Expert Systems Directors Prof Zixing Cai Miss

Expert Systems Directors : Prof. Zixing Cai &Miss Wen. Sha Central South University College of Information Science and Engineering

What is an Expert System? Experts are people who are very familiar with solving specific types of problems. Expert System Until now, no unified definition has been given. Knowledge-based system The fundamental function of the expert system depends upon its knowledge, therefore, the expert system is sometimes IC CIS called knowledge-based system. Central South University Artificial Intelligence

What is an Expert System(ES)? Definition 1: ES can handle real-world complex problems which need an expert’s interpretation and. In short, an by using a computer model of solve problems ES is an intelligent human expert reasoning to reach the same computer program that can conclusions that the human expert would do if he or she faces with a comparable problem. perform special and difficult task(s) Definition 2: ESfield(s) at the level program in some is an intelligent computer of to that uses knowledge and inference procedures human experts. solve problems that are difficult enough to require significant human expertise for their solutions. ISIC C Central South University Artificial Intelligence

Architecture of ideal expert system User Communication Interface Knowledge Base Interpreter Planner Agenda Coordinator Solution Adjuster Blackboard Reasoning Machine Architecture of an ideal expert system Central South University Artificial Intelligence ISIC C

ES-Knowledge Base(1) Knowledge Base To store knowledge from the experts of special field(s). It contains facts and feasible operators or rules for heuristic planning and problem solving. The other data is stored in a separate database called global database, or database simply. ISIC C Central South University Artificial Intelligence

ES-Reasoning Machine(2) Reasoning Machine To memorize the reasoning rules and the control strategies applied. According to the information from the knowledge base, the reasoning machine can coordinate the whole system in a logical manner, draw inference and make a decision. ISIC C Central South University Artificial Intelligence

ES- User Interface (3) User Interface To communicate between the user and the expert system. The user interacts with the expert system in problem-oriented language such as in restricted English, graphics or a structure editor. The interface mediates information exchanges between the expert system and the human user. ISIC C Central South University Artificial Intelligence

ES- Interpreter(4) Interpreter Through the user interface, interpreter explains user questions, commands and other information generated by the expert system, including answers to questions, explanations and justifications for its behavior, and requests for data. ISIC C Central South University Artificial Intelligence

ES-Blackboard (5) Blackboard To record intermediate hypotheses and decisions that the expert system manipulates. ISIC C Central South University Artificial Intelligence

ES-Note Note: Almost no exiting expert system contains all the components shown above, but some components, especially the knowledge base and reasoning machine, occur in almost all expert systems. Many ESs use global database in place of the blackboard. The global database contains information related to specific tasks and the current state. ISIC C Central South University Artificial Intelligence

Building Expert System The key for successfully building an expert system is to begin it from a smaller one, and extend and test it step by step, make it into a larger-scale and more perfect system. The general procedure for building ESs : Ø Design of initial Knowledge Base Ø Development & test for prototype原型 system Ø Improvement & induction归纳 for the ISIC knowledge C Central South University Artificial Intelligence

Design of initial Knowledge Base Problem identification Knowledge conceptualization Concept formulization Rule formulation Rule validation ISIC C Central South University Artificial Intelligence

Stages for Designing KB Re-designment define key concept of the knowledge , for example : type identify what the problem use knowledge change the knowledge to check the of data structure , conditions is , how to define it , can representation method programming language correctness of that have known, the goal Refinements we divide it into some sub rules or Questions Knowledge Concepts to represent the that can be identified by state, assumption and control problems Structure knowledge. the computer. knowledge strategy. Rules Indentification Conceptualization Formalization Rule Formalization Concepts Stages for designing knowledge base Central South University Artificial Intelligence Validation Conclusion Representation ISIC C

Types of Expert System (ES) Category Interpretation Prediction Diagnosis Design Planning Monitoring Debugging Repair Instruction Problem Addressed Inferring situation descriptions from sensor data Inferring likely consequences of given situation Inferring system malfunction from observation Configuring objects under constrains Designing actions Comparing observation to plan vulnerabilities Prescribing remedies for malfunction Executing a plan to administer a prescribed remedy Diagnosing, debugging and repairing student behavior ISIC Interpreting, predicting, repairing and monitoring C system behavior Control Central South University Artificial Intelligence

