Although the title of the book mentions the phrase 'Expert Systems', the book is in Principles of Expert Systems by Peter Lucas and Linda van der Gaag is a. PDF | Book on knowledge-based (expert) systems, published in Contains a description of principal methods and techniques and implementations in. Expert Systems. Source: Expert Systems, Book edited by: Petrică Vizureanu,. ISBN , pp. , January , INTECH, Croatia, downloaded .
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This monograph provides an introduction to the theory of expert systems. The task of medical diagnosis is used as a unifying theme throughout. A. ARTIFICIAL INTELLIGENCE – Expert Systems - Peter Lucas Summary. Expert systems, also called knowledge-based systems or knowledge systems, are .. [ Book describing a restrictive method for abductive diagnosis using set theory.]. Times Books), celebrates the advances that expert systems have made in the s. Written by Edward Feigenbaum of Stanford. University, widely regarded as.
It would match R1 and assert Mortal Socrates into the knowledge base. Backward chaining is a bit less straight forward. In backward chaining the system looks at possible conclusions and works backward to see if they might be true. So if the system was trying to determine if Mortal Socrates is true it would find R1 and query the knowledge base to see if Man Socrates is true.
One of the early innovations of expert systems shells was to integrate inference engines with a user interface. This could be especially powerful with backward chaining.
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If the system needs to know a particular fact but does not, then it can simply generate an input screen and ask the user if the information is known. So in this example, it could use R1 to ask the user if Socrates was a Man and then use that new information accordingly. The use of rules to explicitly represent knowledge also enabled explanation abilities. In the simple example above if the system had used R1 to assert that Socrates was Mortal and a user wished to understand why Socrates was mortal they could query the system and the system would look back at the rules which fired to cause the assertion and present those rules to the user as an explanation.
In English if the user asked "Why is Socrates Mortal? A significant area for research was the generation of explanations from the knowledge base in natural English rather than simply by showing the more formal but less intuitive rules. These systems record the dependencies in a knowledge-base so that when facts are altered, dependent knowledge can be altered accordingly. For example, if the system learns that Socrates is no longer known to be a man it will revoke the assertion that Socrates is mortal.
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Hypothetical reasoning. In this, the knowledge base can be divided up into many possible views, a. This allows the inference engine to explore multiple possibilities in parallel. For example, the system may want to explore the consequences of both assertions, what will be true if Socrates is a Man and what will be true if he is not?
Uncertainty systems. One of the first extensions of simply using rules to represent knowledge was also to associate a probability with each rule.
So, not to assert that Socrates is mortal, but to assert Socrates may be mortal with some probability value. Simple probabilities were extended in some systems with sophisticated mechanisms for uncertain reasoning, such as Fuzzy logic , and combination of probabilities.
Ontology classification. With the addition of object classes to the knowledge base, a new type of reasoning was possible.
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Along with reasoning simply about object values, the system could also reason about object structures. It can give reliable advice in a specific area of expertise its domain and get new conclusions about difficult activities to examine[ 1 ].
An expert system can explain its reasoning everytime and is able to interact with a user in the same way that you might consult a human expert. A human expert can have unsurpassed knowledge in the field and can gain as much knowledge as possible, but be hopeless explaining that to someone else.
Human experts cannot be available all the time; i. Figure 1.
General description for human expert vs. Figure 2. Main elements of an expert system. Figure 3. Logical flow inside an expert system.
Figure 4. Forward-chaining technique for an expert system. Figure 5. Backward-chaining technique for an expert system. An expert system does not get tired.
An expert system should be realized to explain and justify all advices it gives. Although an expert system can be expensive to develop, once it is there, its running costs should be low, so there will be economic benefits for the company.
An expert system is always available. You can take it with you if you have a notebook or Internet connection, so you could consult an expert system over the Internet.
Many applications of expert systems are very well known: Prospector—used by geologists to identify sites for drilling or mining; PUFF—medical system for diagnosis of respiratory conditions; Design Advisor—gives advice to designers of processor chips; MYCIN—medical system for diagnosing blood disorders first used in ; LITHIAN—gives advice to archeologists examining stone tools;and DENDRAL—used to identify the structure of chemical compounds first used in It then displays the results to the user[ 3 ].Generalized evaluations in sociological researches.
Also, these can be dedicated to a new knowledge representation model, providing convergence of classical operations research and modern knowledge engineering or to a mathematical model that graphically and numerically represents the probabilistic relationships between random variables.
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Lesser, and D. Human experts cannot be available all the time; i. Shiva Kumar and Mr.
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