Rule-Based/Expert Systems Catie Welsh CSE 435 – IDSS October 13, 2006

Outline Defining Expert Systems Describing uses and components of Expert Systems Showing an example of an Expert System Describing the underlying programming used to build an expert system.

Definition Expert systems are: – Knowledge based systems – Part of the Artificial Intelligence field – Computer programs that contain some subject-specific knowledge of one or more human experts – Made up of a set of rules that analyze user supplied information about a specific class of problems. – Systems that utilize reasoning capabilities and draw conclusions.

Terms Associated with Expert Systems Knowledge Engineering – building an expert system Knowledge Engineers – the people who build the system Knowledge Representation – the symbols used to represent the knowledge Factual Knowledge – knowledge of a particular task domain that is widely shared Heuristic Knowledge – more judgmental knowledge of performance in a task domain.

Uses of Expert Systems Very useful to companies with a high-level of experience and expertise that cannot easily be transferred to other members. Solves problems that would normally be tackled by a medical or other professional. Currently used in fields such as accounting, medicine, process control, financial service, production, and human resources

Prominent Expert Systems MYCIN – used to diagnose infectious blood diseases and recommend antibiotics. DENDRAL – embedded a chemist’s knowledge of mass spectrometry rules to use in analysis. CADUCEUS – used to analyze blood-borne infectious bacteria CLIPS and Prolog programming languages are both used in expert systems – The Age of Empire game uses CLIPS to control its AI

Building an Expert System Can be built from scratch – using lots of if-then-else statements. There are many products being sold to make programming these systems easier. – A few that I’m aware of: Exsys – – provides an easy to use user interface to develop traditional applications or web-based solutions. Barisoft – – developed by a PSU professor and finely tuned by his studentswww.barisoft.com CLIPS - provides a complete environment for the construction of rules and/or object based expert systems Jess – the Rule Engine, built on top of CLIPS, for the Java Platform

Components of an Expert System Set of Rules – derived from the knowledge base and used by the interpreter to evaluate the inputted data Knowledge Engineer – decides how to represent the experts knowledge and how to build the inference engine appropriately for the domain Interpreter – interprets the inputted data and draws a conclusion based on the users responses.

Problem-solving Models Forward-chaining – starts from a set of conditions and moves towards some conclusion Backward-chaining – starts with a list of goals and the works backwards to see if there is any data that will allow it to conclude any of these goals. Both problem-solving methods are built into inference engines or inference procedures

Knowledge Objects Classes – questions the user is asked Parameters – place holder for a character string which can be variable and used to decide if/when a question is asked Procedures – definitions of calls to external procedures Rule Nodes – inferencing is done by a tree structure (decision trees!) and the nodes are called rule nodes.

Rule Base as a forest of trees Top node is called the goal node. Leaves are rule nodes – several types of rule nodes – Evidence node – functions to obtain information from the operator by asking specific questions – External node – indicates that data will be used which was obtained from a procedure call – Reference node – refers to another tree or subtree

Advantages of Expert Systems Provide consistent answers for repetitive decisions, processes and tasks. Hold and maintain significant levels of information. Reduce employee training costs Centralize the decision making process. Create efficiencies and reduce the time needed to solve problems.

Advantages (cont’d) Combine multiple human expert intelligences Reduce the amount of human errors. Give strategic and comparative advantages creating entry barriers to competitors Review transactions that human experts may overlook.

Disadvantages of Expert Systems Lack human common sense needed in some decision making. Will not be able to give the creative responses that human experts can give in unusual circumstances. Domain experts cannot always clearly explain their logic and reasoning. Challenges of automating complex processes. Lack of flexibility and ability to adapt to changing environments. Not being able to recognize when no answer is available.

Example Expert System This is an example expert system built to help show a user their potential risk of contracting colon cancer based on their lifestyle and family history.

Example System OBJECT NUMBER 21 Name: [Age_Related] Prompt: How OLD were you when you were FIRST diagnosed with any of the other related cancer? Max. selections: 100 Legal Values: OBJECT NUMBER 22 Name: {orc_age_rpt} Prompt: Please enter a value for the ORC_AGE_RPT. Max. selections: open Legal Values: -OPEN STRING OBJECT NUMBER 23 Name: Two_Related_Cancers Prompt: Have you ever been diagnosed with any TWO or more of the following cancers? Colon, Gastric, Small Bowel, Hepatobiliary, Pancreatic, Endometrial, Ovarian, Renal pelvis, or Ureter. Max. selections: 1 Legal Values: -Yes -No OBJECT NUMBER 24 Name: {Non_Related_Cancers} Prompt: Please enter any additional CANCERS for which YOU have been diagnosed. Example: Breast, Lymphoma. If no other cancers, then type None. Max. selections: open Legal Values: -OPEN STRING

Example System (cont’d) * * * * RULES SUMMARY * * * * No. of rules defined: 129 RULE NUMBER 1:disclaimer-no IF Disclaimer=No - I do not agree to the terms and conditions in the disclaimer THEN {status}=stop program !Action=!LOOP RULE NUMBER 2:disclaimer-yes IF Disclaimer=Yes - I have read the disclaimer and agree to all of its terms and conditions THEN {status}=continue with program RULE NUMBER 3:Object Initialization IF THEN [LS_Score]=0 RULE NUMBER 4:BMI Calculation IF [height]==!NOTBLANK [weight]==!NOTBLANK THEN [bmi]=([weight]*703)/([height]*[height])

More Rules for the Example System RULE NUMBER 123:Calc Greater Next or Suggested IF [age]==!NOTBLANK [Suggested_Screen_Age]==!NOTBLANK [Next_Scrn_Age]==!NOTBLANK THEN [Dif_Next]=[Next_Scrn_Age]-[Suggested_Screen_Age] RULE NUMBER 124:If Next later than Suggested IF [Dif_Next]=>0 THEN [Age_Difference]=[age]-[Next_Scrn_Age] RULE NUMBER 125:If Next less or equal Suggested IF [Dif_Next]=<1 THEN [Age_Difference]=[age]-[Suggested_Screen_Age] RULE NUMBER 126:Get1stScrn Now IF [Age_Difference]==0 THEN {screening_rec_age_rpt}=Schedule Screening Now at Age ([age]). [Get_Screened_Age]=[age]

Conclusions Very interesting field of AI. Expert Systems are extremely useful in the right domain. They are inflexible and require a lot of collaboration between a knowledge engineer and a domain expert. When implemented correctly, expert systems remove human error from the equation.

References “Expert Systems and Artificial Intelligence”. Engelmore, R., Feigenbaum, E., Chapter “The Origin of Rule-Based Systems in AI”, Davis, R., King, J. “Expert System – Wikipedia”,