Chapter 13 Inference Techniques 917807 Allen

1. Chapter 13Inference Techniques917807 Allen

2. Konica Automates A Help Desk with Case-based Reasoning • The situation to be diagnosed is entered into the system . • The text is analyzed by a natural language processor to interpret the situation and compare it to existing cases stored in a case base. • The user can either go with the recommended course of action or refine the wording to tighten up the description of the problem. • The system can apply AI technology in solving real problems.

3. Reasoning in Artificial Intelligence • A computer program is needed to access the knowledge for making inferences. • This program is an algorithm that controls a reasoning process and usually called the inference engine. • The inference engine directs the search through the knowledge base, a process that may involve the application of inference rules in what is called pattern matching. • The most popular inference engines: forward and backward chaining.

4. The ways of people reasoning • Formal reasoning methods • Heuristic reasoning • Common sense applied to specific goals • Dividing complex problems into subproblems • Parallelism-neural processors operating in parallel • Analogy, or the ability to associate and relate concepts • Synergy, in which the whole is greater than the sum of its parts • Serendipity, or fortuitous accidents

5. Inference Methods • Deductive reasoningMove from a general principle to a specific inference. • Inductive reasoningMove from some established facts to draw general conclusions. • Analogical reasoningDerive an answer to a question by known analogy. It is a verbalization of internalized learning process. Use of similar past experiences.

6. Inference Methods (Conti.) • Formal reasoningSyntactic manipulation of a data structure to deduce new facts following prescribed rules of inferences. • Procedural reasoningUse of mathematical models or simulation. • Metalevel reasoningKnowledge about what is known.

7. Reasoning with Logic The most important method is called modus ponens. If A, then B In the terminology of logic, we express this as [A AND (A B ) ] B

8. Forward and Backward Chaining: An Overview • There are two methods for controlling inference in rule-based ES: forward chaining and backward chaining. • Example 1, pp 513 • Example 2, pp513

9. Backward Chaining • If the current goal is to determine the correct conclusion, then the process attempts to determine whether the premise clauses (facts) match the situation. • Backward chaining is a goal-driven approach in which you start from an expectation of what is going to happen and then seek evidence that supports your expectation.

10. The Step of Backward Chaining • The program starts with a goal to be verified. • Look for a rule that has this goal in its conclusion. • Check the premise of the rule in an attempt to satisfy the rule. • It examines the assertion base first. • If the search fails, the program looks for another rule. • An attempt is then made to satisfy the second rule. • The process continues until all the possibilities that apply are checked or until the rule initially checked is satisfied. Example 3, pp514.

11. Forward Chaining • If the premise clauses match the situation, then the process attempts to assert the conclusion. • Forward chaining is a data-driven approach. • Start from available information, and then try to draw conclusions. • The ES analyzes the problem by looking for the facts that match the IF part of its IT-THEN rules. Example 4, pp516.

12. The Inference Tree • An inference tree provides a schematic view of the inference process. • In building an inference tree, the premises and conclusions are shown as nodes. • The branches connect the premises and the conclusions. • The operators AND and OR are used to reflect the structures of the rules. • Provide better insight into the structure of the rules. • Visualize the process of inference and movement along its branches. • It provides a guide for answering the why and how questions in the explanation process.

13. Inferencing with Frames • Reasoning with frames is much more complicated than reasoning with rules. • The slot provides a mechanism for a kind of reasoning called expectation-driven processing. • Empty slots can be filled with data that confirm expectations. • It is easy to make inferences about new objects, events, or situations because the frames provide a knowledge base drawn from previous experience.

14. Model Reasoning • It is based on knowledge of the structure and behavior of the devices a system is designed to understand. • Model-based systems are useful in diagnosing difficult equipment problems. • It includes a model of the device to be diagnosed which is then used to identify the causes of the equipment’s failure.

15. Case-based reasoning • Adapt solutions used to solve old problems for new problems • Variation - Rule-induction method • A different process of case-based reasoning- Finds cases that contain solved problems similar to the current problem- Adapts the previous solution or solutions to fit the current problem, while considering any difference between the two situations

16. Finding Relevant Cases Involves: • Characterizing the input problem, by assigning appropriate features to it • Retrieving the cases with those features • Picking the case(s) that best match the input best

17. What is a Case? • Case - Defines a problem in natural language descriptions and answers to questions, and associates with each situation a proper business action • Scripts - Describe a well-known sequence of eventsOften “reasoning is applying scripts” • Often “reasoning is applying scripts” • More Scripts, Less (Real) Thinking • Can be constructed from historical cases • Case-based reasoning is the essence of how people reason from experience • CBR - a more psychologically plausible expert reasoning model than a rule-based model

18. Advantages of Case-based Reasoning • Knowledge acquisition is improved: easier to build, simpler to maintain, less expensive to develop and support. • System development time is faster. • Existing data and knowledge are leveraged . • Complete formalized domain knowledge is not required. • Experts feel better discussing concrete cases. • Explanation becomes easier. Rather than showing many rules. • Acquisition of new cases is easy.

19. Process of Case-based Reasoning • Assign Indexes. Features of the new event are assigned as indexes characterizing the event. • Retrieve. The indexes are used to retrieve a similar past case from memory. The past case contains the prior solution. • Modify. The old solution is modified to conform to the new situation. • Test. The proposed solution is tried out. • Assign and Store. If the solution succeeds, then assign indexes and store a working solution. • Explain, Repair and Test. If the solution fails, then explain the failure, repair the working solution, and test again.

