I B.TECH V___ARTIFIFICIAL INTEIGENCE R20 SEM UNIT-V Expert Systems What is expert system? The expert system is a computer program which is developed by using Al technologies to solve the complex problems ina particular field. The computer program consists of expert level knowledge to respond properly. The expert system should be reliable, highly responsive and understandable. What are Expert Systems? The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise. The system helps in decision making for complex problems using both facts and heuristics like a human expert. It is called so because it contains the expert knowledge of a specific domain and can solve any complex problem of that particular domain, These systems are designed for a specific domain, such as medicine, science, ctc. The performance of an expert system is based on the expert's knowledge stored in its knowledge base. The more knowledge stored in the KB, the more that system improves its performance. One of the common examples of an ES is a suggestion of spelling errors while typing in the Google search box. Characteristics of Expert Systems © High Performance: The expert system provides high performance for solving any type of complex problem of a specific domain with high efficiency and accuracy. © Understandable: It responds in a way that can be easily understandable by the user. It can take input in human language and provides the output in the same way. © Reliable: It is much reliable for generating an efficient and accurate output. © Highly responsive: ES provides the result for any complex query within a very short petiod of time. Capabilities of Expert Systems + Advising = Explaining + Instueting and assisting human in ‘© Interpreting input decision making ‘+ Predicting results »* Demonstrating «Justifying the conclusion * Deriving a solution © Suggesting alternative options to a + Diagnosing problem Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 1Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 ‘They are incapable of — + Substituting human decision makers + Possessing human capabilities © Producing accurate output for inadequate knowledge base + Refining their own knowledge Components of Expert System User Interface Inference Engine Knowledge Base Knowledge Base walnuts Fig: Expert System The basic components of an expert system are given below: 1. User interface Itis a software which provides communication access between user and the system. For example: If an user asks questions, then the system responds with an answer 2. Knowledge base Knowledge base contains expert level knowledge of a particular field that is stored in knowledge representational form, 3. Inference engine The Inference engine is a software used to perform the inference reasoning tasks. It uses the knowledge which is stored in the knowledge base and then the information is provided by the user to conclude a new knowledge. Eo aay Human Knowledge Expert Engineer User (May not be an expert) Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 2Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 Let us see them one by one briefly - Knowledge Base It contains domain-specific and high-quality knowledge. Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate and precise knowledge. What is Knowledge? The data is collection of facts. The information is organized as data and facts about the task domain. Data, information, and past experience combined together are termed as knowedge. Components of Knowledge Base The knowledge base of an ESis a store of both, factual and heuristic knowledge + Factual Knowledge - It is the information widely accepted by the Knowledge Engineers and scholars in the task domain. + Heuristic Knowledge - It is about practice, accurate judgment, one's ability of evaluation, and guessing Knowledge representation It is the method used to organize and formalize the knowledge in the knowledge base, Itis in the form of IF-THEN-ELSE rules. Knowledge Acquisition It is the process of extracting, organizing, and structuring the domain knowledge, specifying the rules to acquire the knowledge from various experts, and store that knowledge into the knowledge base. Inference Engine In case of knowedge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution. In case of rule based ES, it - + Applies rules repeatedly to the facts, which are obtained from earlier rule application. + Adds new knowledge into the knowledge base if required + Resolves rules conflict when multiple rules are applicable to a particular case To recommend a solution, the Inference Engine uses the following strategies - + Forward Chaining * Backward Chaining Forward Chaining strategy of an expert system to answer the question, “What can happen Here, the Inference Engine follows the chain of conditions and derivations and finally deduces the outcome. It considers all the facts and rules, and sorts them before concluding to a solution Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 3Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 This strategy is followed for working on conclusion, result, or effect. For example, prediction of share market status as an effect of changes in interest rates. Peereney Backward Chaining With this strategy, an expert system finds out