1. Explanation Facility دكترمحسن كاهاني http://www.um.ac.ir/~kahani/

2. Explanation Facility  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.

3. Explanation Facility:Example 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? System: This follows from Rule 2: IF NOT engine_turns_over AND... NOT engine_turns_over was given by the user. NOT lights_come_onwas given by the user.

4. Explanation Facility  Providing such an explanation facility involves, at least,  recording what rules are used in drawing conclusions,  using these records to compose explanations like the ones before.  Giving simple explanations is not very difficult (the answers always have a simple set form), and is sometimes quite useful.  However, explanation facilities in expert systems are often not widely used, and where used not viewed as acceptable by their users.  Reasons:  the explanations just reference the ``surface'' knowledge encoded in the rules, rather than the ``deep'' knowledge about the domain  if the user fails to understand or accept the explanation, the system can't re-explain in another way.

5. Explanation Facility  Justifier  Makes system more understandable  Exposes shortcomings  Explains situations that the user did not anticipate  Satisfies user’s psychological and social needs  Clarifies underlying assumptions  Conducts sensitivity analysis  Types  Why  How  Journalism based  Who, what, where, when, why, how  Why not

6. Generating Explanations  Static explanation  Preinsertion of text  Dynamic explanation  Reconstruction by rule evaluation  Tracing records or line of reasoning  Justification based on empirical associations  Strategic use of metaknowledge

7. Causes and Explanations  We have already seen that causes depend on explanations  What we consider to be a cause depends on what we are trying to explain  So, how good are people at giving explanations?

8. Shallow explanation  Do you know how  A flush toilet works?  A derailleur system works?  A car engine works?  A computer works?

9. Shallow explanation  Rosenblit & Keil  Asked people whether they could generate a good explanation  Had them generate an explanation  Showed them a good explanation  Had them evaluate their own explanation against the good one.  People were not good at knowing when they could provide a good explanation?

10. Why?  People are usually good at predicting what they know.  Metacognition research in memory  Good at predicting whether they know the plots of movies  Factors that affect explanations  People have mental simulations of complex objects working  Causal explanations are recursive

11. Recursion in explanation  At the top level, we know the parts of an object and the spatial relations between them.  Then, we need to know the functional components, and how they are connected.  Then, we need to know how those functional components operate

12. Becoming better at explaining  Practice really does help  Teaching helps learning  Why? Teachers have to explain things.  Teaching lets people practice giving explanations  Study hint  Many students study for exams by doing recognition.  Study by producing explanations.

13. Distribution of expertise  Causal knowledge is distributed across people  We are very good at knowing who to go to for particular kinds of explanations  Children are also sensitive to domains of expertise  Studies by Keil with kids  Someone who know how refrigerators work is more likely to know how stereos work than about what makes people smart.

14. Explanation System  Can display rule being invoked at any point in consultation  Record rule invocation and associates them with questions asked and rules invoked  Use rule index to retrieve particular rules in answer to questions  Why and how questions answered using goal tree

15. Designing for Explanation An expert system should explain the decisions it makes: background information, causes, associations, WHY and HOW? This ability is important to enhance understanding, evaluation and system’s acceptance. An explanation should: Make sense Use terminology and structures that the user is familiar with. Sometimes include graphical information Be accurate and efficient

16. Designing for Explanation The ability to include explanations in a rule-based system is fairly simple, even when most systems simply state the rules that have been processed. User has to develop the true explanation. It is much more difficult to develop explanations in non-rule based systems: Complex models, “black box” approaches.

17. purposes of an explanation facility  To make the system more intelligible/ understandable to the user.  To uncover the shortcomings of the rules and the KB.  To describe the situations that were unanticipated by the user.  To satisfy psychological, social and safety requirements. A user should feel assured about the ES’ actions.

18. Designing for Explanation An explanation should address at least of the following questions: Why a decision was made? How? Why explanations It typically requires the ES to look through the rules to determine what higher goals the system is attempting to achieve. Example: ES: What is your annual income? User: Why? (Why do you need to know?) ES: In checking R2, I need to know whether your income is above 20,000. If this is true, I will conclude that because you have a college degree, you should invest in…

19. Designing for Explanation How explanations It requires the system to look down through the rules to determine what sub-goals were satisfied to achieve the goal. Example: ES: Invest in IBM stocks. User: How? (How was the conclusion reached?) ES: (Ideal solution) Given that you have $10,000 to invest and you are younger than 30, then according to R4 you have a college degree. If this is the case, then according to R1 you should invest in securities. For a young investor such as you, according to R3, you should.... Finally, according to R5, if you need to invest in growth stocks, then IBM is your best bet….

20. Why and How Explanations Limitations They typically show the rules as they were programmed and not in a natural language. This is usually not easy to understand and follow, especially if the chain of reasoning is long. Why & how do not cover all possible questions.

21. Explanation Generation Static explanation: The ES contains answer text for every question the user may ask. The text is provided by the expert. All questions and answers must be anticipated. The system really does not know what it is saying If the program is changed and the text is not changed, then the explanation may be incorrect.

22. Explanation Generation Dynamic explanation: An ES creates an explanation based upon the execution pattern of the rules uses. This is better than static explanation in that any changes to the system will be reflected in the explanation. Limitation: It may not contain enough understandable information for the user.

23. Multimedia Explanation Explanation is driven by human factors and user interface design. Text only explanations are not always useful. Not all users can read text and understand its meaning, they need diagrams, pictures, or audio to assist them. An ES may provide the user with a diagram showing the rule implementation: Relationship diagram between rules, graphs of data over time It may provide image, video or animations that support the explanation and the task to be completed. (Simulations for How Explanations)