1. CSCI 4310 Lecture 7: Rule Systems

2. Book Winston Chapter 7

3. Knowledge Representation General problem-solving techniques are useful, but effectiveness often depends on extensive, domain- specific knowledge. Knowledge-based systems use a KNOWLEDGE BASE (KB) of facts about the world Knowledge usually comes from “experts” in the domain.

4. Examples of knowledge Diagnosing a programming bug Ex: checking for base cases in recursive routines Requires domain knowledge

5. Using Knowledge Representations Contents of KB is part of cognitive model Knowledge Base Inference Mechanism(s) Learning Mechanism(s) Examples, Statements Questions, requests Answers, analyses

6. Knowledge Representation (KR) language Expressiveness can all the knowledge required for the problem be represented adequately? Naturalness Does the representation allow the knowledge to be input and manipulated in a natural fashion?

7. Knowledge Representation (KR) language continued Efficiency Can the system access and process the domain knowledge efficiently? Inference Does the representation support the generation of inferences? (new knowledge)

8. Definition A knowledge-based (or expert) system is: An AI program Capable of representing and reasoning about some knowledge-rich domain With a view to solving problems and giving advice

9. KB system levels We can talk about knowledge-based systems at different levels: Knowledge Logical Implementation

10. KB system levels continued The knowledge level describes what is known independent of representation. knowing in an abstract sense that robins are birds The logical level describes the statement(s) in the Knowledge Representation model that represent a fact. isa(robin,bird)

11. KB system levels continued The implementation level refers to the way in which the knowledge is encoded isa(robin,bird) might be encoded as List Array Database record Something more abstract

12. Getting started What types of information need to be represented? Which knowledge representation model should we use? How should information be encoded in the knowledge representation model? How will the information be accessed?

13. Types of Knowledge Declarative: facts about the world. ex: Robins have wings Procedural: Operations embodying knowledge ex: an algorithm for addition Analogy: Associating knowledge about different things. ex: Robins can fly. Robins are like sparrows. So I suspect that sparrows can fly too.

14. Types of Knowledge cont. Generalization: Making generalizations from specific examples: ex: Robins can fly, sparrows can fly, cardinals can fly…I suspect all birds can fly. Meta-level Knowledge: Knowledge about what is known. ex: I don’t know Jim Rogers, so I probably don’t know his phone number. Rough definition of meta: one level higher

15. Explicit vs. Implicit Knowledge Explicit knowledge is information that is encoded directly in the representation. ex: has_part(robin,wing) Implicit knowledge is information that can be derived from the representation. explicit: has-part(bird,wing) explicit: is-a(robin,bird) implicit: has-part(robin,wing)

16. Explicit vs. Implicit Knowledge Trade-off: Relying on implicit knowledge can reduce the size of the knowledge base, but it can increase the access time…. Similar to using opening chess moves calculated offline. Time vs. space once again

17. Explicit vs. Implicit Knowledge Trivial example previously is deceptive This can be prohibitively expensive Satisfiability (SAT) problem The granddaddy NP-complete problem Graph coloring can be encoded as SAT Many planning and scheduling problems also

18. Knowledge Representation Models Propositional Logic Predicate Calculus Production Systems (rule-based systems) Semantic networks Frames Bayesian Networks

19. Example http://www.aiinc.ca/demos/whale.html

20. Components of a rule­based system Working memory Knowledge base of current assertions sometimes called context Things that are believed to be true about the world. This will change as rules are evaluated Overlap with concepts from Automata Theory

21. Components of a rule­based system cont. Rule base: set of inference rules Each rule (sometimes called a production) is a condition­action pair. All predicates in the condition must be true for the rule to fire. An action can add or delete facts from the working memory. Rule interpreter Determines which rules to apply and executes actions.

22. Forward Reasoning Until no rule can fire or goal state is achieved: 1. Find all rules whose left sides match assertions in working memory. 2. Pick some to execute; modify working memory by applying right sides of rules. There is no point executing multiple rules that take identical actions. Rules may be implicitly ordered in terms of likelihood, importance, etc. Choose the first or highest priority satisfied rule 3. Iterate

23. Example 1. If A or B then C 2. If C or (D and E) then F 3. If C and F then G 4. If G or H then I Working Memory: A E Which rules will follow? Cycle 1: Cycle 2: Cycle 3: etc.