the answer to the question, “Why this happened?” On the basis of what has already happened, the Inference Engine tries to find out which conditions could have happened in the past for this result. This strategy is followed for finding out cause or reason. For example, diagnosis of blood cancer in humans. Pena Peete User Interface User interface provides interaction between user of the Expert System and the Expert S; itself. It is generally Natural Language Processing so as to be used by the user wh versed in the task domain. The user of the Expert System need not be necessarily an expert in Artificial Intelligence. Expert System forms — * Natural language displayed on screen. # Verbal narrations in natural language. . ing of rule numbers displayed on the screen. The user interface makes it easy to uace the credibility of the deductions. Requirements of Efficient Expert Systems User Interface + It should help users to accomplish their goals in shortest possible way. + Itshould be designed to work for nser’s existing or desired work practices + Its technology should be adaptable to user’s requirements; not the other way round. + Itshould make efficient use of user input Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 4I B.TECH SEM UNr V___ARTIFIFICIAL INTEIGENCE R20 Development of Expert Systems: General Steps The process of Expert System development is iterative. Steps in developing the Expert System include ~ Identify Problem Domain + The problem must be suitable for an expert system to solve it, + Find the experts in task domain for the ES project. * Establish cost-effectiveness of the system. Design the System + Identify the ES Technology + Know and establish the degree of integration with the other systems and databases. * Realize how the concepts can represent the domain knowledge best. Develop the Prototype From Knowledge Base’ The knowledge engineer works to — + Acquire domain knowledge from the expert. + Represent it in the form of If THEN-ELSE rules. ‘Test and Refine the Prototype + The knowledge engineer uses sample cases to test the prototype for any deficiencies in performance. + End users test the prototypes of the ES. Develop and Complete the Expert System + Test and ensure the interaction of the ES with all elements of its environment, including end users, databases, and other information systems. + Document the ES project well, + Train the user to use ES. Maintain the System * Keep the knowledge base up-to-date by regular review and update. + Cater for new interfaces with other information systems, as those systems evolve. Participants in the development of Expert System There are three primary participants in the building of Expert System: 1, Expert: The success of an ES much depends on the knowledge provided by human experts, These experts are those persons who are specialized in that specific domain, 2. Knowledge Engineer: Knowledge engineer is the person who gathers the knowledge from the domain experts and then codifies that knowledge to the system according to the formalism, 3. End-User: This is a particular person or a group of people who may not be experts. and working on the expert system needs the solution or advice for his queries, which are complex. Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 5Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 Benefits of Expert System Some important benefits of an expert system are listed below: + Availability ~ They are easily available due to mass production of software + Less Production Cost ~ Production cost is reasonable. This makes them affordable + Speed ~ They offer great speed. They reduce the amount of work an individual puts in. + Less Error Rate ~ Error rate is low as compared to human errors, + Reducing Risk ~ They can work in the environment dangerous to lnumans. + Steady response ~ They work steadily without getting motional, tensed or fatigued + Knowledge Sharing Below are some popular examples of the Expert System: © DENDRAL: It was an antificial intelligence project that was made as a chemical analysis expert system. It was used in organic chemistry to detect unknown organic molecules with the help of their mass spectra and knowledge base of chemistry © MYCIN: It was one of the earliest backward chaining expert systems that was designed to find the bacteria causing infections like bacteraemia and meningitis. It was also used for the recommendation of antibiotics and the diagnosis of blood clotting diseases. © PXDES: It is an expert system that is used to determine the type and level of lung cancer. To determine the disease, it takes a picture from the upper body, which looks like the shadow. This shadow identifies the type and degree of harm, © CaDeT: The CaDet expert system is a diagnostic support system that can detect cancer at early stages. Advantages of Expert System © These systems are highly reproducible. © They can be used for risky places where the human presence is not safe. © Error possibilities are less if the KB contains correct knowledge. not affected by emotions, © The performance of these systems remains steady as it tension, or fatigue. © They provide a very high speed to respond to a particular query. Limitations of Expert System © The response of the expert system may get wrong if the knowledge base contains the wrong information. © Like a human being, it cannot produce a creative output for different scenarios, © Its maintenance and development costs are very high. © Knowledge acquisition for designing is much difficult. © Itcannot learn from itself and hence requires manual updates. Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 6I B.TECH SEM UNr V__ARTIFIFICIAL INTEIGENCE R20 Applications of Expert System The following table shows where Expert System can be applied. Application Description Design Domain Camera lens design, automobile design. Diagnosis Systems to deduce cause of disease from observed data, Medical Domain conduction medical operations on humans. ‘Comparing data continuously with observed system or with Monitoring Systems prescribed behavior such as leakage monitoring in long petroleum pipeline. Process Control Systems Controlling a physical process based on monitoring, Knowledge Domain Finding out faults in vehicles, computers. Detection of possible fraud, suspicious transactions, stock market trading, Airline scheduling, cargo scheduling. Finance/Commerce The Expert systems have found their way into most areas of knowledge work. The applications of expert systems technology have widely proliferated commercial problems, and to industrial even helping NASA to plan the maintenance of a space shuttle for its next flight Diagnosis and Troubleshooting of Devices and Systems © Medical diagnosis was one of the first knowledge areas to which Expert system technology was applied in 1976. However, the diagnosis of engineering systems quickly surpassed medical diagnosis. Planning and Scheduling © The Expert system's commercial potential in planning and scheduling has been ree! Examples are airlines scheduling their flights, personnel, ized as very la and gates; the manufacturing process planning and job scheduling; Configuration of Manufactured Objects from sub-assemblies nfiguration problems are synthesized from a given set of elements related by a set of constraints. The Expert systems have been very useful to find solutions. For example, modular home building and manufacturing involving complex engineering design, nm Making © The financial services are the vigorous user of expert system techniques. Advisory programs have been created to assist bankers in determining whether to make loans to businesses and individuals. Insurance companies to assess the risk presented by the customer and to determine a price for the insurance. Expert Systems are used in typical applications in the financial markets / foreign exchange trading. Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 7Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 Knowledge Pub! ing © This is relatively new, but also potentially explosive area. Here the primary function of the Expert system is to deliver knowledge that is relevant to the user's problem, The two most widely known Expert systems are : one, an advisor on appropriate grammatical usage in a text; and the other, is a tax advisor on tax strategy, tactics, and individual tax policy. Process Monitoring and Control © Here Expert system does analysis of real-time data from physical devices, looking for anomalies, predicting trends, controlling optimality and failure correction. Examples of real-time systems that actively monitor processes are found in the steel making and oil refining industries. Design and Manufacturing © Here the Expert systems assist in the de: ranging from high-level conceptual desi floor configuration of manufacturing proces ign of physical devices and processes, of abstract entities all the way to factory Why Expert System? Why Expert / ftessssin | Cun ~ "Regular updates improve the e 4 performance Before using any technology, we must have an idea about why to use that technology and hence the same for the ES. Although we have human experts in every field, then what is the need to develop a computer-based system? So below are the points that are describing the need of the Expert Systems Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 8Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 1, No memory Limitations: It can store as much data as required and can memorize it at the time of its application, But for human experts, there are some limitations to ‘memorize all things at every time. 2. High Efficiency: If the knowledge base is updated with the correct knowledge, then it provides a highly efficient output, which may not be possible for a human. 3. Expertise in a domain: There are lots of human experts in each domain, and they all have different skills, different experiences, and different skills, so it is not easy to get a final output for the query. But if we put the knowledge gained from human experts into ing all the facts and the expert system, then it provides an efficient output by mi: knowledge 4. Not affected by emotions: These systems are not affected by human emotions such as fatigue, anger, depression, anxiety, etc... Hence the performance remains constant 5. High security: These systems provide high security to resolve any query. 6. Considers all the facts: To respond to any query, it checks and considers all the available facts and provides the result accordingly. But it is possible that a human expert may not consider some facts due to any reason. 