20. Types of Knowledge structures • Indexing rules • Case memory • Similarity metrics • Modification rules • Repair rules

21. Success Factors for a Case-base Reasoning System • Determine specific business objectives. • Understand your end users and customers • Design the system appropriately • Plan an ongoing knowledge management process • Establish achievable returns on investment • Plan and execute customer access strategy • Expand knowledge generation and access across the enterprise

22. Explanation and Metaknowledge • Explanation • Human experts justify and explain their actions • ES should also do so • Explanation: attempt by an ES to clarify reasoning, recommendations, other actions (asking a question) • Explanation facility (justifier)

23. Explanation Purposes • Make the system more intelligible • Uncover shortcomings of the rules and knowledge base (debugging) • Explain unanticipated situations • Satisfy users’ psychological and/or social needs • Clarify the assumptions underlying the system's operations • Conduct sensitivity analyses

24. Two Basic Explanations • Why Explanations - Why is a fact requested? • How Explanations - To determine how a certain conclusion or recommendation was reached.

25. Metaknowledge • Knowledge about how the system reasons • Knowledge about knowledge • Inference rules are a special case • Metaknowledge allows the system to examine the operation of the declarative and procedural knowledge in the knowledge base • Explanation can be viewed as another aspect of metaknowledge • Over time, metaknowledge will allow ES to create the rationale behind individual rules by reasoning from first principles

26. Generating Explanations • Static Explanation: be anticipated in advance for all questions and answers. • Dynamic Explanation: reconstruct explanation according to the execution pattern of the rules

27. Typology of ES Explanations • Trace, or Line of Reasoning – a record of the inferential steps taken by an ES to reach a conclusion • Justification - explicit description of the causal argument or rationale behind each inferential step taken by the ES

28. Inferencing with Uncertainty • In Step 1, An expert provides inexact knowledge in terms of rules with likelihood values • In Step 2, The inexact knowledge of the basic set of events can be directly used to draw inferences in simple cases (Step 3) • In Step 3, Working with the inference engine, experts can adjust the Step 1 input after viewing the results in Steps 2 and 3.

29. Representing Uncertainty • Numeric • Graphic and Influence Diagram • Symbolic

30. Numeric Uncertainty Representation • Scale (0-1, 0-100) • 0 = Complete uncertainty • 1 or 100 = Complete certainty • Problems with Cognitive Biases • People May be Inconsistent at Different Times

31. Graphic and Influence Diagram • Horizontal bars • Not as accurate as numbers • Experts may not have experience in marking graphic scales • Many experts prefer ranking over graphic or numeric methods

32. Symbolic Uncertainty Representation • Likert Scale Approach • Ranking • Ordinal • Cardinal • Pair-wise Comparison • Fuzzy logic includes a special symbolic representation combined with numbers

33. Probabilities and Related Approaches • The Probability RatioThe degree of confidence in a conclusion can be expressed as a probability. • P(X) = Number of outcomes favoring the occurrence of X / Total number of outcomes

34. The Bayesian Extension • Bayes' Theorem for combining new and existent evidence usually given as subjective probabilities • a subjective probability is provided for each proposition • To revise existing prior probabilities based on new information

35. Dempster-Shafer Theory of Evidence • Distinguishes between uncertainty and ignorance by creating belief functions • Especially appropriate for combining expert opinions, since experts do differ in their opinions with a certain degree of ignorance • Assumes that the sources of information to be combined are statistically independent

36. Theory of Certainty (Certainty Factors) • Uncertainty is represented as a Degree of Belief • Express the Measure of Belief • Manipulate degrees of belief while using knowledge-based systems • Certainty Theory uses Certainty Factors • Certainty Factors (CF) express belief in an event (or fact or hypothesis) based on evidence

37. Several methods of using certainty factors in handling uncertainty in knowledge-based systems • 1.0 or 100 = absolute truth (complete confidence) • 0 = certain falsehood • CFs are NOT probabilities • CFs need not sum to 100

38. Belief and Disbelief • CF[P,E ] = MB[P,E] - MD[P,E] whereCF = certainty factor MB = measure of belief MD = measure of disbelief P = probability E = evidence or event

39. Combining Certainty Factors (AND) IF inflation is high, CF = 50 percent, (A), AND IF unemployment rate is above 7 percent, CF =70 percent, (B), AND IF bond prices decline, CF = 100 percent, (C) THEN stock prices decline • CF(A, B, and C) = Minimum[CF(A), CF(B), CF(C)] • The CF for “stock prices to decline” = 50 percent • The chain is as strong as its weakest link

40. Combining Certainty Factors (OR) IF inflation is low, CF = 70 percent; OR IF bond prices are high, CF = 85 percent; THEN stock prices will be high • Only one IF need be true • Conclusion has a CF with the maximum of the two • CF (A or B) = Maximum [CF (A), CF (B)] • CF = 85 percent for stock prices to be high

41. Approximate Reasoning Using Fuzzy Logic • Fuzzy logic deals with quantifying and reasoning using imprecise and uncertain values • Fuzzy aims at formalizing approximate reasoning process • Fuzzy rules define the mapping of input variables with precise or imprecise values to output variables with precise values • Fuzzy reasoning is the process of making logical inferences based on fuzzy rules and inputs