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2 Decision Support and Expert Systems سيستم ‌ هاي خبره و تصميم ‌ يار Lecturer: A. Rabiee Rabiee.iauda.ac.ir

Decision A decision is a choice between alternatives based on estimates of the values of those alternatives. Supporting a decision means helping people working alone or in a group gather intelligence, generate alternatives and make choices 3

Decision Support System A Decision Support System (DSS) is an interactive computer-based system intended to help decision makers use: – communications technologies, – data, – documents, – knowledge and/or models to identify and solve problems, complete decision process tasks, and make decisions. 4

Decision Support System Decision Support System is a general term for any computer application that enhances a person or group’s ability to make decisions. Also, Decision Support Systems refers to an academic field of research that involves designing and studying Decision Support Systems in their context of use. 5

History of DSS 6

Taxonomy Using the mode of assistance as the criterion, Power (2002) differentiates five types for DSS: – communication-driven DSS, – data-driven DSS, – document-driven DSS, – model-driven DSS, and – knowledge-driven DSS. 7

Communication-driven DSS A communication-driven DSS use network and comminication technologies to faciliate collaboartion on decision making. It supports more than one person working on a shared task. examples include integrated tools like Microsoft's NetMeeting, google doc, or Vide conferencing. It is related to group decision support systems (GDSS). 8

Data-driven (retrieving) DSS A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data. 9

Document-driven DSS A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats. A search engine is a primary tool associated with document-driven DSS. 10

Model-driven DSS A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data intensive. Examples: – A spread-sheet with formulas in – A statistical forecasting model – An optimum routing model 11

Knowledge-driven DSS A knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures. It suggest or recommend actions to managers. Expert Systems like MYCIN 12

Course Outline Chapter1: Introduction Chapter2: Knowledge Engineering Chapter3: Knowledge Representation Chapter4: Inference Techniques Chapter11: Bayesian approach Chapter12: Certainty Theory Chapter13: Fuzzy Logic 13

منابع و مراجع Durkin, J., (1998). Expert systems: design and development. Prentice Hall PTR. Negnevitsky, M. (2005). Artificial intelligence: a guide to intelligent systems. Pearson Education. Russell, S., & Norvig, P. (1995). Artificial intelligence: a modern approach. Whinston, A. B., & Holsapple, C. W. (1996). Decision Support Systems: A Knowledge-Based approach. Durkin, J. (1993). Expert systems: catalog of applications. Intelligent Computer Systems. 3

ارزشيابي درس Final Exam:50 Tasks + mini projects:20 Project + presentation:30 Paper (optional):+15 4

Chapter 1: Introduction to Decision Support and Expert System 16

Table of Contents Definition Advantages and Limitations Basic Structure of an Expert System Developing an Expert System Basic Rule-based Expert System 17

18 Areas of Artificial Intelligence

19 What is an expert system? “An expert system is a computer system that emulates, or acts in all respects, with the decision-making capabilities of a human expert.” Professor Edward Feigenbaum Stanford University => Expert knowledge is required

20 What is the Expert Knowledge? Base Knowledge / Expert Knowledge – Law Book Rules / lawyer Heuristics and Experiences (secrets!) – Chess Rules / Chess Master Patterns

21 Basic Functions of Expert Systems

22 Expert System Main Components Knowledge base – obtainable from books, magazines, knowledgeable persons, etc. Inference engine – draws conclusions from the knowledge base

23 Expert Systems Applications and Domains Applications: – A replacement for an expert person – Assistant Domains: – Control – Design (Specially with constrains) – Diagnosis (Car fault detection, disease) – Prescription – Learning – Monitoring (Fire in the Jungle) – Planning – Simulation – Prediction

24 Advantages of Expert Systems Increased availability Reduced cost Reduced danger Increase Performance Multiple expertise Increased reliability Explanation (Why? and How?) Fast response Steady, unemotional, and complete responses at all times

25 Limitations of Expert Systems Specific to a special domain; An expert system can solve the problems that an expert person can solve Typical expert systems cannot generalize through analogy to reason about new situations in the way people can. The probability of mistakes

26 Early Expert Systems DENDRAL – used in chemical mass spectroscopy to identify chemical constituents MYCIN – medical diagnosis of illness DIPMETER – geological data analysis for oil PROSPECTOR – geological data analysis for minerals XCON/R1 – configuring computer systems

Expert Systems vs. Conventional Programs Conventional ProgramExpert System NumericSymbolic AlgorithmicHeuristic Information and control integratedKnowledge separate from control Difficult to modifyEasy to modify Precise InformationUncertain Information Command InformationNatural dialogue with explanation Final result givenRecommendation with explanation Optimal solutionAcceptable solution 27

28 Representing the Knowledge The knowledge of an expert system can be represented in a number of ways, including IF- THEN rules: IF you are hungry THEN eat

29 Rules and Facts Rules: –IF the car doesn’t run and the fuel gauge reads empty THEN fill the gas tank. –IF there is flame, THEN there is a fire. –IF there is smoke, THEN there may be a fire. –IF there is a siren, THEN there may be a fire. Facts: –The car doesn’t run –There is a flame –There is smoke –There is a siren The meaning of firing a rule: –Condition is true => Generating the conclusion

30 Development of a Rule-based Expert Systems Rooted from Cognitive Studies: – How does human process information An example: – Newell/Simon Model (General Problem Solver) (Ernst & Newell, 1969; Newell & Simon, 1972) Long Term Memory: IF-Then Rules Short Term Memory: Current Facts Inference Engine/Conflict Resolution

Production System Model 31

Basic Rule-Based Expert System 32

33 Structure of a Rule-Based Expert System

34 Elements of an Expert System User interface – mechanism by which user and system communicate. Exploration facility – explains reasoning of expert system to user. Working memory – global database of facts used by rules. Inference engine – makes inferences deciding which rules are satisfied and prioritizing.

35 Elements Continued Agenda – a prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in working memory. Knowledge acquisition facility – automatic way for the user to enter knowledge in the system bypassing the explicit coding by knowledge engineer. Knowledge Base!

Common Rule-Based Expert System 36

Homework 1 موضوع پروژه ‌ ها به همراه يك صفحه پروپوزال تا هفته اول آبان ماه به اينجانب ايميل شود.