Expert Control Systems Important differences between expert systems and expert control systems: Ø Expert systems simply complete consultative function for problems of special domains and aid users to work. Expert control systems need to make decisions to control action independently and automatically. Ø Expert systems usually work in off-line mode. Expert control systems need to acquire dynamic information in on-line mode and make real-time ISIC C control for the system. Central South University Artificial Intelligence

Two main types of expert control Two main types of expert control: Ø Expert control system With a more complex structure, higher cost, better performance, and used to plants or processes where higher technical requirements are needed. Ø Expert controller With a simpler structure, lower cost and has a performance that can meet the general requirements for the industrial process control. ISIC C Central South University Artificial Intelligence

Structures of Expert Control System ISIC C A typical structure of expert control system Central South University Artificial Intelligence

Tasks of Expert Control System The expert control system should execute following tasks: Ø Supervise the operation of the plant (process) and controller. Ø Examine possible failure or fault of the system components, replace these faulty components or revise control algorithms to keep the necessary performance of the system. Ø In special cases, select suitable control algorithm to adapt the variation of the system parameters and environment. SIC CI Central South University Artificial Intelligence

Store the domain knowledge of industrial process control, experience of experts(expertise) and Extract and process Use the forward chaining Sum up every control facts Expert Controller information, provide reasoning to judge the pattern and control strategy and learn Knowledge Base (KB) conditions of every rule in experience of the controlled adaptation with foundation the sequence process K G Feature I U Y Inference Set of S Recognition e Plant Engine (IE) Control Rules Information R Processing u Sensor(s) Industrial expert controller Central South University Artificial Intelligence ISIC C

Expert system-MYCIN An early expert system developed in early 1970 s at Stanford University Wrote by Lisp Language Author: Bruce G. Buchanan & Edward H. Shortliffe <> This expert system was designed to identify bacteria causing severe infections ISIC C Central South University Artificial Intelligence

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Reasoning & Problem solving strategy MYCIN could use backward chaining to find out whether a possible bacteria was to blame. “Certainty factor” is used for an assessment of the likelihood可能性评估 of one bacteria. MYCIN’s problem solving strategy was simple: For each possible bacteria: Using backward chaining, try to prove that it is the case, finding the certainty. Find a treatment which ” covers” all the bacteria above some level of certainty. IC CIS Central South University Artificial Intelligence

MYCIN: Problem Solving When trying to prove a goal through backward chaining, system could ask user certain questions. Certain facts are marked as “askable”, so if they couldn’t be proved, ask the user. The ask procedure is carried out in following style of dialogue: MYCIN: Has the patient had neurosurgery? USER: No. MYCIN: IS the patient a burn patient? USER: No. … MYCIN: It could be Diplococcus. . Central South University Artificial Intelligence ISIC C

Modeling Human Diagnostic Strategies Problem Solving Strategy used in MYCIN only works when small number of hypotheses (e. g. , bacteria). For hundreds of possible diseases, need a better strategy. Later medical diagnostic systems used an approach based on human expert reasoning. ISIC C Central South University Artificial Intelligence

Diagnostic Reasoning: Internist is a medical expert system for general disease diagnosis. Knowledge in system consists of disease profiles概况, giving symptoms症状 associated with disease and strength of association. ISIC C Central South University Artificial Intelligence

Problem Solving in Internist Use initial data (symptoms) to suggest, or trigger引发 possible diseases. Determine what other symptoms would be expected to confirm these diseases. Gather more data to differentiate区分 between these hypotheses. Either: If one hypothesis most likely, try to confirm it. If many possible hypotheses, try to throw some out. If a few hypotheses, try to discriminate区别 between them. ISIC C Central South University Artificial Intelligence

Medical Expert Systems Today Medical expert systems were quite effective in evaluations comparing their performance with human experts. Support the physicians医生 decisions, rather than doing the whole diagnosis. Include many useful support materials辅助材料, such as report generating tools, reference material etc. ISIC C Central South University Artificial Intelligence

Summary: Expert Systems Effective systems have been developed that capture expert knowledge in areas like medicine. Typically combine rule-based approaches, with additional certainty/probabalistic reasoning, and some top level control of the problem solving process. Not a huge take-up of systems, perhaps due to failure to adequately consider how they would be integrated into current practice. ISIC C Central South University Artificial Intelligence