A little time to experiment with the forefront technologies and society will be rewarded with big pay-offs. Soon there will be no place uncharted and no stone unturned. Computers are the future in the world and we should learn to welcome their benefits and improve their shortcomings to enrich the lives of the world. Work Cited 166-173, 1997.
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