21-05-2013, 12:36 PM
Expert System
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Introduction
An expert system is software that uses a knowledge base of human expertise for problem solving, or to clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and are a traditional application and/or subfield of artificial intelligence (AI). A wide variety of methods can be used to simulate the performance of the expert; however, common to most or all are: 1) the creation of a knowledge base which uses some knowledge representation structure to capture the knowledge of the Subject Matter Expert (SME); 2) a process of gathering that knowledge from the SME and codifying it according to the structure, which is called knowledge engineering; and 3) once the system is developed, it is placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or as a supplement to some information system. Expert systems may or may not have learning components.
Components
An expert system has four major components:
1. Natural (software) language interface for the user
2. Knowledge base (Like a database, where the facts are stored)
3. Inference machine (software that solves problems and makes logical inferences)
4. Explanation (which explains its conclusions to the user)
One of the most famous expert systems-an older system now being replaced by updated ones is MYCIN, a system that diagnosis infectious disease and recommends appropriate drugs. For example, bacterium can be fatal if it not treated quickly. Unfortunately, tradition tests for it require 24 to 48 hours to verify a diagnosis. However, MYCIN provides physicians with a diagnosis and recommended therapy within minutes.
Expert System Features
There are a number of features which are commonly used in expert systems. Some shells provide most of these features, and others just a few. Customized shells provide the features which are best suited for the particular problem. The major features covered in this book are:
• Goal driven reasoning or backward chaining - an inference technique which uses IF THEN rules to repetitively break a goal into smaller sub-goals which are easier to prove;
• Coping with uncertainty - the ability of the system to reason with rules and data which are not precisely known;
• Data driven reasoning or forward chaining - an inference technique which uses IF THEN rules to deduce a problem solution from initial data;
• Data representation - the way in which the problem specific data in the system is stored and accessed;
• User interface - that portion of the code which creates an easy to use system;
• Explanations - the ability of the system to explain the reasoning process that it used to reach a recommendation.
Advantages and disadvantages
Advantages
• Compared to traditional programming techniques, expert-system approaches provide the added flexibility (and hence easier modifiability) with the ability to model rules as data rather than as code. In situations where an organization's IT department is overwhelmed by a software-development backlog, rule-engines, by facilitating turnaround, provide a means that can allow organizations to adapt more readily to changing needs.
• In practice, modern expert-system technology is employed as an adjunct to traditional programming techniques, and this hybrid approach allows the combination of the strengths of both approaches. Thus, rule engines allow control through programs (and user interfaces) written in a traditional language, and also incorporate necessary functionality such as inter-operability with existing database technology.
Disadvantages
• The Garbage In, Garbage Out (GIGO) phenomenon: A system that uses expert-system technology provides no guarantee about the quality of the rules on which it operates. All self-designated "experts" are not necessarily so, and one notable challenge in expert system design is in getting a system to recognize the limits to its knowledge.
• 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 are 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 drawing much recognition.
Knowledge Engineering Principles
Since the mid-1980s, knowledge engineers have developed a number of principles, methods and tools that have considerably improved the process of knowledge acquisition. Some of the key principles are summarised as follows:
• Knowledge engineers acknowledge that there are different types of knowledge, and that the right approach and technique should be used for the knowledge required.
• Knowledge engineers acknowledge that there are different types of experts and expertise, such that methods should be chosen appropriately.
• Knowledge engineers recognise that there are different ways of representing knowledge, which can aid the acquisition, validation and re-use of knowledge.
• Knowledge engineers recognise that there are different ways of using knowledge, so that the acquisition process can be guided by the project aims.
• Knowledge engineers use structured methods to increase the efficiency of the acquisition process.
KA Techniques
Many techniques have been developed to help elicit knowledge from an expert. These are referred to as knowledge elicitation or knowledge acquisition (KA) techniques. The term "KA techniques" is commonly used.
The following list gives a brief introduction to the types of techniques used for acquiring, analysing and modelling knowledge:
• Protocol-generation techniques include various types of interviews (unstructured, semi-structured and structured), reporting techniques (such as self-report and shadowing) and observational techniques
• Protocol analysis techniques are used with transcripts of interviews or other text-based information to identify various types of knowledge, such as goals, decisions, relationships and attributes. This acts as a bridge between the use of protocol-based techniques and knowledge modelling techniques.
• Hierarchy-generation techniques, such as laddering, are used to build taxonomies or other hierarchical structures such as goal trees and decision networks.
• Matrix-based techniques involve the construction of grids indicating such things as problems encountered against possible solutions. Important types include the use of frames for representing the properties of concepts and the repertory grid technique used to elicit, rate, analyse and categorise the properties of concepts.
• Sorting techniques are used for capturing the way people compare and order concepts, and can lead to the revelation of knowledge about classes, properties and priorities.
• Limited-information and constrained-processing tasks are techniques that either limit the time and/or information available to the expert when performing tasks. For instance, the twenty-questions technique provides an efficient way of accessing the key information in a domain in a prioritised order.
• Diagram-based techniques include the generation and use of concept maps, state transition networks, event diagrams and process maps. The use of these is particularly important in capturing the "what, how, when, who and why" of tasks and events.
HOW GOOD AN EXPERT IS MYCIN?
MYCIN has been evaluated in several different ways. Its success with several hundred cases has confirmed its competence in identifying the infectious agents, selecting appropriate doses of effective drugs, and recommending additional diagnostic tests. In one complex evaluation, eight independent evaluators with special expertise in the management of meningitis compared MYCIN's choice of medicines with the choices prescribed by nine human diagnosticians for difficult cases of meningitis. The task used for this test was the selection of drugs for cases of infectious meningitis before the causitive agent had been identified. In the first phase of the evaluation, MYCIN and faculty members in the Stanford University Medical School's Division of Infectious Diseases each evaluated 10 cases that had been chosen to offer a wide variety of difficult problems.