24. Three Parts to the Forward­Chaining Rule Interpreter Match: identify which rules are applicable at any given point in the reasoning Conflict Resolution: select which (of possibly many rules) should be applied at any given point in the reasoning Execute: execute the right­hand side of the chosen rule

25. Three Parts to the Forward­Chaining Rule Interpreter cont. Heart of Rule-Based Systems The Knowledge Base Matching algorithm Conflict Resolution

26. Matching for Forward­Chaining Simply search all rules incrementally Problems: A large rule base would lead to a very slow search. Satisfaction of the rule's preconditions may not be obvious. Could view as a search through all possible variable bindings and use depth­first search, for example. It's not obvious what sort of heuristics would help.

27. Forward Chaining Example IF X & Y & Z THEN C unknown

28. Pros and Cons of Forward Reasoning Forward reasoning has no goal in mind so it can generate a lot of irrelevant assertions in undirected fashion. Think of all the info you discard when you add something to your working memory I am driving… On a road Roads are made of asphalt My tires are making contact with the road None of this is relevant. Humans detect relevancy to prune the search space Some mental disorders cloud this ability

29. Pros and Cons of Forward Reasoning Forward­chaining systems often require user to encode heuristic knowledge to guide the search. Rule base often consists of both domain knowledge and control knowledge.

30. Rules 1. If (?x has hair) then (?x is mammal) 2. If (?x has feathers) then (?x is bird) 3. If (?x files) and (?x lays eggs) then (?x is bird) 4. If (?x is mammal) and (?x eats meat) then (?x is carnivore) 5. If (?x is mammal) and (?x eats grass) then (?x is herbivores) 6. If (?x is mammal) and (?x has hooves) then (?x is herbivores) 7. If (?x is carnivore) and (?x has tawny color) then (?x is tiger) 8. If (?x is herbivores) and (?x has black/white color) then (?x is zebra) 9. If (?x is bird) and (?x swims) then (?x is penguin) 10. If (?x is bird) and (?x files) and (?x black/white color) then (?x is albatross)

31. Facts F1: (Subject has hair) F2: (Subject eats grass) F3: (Subject has black/white color) Stored in Working Memory

32. Backward Reasoning Start with an hypothesis and… Some assertions in working memory Work backward from the hypothesis Tries to counter the undirected nature of forward-chaining

33. Backward Reasoning Until the hypothesis has been satisfied, or until no more rules are applicable, do the following: 1. Find all rules whose right side matches the hypothesis.

34. Backward Reasoning part 2 2. For each matching rule: Try to support each of the rule's conditions by matching against assertions in working memory, or generating sub­hypotheses and backward chaining recursively. If all the rule's conditions are satisfied, then success!

35. Control: Backward Chaining IF A THEN C IF B THEN C IF C THEN D If we want to establish D as being true, then we should establish C as being true. To do this we need to show that A is true or that B is true Eventually we have to ask the user of the system go to a database interrogate a sensor...

36. Rules form a search tree R1: IF A and B and C and D THEN E R2: IF X and Y THEN A R3: IF Z THEN B R4: IF W THEN C R5: IF F THEN D R6: IF G THEN D R7: IF H THEN G Prove E? Facts: X, Y, Z, W, H

37. Rules drawn as a tree E A B C D X Y Z W F G H R1 R2 R3 R4 R5 R6 R7 AND

38. Search aspects of backward chaining What search strategy do we use normally depth first When two rules are available to prove a conclusion, which one do we use first? if A then C if B then C When a premise consists of multiple components, what order do we work on them? If A and B and C then D

39. Knowledge about animals (1) 1. If (?x has hair) then (?x is mammal) 2. If (?x feeds young milk) then (?x is mammal) 3. If (?x has feathers) then (?x is bird) 4. If (?x flies) and (?x lays eggs) then (?x is bird) 5. If (?x is mammal) and (?x eats meat) then (?x is carnivore) 6. If (?x is mammal) and (?x has pointed teeth) and (?x has claws) and (?x has eyes point forward) then (?x is carnivore) 7. If (?x is mammal) and (?x eats grass) then (?x is herbivore) 8. If (?x is mammal) and (?x has hooves) then (?x is herbivore)

40. Knowledge about animals (2) 9. If (?x is carnivore) and (?x has tawny color) and (?x has dark spots) then (?x is cheetah) 10. If (?x is carnivore) and (?x has tawny color) and (?x has dark stripes) then (?x is tiger) 11. If (?x is herbivore) and (?x has tawny color) and (?x has dark spots) and (?x has long neck) then (?x is giraffe) 12. If (?x is herbivore) and (?x has black/white color) then (?x is zebra) 13. If (?x is bird) and (?x has long neck) then (?x is ostrich) 14. If (?x is bird) and (?x swims) and (?x is black/white color) then (?x is penguin) 15. If (?x is bird) and (?x flies) and (?x is black/white color) then (?x is albatross)

41. What species is george? Want to know what species george is Information available on request: (george has hair). (george lays unknown). (george, unknown). (george eats unknown). (george has pointed teeth). (george has claws). (george has eyes point forward). (george tawny color). (george has dark spots). (george neck short).

