Expert Systems: Simple Examples and case study

Transcription

1 Expert Systems: Simple Examples and case study KR Chowdhary, Professor, Department of Computer Science & Engineering, MBM Engineering College, JNV University, Jodhpur

2 A Simple Example 1.IF engine_getting_petrol AND engine_turns_over THEN problem_with_spark_plugs 2.IF NOT engine_turns_over AND NOT lights_come_on THEN problem_with_battery 3.IF NOT engine_turns_over AND lights_come_on THEN problem_with_starter 4.IF petrol_in_fuel_tank THEN engine_getting_petrol (i.e.petrol reaches to engine)

3 A Simple Example coded in prolog problem_with_spark_plugs :- engine_getting_petrol, engine_turns_over. problem_with_battery :- NOT engine_turns_over, NOT lights_come_on. problem_with_starter :- NOT engine_turns_over, lights_come_on. engine_getting_petrol :- petrol_in_fuel_tank.

4 Explanation facilities Most expert systems have explanation facilities that allow the user to ask why it asked some question, and how it reached some conclusion. These questions are answered by referring to the system goals, the rules being used, and any existing problem data.

5 Explanation facilities System: Is there gas in the fuel tank? User: Yes. System: Is it true that the engine turns over? User: No. System: Is it true that the lights come on? User: Why? System: I am trying to prove: problem with battery. It has been established that it is not the case that the engine turns over. Therefore, if it is not the case that the lights come on then there is a problem with battery. Is it true that the lights come on? User: No. System: I conclude that there is a problem with the battery. User: How?

6 Explanation facilities.. System: This follows from Rule 2: IF NOT engine_turns_over AND... NOT engine_turns_over was given by the user. NOT lights_come_on was given by the user.

7 Probabilistic expert systems Expertise is often intuitive, and facts can have degrees of truth much research has been devoted to inventing new models of probabilistic deduction: e.g., Certainty factors fuzzy logic statistical / Bayesian deduction multi-valued logics: true, false, unknown typical rule: (e.g.). IF patient has a fever CF 0.90 patient s forehead is blue CF.75 THEN illness is Cat Scratch Fever CF.5 weakness: these (CF) certainty factors are not statistically sound values; rather pulled out of the air by expert or KE CF s are combined during deduction; multiple results can arise how legitimate are they?

8 MYCIN: A Quick Case Study One of the earliest expert systems. Design has strongly influenced the design of commercial expert systems and expert system shells Developed at Stanford in the 1970s Its job was to diagnose and recommend treatment for certain blood infections To do the diagnosis properly'' involves growing cultures of the infecting organism Mycin was developed partly in order to explore how human experts make these rough (but important) guesses based on partial information.

9 Mycin was never actually used in practice Mycin represented its knowledge as a set of IF-THEN rules with certainty factors IF the infection is primary-bacteremia AND the site of the culture is one of the sterile sites AND the suspected portal of entry is the gastrointestinal tract THEN there is suggestive evidence (0.7) that infection is bacteroid. The 0.7 is roughly the certainty that the conclusion will be true given the evidence. If the evidence is uncertain the certainties of the bits of evidence will be combined with the certainty of the rule to give the certainty of the conclusion.

10 Mycin was written in Lisp, and its rules are formally represented as Lisp expressions. Mycin is a (primarily) goal-directed system, using the basic backward chaining reasoning strategy Mycin used various heuristics to control the search for a solution (or proof of some hypothesis)

11 These were needed both to make the reasoning efficient and to prevent the user being asked too many unnecessary questions One strategy is to first ask the user a number of more or less preset questions that are always required and which allow the system to rule out very unlikely diagnoses. The other strategies relate to the way in which rules are invoked. Given a possible rule to use, Mycin first checks all the premises of the rule to see if any are known to be false

12 The other strategies relate more to the certainty factors. Mycin will first look at rules that have more certain conclusions, and will abandon a search once the certainties involved get below 0.2 There are three main stages to the dialogue. 1. Initial data about the case is gathered so the system can come up with a very broad diagnosis. 2. More directed questions are asked to test specific hypotheses. At the end of this section a diagnosis is proposed.

13 3. Questions are asked to determine an appropriate treatment, given the diagnosis and facts about the patient. This concludes with a treatment recommendation. At any stage user can ask why a question was asked or how a conclusion was reached, and when treatment is recommended the user can ask for alternative treatments if the first is not viewed as satisfactory. Other developments from the MYCIN 1. EMYCIN was really the first expert shell developed from Mycin 2. system called NEOMYCIN was developed for training doctor