1. March 1999Dip HI KBS1 Knowledge-based Systems Alternatives to Rules

2. March 1999Dip HI KBS2 Knowledge-based Systems Rule-based –heuristic (expert) knoweldge encoded in rules. Model-based –reasoning is based on a model of a device/system. Case-based –knowledge is provided by many examples of solutions to previous cases.

3. March 1999Dip HI KBS3 Problems with Rules Fail to work if problem is not anticipated by rules. Heuristic rules can be applied inappropriately if some condition is omitted. With some understanding of the problematic system these inadequacies could be overcome.

4. March 1999Dip HI KBS4 Model-based Reasoning Just as experts revert to first principles when confronted with new or difficult problems… Model-based reasoners are based on a representation of the structure and behaviour of the system under analysis. Used especially in diagnosis of equipment malfunctions.

5. March 1999Dip HI KBS5 MBR : Diagnosis Simulate behaviour of components of device/system. Represent component interactions. Represent known failure modes of components and interconnections. Compare actual device performance with that predicted by the model. If there is a discrepancy, reason about what failures could account for observed bahaviour.

6. March 1999Dip HI KBS6 MBR Example MULT-1 MULT-2 MULT-3 ADD-1 ADD-2 A=3 B=3 E=3 C=2 D=2 (F=12) (G=10) Actual F is 10 Predicted outputs Fig 6.14 of Luger and Stubblefield, Third Edition.

7. March 1999Dip HI KBS7 Reasoning phase Generate hypotheses –either ADD-1, MULT-1 or MULT-2 is faulty Test each hypothesis –find MULT-2 appears to be OK (since ADD- 2’s output is good). Discriminate between surviving hypotheses with further observations. –E.g. check the actual output of MULT-1.

8. March 1999Dip HI KBS8 Problems with MBR Intensive knowledge acquisition. Requires an explicit domain model, a well- defined theory. –Excludes some medical specialties, financial applications,... Complex and detailed reasoning, slow?. Ignores (possibly valuable) experiential knowledge.

9. March 1999Dip HI KBS9 Problems cont/ Can only handle problems explained by the model. –A model is a representation of some reality. It leaves out many aspects. If the things that left out are the cause of the problem, the MBR won’t work.

10. March 1999Dip HI KBS10 Advantages of MBR More robust and flexible reasoning Can provide causal explanations. May serve a tutorial role. Knowledge may be transferable to related tasks.

11. March 1999Dip HI KBS11 Case-based Reasoning Rules and models may be difficult to devise for natural domains (e.g. medicine). In CBR “knowledge” is held in a case base of real prior problems and their solutions. Case-based diagnosis is common –physician matches new case with one seen previously and uses the diagnosis of the old case as a starting point.

12. March 1999Dip HI KBS12 Application domains Technical support help desks Classification type problems –see Machine Learning lecture Case-based design Fraud detection Legal planning –much law is precedent (case) based

13. March 1999Dip HI KBS13 Components Representation Retrieval –Matching engine retrieves cases similar to target case. Adaptation Remembering

14. March 1999Dip HI KBS14 Breathalyser Example cases Duration is duration of drinking session. Perhaps elapsed time should be added as a case feature?

15. March 1999Dip HI KBS15 Case Representation The knowledge engineering task is focused on deciding how to represent cases –what features best characterise cases i.e. predictive features –may require expert analysis e.g. for image classification the bitmap may need to be converted to an edge map. e.g. height and weight may not be useful in themselves for classifying apples and pears,but height/weight ratio is.

16. March 1999Dip HI KBS16 Case retrieval Based on some similarity measure. –e.g number of matching features –e.g. distance measure based on difference between numeric features Indexes may be used to speed the retrieval

17. March 1999Dip HI KBS17 Case indexing - Example

18. March 1999Dip HI KBS18 k-Decision Tree Tree can be built automatically (see later). What if no. of bedrooms is less important (predictive) than age of house?

19. March 1999Dip HI KBS19 Case Adaptation Breathalyser –if actual consumption is 2 more than in retrieved case add 0.5 to blood alcohol count. Property Valuation –for extra bedroom add x% to price More complex adaptation may be needed where solutions are plans or designs, rather than single values.

20. March 1999Dip HI KBS20 Retrieval revisited Objective: to find the case most applicable to the current one. Applicable ? –If there is no adaptation, find case whose solution we are most confident of reusing i.e. whose differences don’t invalidate the solution –With adaptation, find case whose solution is easiest to adapt to current problem use an adaptation cost measure instead of similarity measure.

21. March 1999Dip HI KBS21 Advantages of CBR May work better than inductive and deductive methods for natural domains. Does not require extensive analysis of domain knowledge. Existing data and knowledge - case histories, repair logs - are leveraged. Shortcuts complex reasoning - may be quicker than rule-based or model-based.

22. March 1999Dip HI KBS22 Problems with CBR Lack of deep knowledge - –poor explanation –danger of misapplication of cases. Large case base can slow things down –(compute-store tradeoff) Knowledge engineering can still be arduous –designing and selecting features –similarity matching algorithms

23. March 1999Dip HI KBS23 Hybrid Systems Integrate two or more reasoning methods to get a cooperative effect. See Protos system –builds a model from cases with “teacher” help –better explanation and more convincing

24. March 1999Dip HI KBS24 References and Acknowledgements Padraig Cunningham provided much of the material on CBR. Luger and Stubblefield: Third Edition of “Artificial Intelligence” has a lot more than the previous edition.