7. Regular updates improve the performance: If there is an issue in the result provided by the expert systems, we can improve the performance of the system by updating the knowledge base. Knowledge Base (Representing and Using Domain Knowledge) Expert system is built around a knowledge base module. Expert system contains a formal representation of the information provided by the domain expert. This information may be in the form of problem-solving rules, procedures, or data intrinsic to the domain. To incorporate this information into the system, it is necessary to make use of one or more knowledge representation methods. Some of these methods are described here. * Transferring knowledge from the human expert to a computer is often the most difficult part of building an expert system. © The knowledge acquired from the human expert must be encoded in such a way that it remains a faithful representation of what the expert knows, and it can be manipulated by a computer. Three common methods of knowledge representation evolved over the years are IF-THEN rules, Semantic networks and Frames. 1, IF-THEN rules Human experts usually tend to think along: condition = action or Situation = conclusion Rules "if-then" are predominant form of encoding knowledge in expert systems, These are of the form : Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 9Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 if al,a2,.....,an then bl , b2, bn where each ai is acondition or situation, and eachbi- is anactionor a conclusion. 2. Semantic Networks In this scheme, knowledge is represented in terms of objects and relationships between objects The objects are denoted as nodes of a graph. The relationship between two objects are denoted as a link between the corresponding two nodes. The most common form of semantic networks uses the links between nodes to represent IS-A and HAS relationships between objects. Example of Semantic Network The Fig. below shows a car IS-A vehicle; a vehicle HAS wheels. This kind of relationship establishes an inheritance in the network, with the objects lower down in the network inheriting properties from the objects higher up HAS Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 10Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 3. Frames In this technique, knowledge is decomposed into highly modular pieces called frames, which are generalized record structures. Knowledge consists of concepts, situations, attributes of concepts, relationships between concepts, and procedures to handle relationships as well as attribute values. Each concept may be represented as a separate frame. The attributes, the relationships between concepts and the procedures are allotted to slots in a frame. © The comtents of a slot may be of any data type - numbers, strings, functions ot procedures and so on. * The frames may be linked to other frames, providing the same kind of inheritance as that provided by a semantic network. A. frame-based_ representation jeally suited for objected-oriented programming techniques. Example: Frame-based Representation of Knowledge. Two frames, their slots and the slots filled with data type are shown. eed eae pate ares pee eae eros eer erorss esr eiorss esr itetorss erty eet lHonda | (sl —] | | = | | | | Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 11Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 Working Memory The Working memory is related to user interface. Working memory refers to task- specific data for a problem. The content of the working memory changes with each problem situation. Consequently, it is the most dynamic component of an expert system, assuming that itis kept current. Every problem in a domain has some unique data associated with it. > Data may consist of the set of conditions leading to the problem, its parameters and soon, > Data specific to the problem needs to be input by the user at the time of using, means consulting the expert system. Fig. below shows how Working memory is closely related to user interface of the expert system. Expert System Technology ‘There are several levels of ES technologies available. Expert systems technologies include — Expert System Development Environment — The Expert System development environment includes hardware and tools. They are — + Hardware, Languages and Storage: © Workstations, minicomputers, mainframes. High level Symbolic Programming Languages such as LISP and PROLOG co Large databases. + Tools ~ they reduce the effort and cost involved in developing an expert system to large extent. © Powerful editors and debugging tools with multi-windows. © They provide rapid prototyping o Have Inbuilt definitions of model, knowledge representation, and inference design. Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 12Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 Expert System Shells An Expert system shell is a software development environment. It contains the basic components of expert systems. A shell is associated with a prescribed method for building applications by configuring and instantiating these components. What is shell? A shell is nothing but an expert system without knowledge base. A shell provides the developers with knowledge acquisition, inference engine, user interface & explanation facility. For example, few shells are given below ~ © Java Expert System Shell (JESS) that provides fully developed Java API co Vidwan, a shell developed at the National Centre for Software Technology, Mumbai in 1993, Shell components and description ‘The generic components of a shell: the knowledge acquisition, the knowledge Base, the reasoning, the explanation and the user interface are shown below. The knowledge base and reasoning engine are the core components, Knowledge Engineer = Knowledge Base A store of factual and heuristic knowledge. Expert system tool provides one or more knowledge representation schemes for expressing knowledge about the application domain. Some tools use both Frames (objects) and IF-THEN rules. In PROLOG the knowledge is represented as logical statements. m= Inference Engine Inference mechanisms for manipulating the symbolic information and knowledge in the knowledge base form a line of reasoning in solving a problem. The inference ‘mechanism can range from simple modus ponens backward chaining of IF-THEN rules to Case-Based reasoning. = Knowledge Acquisition subsystem A subsystem (o help experts in build knowledge bases. However, collecting knowledge, needed to solve problems and build the knowledge base, is the biggest bottleneck in building expert systems. Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 13Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 = Explanation subsystem A subsystem that explains the system's actions. The explanation can range from how the final or intermediate solutions were arrived at justifying the need for additional data. = User Interface A means of communication with the user. The user interface is generally not a part of the expert system technology. It was not given much attention in the past. However, the user interface can make a critical difference in the utility of an Expert system. For Example: = Shell manages the input and output operations = Shell processes the information which is provided by the user and then compares with the concept stored in the knowledge base, and provides the solution for a particular problem. Explanation + Most expert systems have explanation facilities that allow the user to ask questions - why and how it reached some conclusion. + The questions are answered by referring to the system goals, the rules being used. and existing problem solving. The rules typically reflect empirical or “compiled” knowledge Example : Dialog with an expert system designed to give advice on car problems. Q. System Does the car A. User No. Q. System Does the engine turn over ? A. User Yes. Q. System Do you smell gas ? A. User Yes Q. System Recommendation - Wait 5 minutes and try again. A. User Why Q. System I used the rule: A. If not start, and engine_turn_over, and smell_gas Then recommendation is "Wait 5 minutes and try again’ User Note: The rule gives the correct advice for a flooded car, and knows the questions to be ask to determine if the car is flooded, but it does not contain the knowledge of what a flooded car is and why waiting will help. Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 14I B.TECH SEM V___ARTIFIFICIAL INTEIGENCE R20 ‘Types of Explanation There are four types of explanations commonly used in expert systems. + Rule trace reports on the progress of a consultation; + Explanation of how the system reached to the given conclusion: «Explanation of why the system did not give any e + Explanation of why the system is asking a question; Knowledge Acqu The success of any expert system majorly depends on the quality, completeness and accuracy of the information stored in the knowledge base, ion The knowledge base is formed by readings from various experts, scholars, and the Knowledge Engineers. The knowledge engineer is a person with the qualities of empathy, quick learning, and case analyzing skills. User acquires information from subject expert by recording, interviewing, and observing him at work, ete. User then categorizes and organizes the information in a meaningful way, in the form of IF- THEN-ELSE rules, to be used by interference machine. The knowledge engineer also monitors the development of the ES. yn, collection, analysis, modeling and validation of knowledge. 1 Issues in Knowledge Acquisition The important issues in knowledge acquisition are: + Knowledge isin the head of experts + Experts have vast amounts of knowledge «Experts have a lot of tacit knowledge ‘They do not know all that they know and use Tacit knowledge is hard (impossible) to describe. Experts are very busy and valuable people One expert does not know everything Knowledge has a "shelf life 2. Techniques for Knowledge Acquisition The techniques for acquiring, analyzing and modeling knowledge are: Protocol-generation techniques, Protocol analysis techniques, Hierarchy-generation techniques, Matrix-based techniques, Sorting techniques, Limited-information and constrained-processing tasks, Diagram-based techniques. * Protocol-generation techniques © Include many types of interviews (unstructured, semi-structured and structured), reporting and observational techniques. Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 15Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 = Protocol analysis techniques Used with transcripts of interviews or text-based information to identify basic knowledge objects within a protocol, such as goals, decisions, relationships and attributes. These act as a bridge between the use of protocol-based techniques and knowledge modeling techniques. = Hierarchy-generation techniques Involve creation, reviewing and modification of hierarchical knowledge. Hierarchy-generation techniques, such as laddering, are used to build taxonomies or other hierarchical structures such as goal trees and decision networks. The Ladders are of various forms like concept ladder, attribute ladder, composition ladders. m= Matrix-based techniques Involve the construction and filling-in a 2-D matrix (grid, table), indicating such things, as may be, for example, between concepts and properties (attributes and values) or between problems and solutions or between tasks and resources, ete. The elements within the matrix can contain: symbols (ticks, crosses, question marks) , colors , numbers , text, = Sor techniques Used for capturing the way people compare and order concepts; it may reveal knowledge about classes, properties and priorities. = Limited-information and constrained-processing tasks Techniques that either limit the time and/or information available to the expert when performing tasks. For example, a twenty-questions technique provides an efficient way of accessing the key information in a domain in a prioritized order. = Diagram-based techniques Include generation and use of concept maps, state transition networks, event diagrams and process maps, These are particularly important in capturing the "what, how, when, who and why" of tasks and events. MYCIN EXPERT SYSTEM MYCIN was an early backward chaining expert system that used artificial intelligence to identify bacteria causing severe infections (treating blood infections) and to recommend antibioties, with the dosage adjusted for patient’s body weight. MYCIN was developed over five or six years in the early 1970s at Stanford University in California. It was written in Lisp. * MYCIN would attempt to diagnose patients based on reported symptoms and medical test results. The program could request further information concerning the patient, as well as suggest additional laboratory tests, to arrive at a probable diagnosis, after which it would recommend a course of treatment. If requested, MYCIN would explain the reasoning that led to its diagnosis and recommendation. * Using about 500-600 production rules, MYCIN operated at roughly the same level of competence as human specialists in blood infections rather better than general practitioners. Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 16Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 MYCIN is a computer program or name of a decision support system designed to provide attending physicians with advice comparable (o that which they would otherwise get from a consulting physician specializing in bacteremia and meningitis infections. To use MYCIN, the attending physician must sit in front of a computer terminal that is connected to a DEC-20 (one of Digital Equipment Corporation's mainfame computers) where the MYCIN program is stored. When the MYCIN program is evoked, it initiates a dialogue. The physician types answers in response to various questions. Eventually MYCIN provides a diagnosis and a detailed drug therapy recommendation. Structure of Mycin Program: Prysctan User ‘cone Program Stabe Factual Expiaaton i 2nd Jucgmental yam oy Knowledge Infections Disease Expert The MYCIN system comprises three major subprograms, as depicted in Figure above. © The Consultation Program © Explanation Program + Knowledge Acquisition Program The Consultation Program is the core of the system; it interacts with the physician to obtain information about the patient, generating diagnoses and therapy recommendations. The Explanation Program provides explanations and justifications for the program’s actions. The Knowledge-Acquisition Program is used by experts fo update the system's knowledge base Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 17Ill B.TECH - 1 SEM UNIT—V___ARTIFIFICIAL INTEIGENCE R20 4 MYCIN(cont..) = MYCIN operated using fairly simple inference engine, and a knowledge base rules. = Te would query the physician running the program via a long series of simple yes/no or textual questions. = At the end, it provided a list of possible culprit bacteria ranked from high to low based on the probability of each diagnosis, its confidence in each diagnosis’ probability, = The reasoning behind each diagnosis (that is, MYCIN would also list the questions and rules which led it to rank a diagnosis a particular way), and its recommended course of drug treatment. tic Database Consultation “Control Structure” = © Rules is + GoaldiretedBackward-chaining Deph- =, — "8800 1 Meta-Rules fist Tree Search I = Templates 1 High-level Algorithm: tan 1 Rule Properties ® det |) en . Determine if Patient has significant infection uae = Context Properties 1 Delemin ily identity of sigan + Fed fiom Knowledge “organisms | & Acquisition System 3 Decide which drugs are potentially useful 4 Select best drug or coverage of drugs Prepared by: D Kalpana, CSE-ET Department - MRCET Page No: 18