42. Matching for Backward Chaining With forward chaining we can generate all applicable rules and then select one using a well­defined conflict resolution strategy. Backward chaining is more complicated because...

43. Matching for Backward Chaining The hypothesis must be matched against WM assertions and rule consequents. The hypothesis can contain variables. More than one rule can provide a variable binding. Backward chaining typically uses DFS with backtracking to select individual rules.

44. Fan Out A set of facts can lead to many conclusions A higher degree of fan out argues for backward chaining

45. Fan In A higher degree of fan in argues for forward chaining

46. Pros and Cons of Backward Reasoning Backward­chaining is best when there is a distinct goal state that is likely to be obtainable. (If there are many acceptable goal states, then forward chaining might be fine.) Backward reasoning can be efficient when the branching factor of the initial working memory is higher than the branching factor of the assertions that lead to the conclusion. Backward reasoning only asks for information when it is relevant, which is extremely useful when knowledge is expensive to access.

47. Rule-based Systems Many problems are best characterized by a set of rules. If-then rules (implication) are the focus of problem-solving. Problem-solving systems that use rule-based knowledge representation and rule-based reasoning are sometimes called production systems. This is not how humans do it, though

48. Rule-based Systems Production systems may use a somewhat less formal representation scheme, use an incomplete inference procedure, and treat the consequents of rules as logical actions rather than logical conclusions. Book refers to this as a reaction system Need conflict-resolution procedure to decide which action to take

50. What can an expert system do? In principle, anything that an expert can do, and can be persuaded to articulate. It is useful to distinguish between analytic tasks which involve analysing something which already exists, and synthetic tasks which involve creating something which doesn’t.

51. Experts vs. knowledge-engineers An expert is someone who: Possesses knowledge about a domain Is skilled at applying this knowledge to problems in the domain A knowledge-engineer is skilled at interacting with domain experts and formalizing their knowledge Automation of this process is very desirable But also difficult The field of machine learning

52. Advantages of rule­based systems Modularity: The knowledge is represented as small, individual pieces. Changes to one rule do not directly affect other rules. Parallelism. Uniformity: All knowledge uses the same simple representation. We can build general­purpose rule interpreters. Naturalness: Domain experts can often easily express their knowledge in terms of rules. Rule­based systems generate explanations that are easy to understand.

53. Disadvantages of rule­based systems Inefficiency: Can be difficult to focus the effort in productive directions. (Writing good control knowledge is hard!) Difficult to organize knowledge hierarchically. Cannot easily control the grain size of steps. Opacity: Can be hard to follow problem­solving control. Rule interactions can be difficult to anticipate and control.

54. Good domains for rule­based systems Domains that are characterized by a lot of individual facts (e.g., medicine), but not necessarily a formal, unified theory (e.g., physics). Domains for which the actions are mostly independent of one another. For example, a patient monitoring system vs. income tax accounting. Domains for which subject knowledge can be easily separated from process knowledge. For example, taxonomic information vs. algorithmic information.

55. Example systems MYCIN Identify bacterial infections and recommend treatment R1 Configure DEC computers PROSPECTOR Interpret geological data and advise on the presence of ore deposits

56. MYCIN Developed at Stanford University by Shortliffe, Buchanan et al. from 1972. Concerned with the diagnosis and treatment of some infectious diseases. Steps: Identify organisms Recommend treatment Important because its approach has been widely copied

57. MYCIN - Sample dialogue What is the patient’s name? John Smith Age? 55 Have you obtained positive cultures indicating general type? Yes Let’s call the most recent culture CULTURE- 1. From what site was CULTURE-1 taken? From the blood ….

58. MYCIN - Sample dialogue 2 When? May 9, 1988 Let’s call the first significant organism from this blood culture ORGANISM-1. Do you know the identity of ORGANISM-1? No Is ORGANISM-1 a rod or a coccus or something else? Rod What is the gramstain of ORGANISM-1? Gramnegative …

59. MYCIN - Sample dialogue 3 My therapy recommendations will be based on the following possible identities of the organism(s) that seem to be significant: the identity of ORGANISM-1 may be PSEUDOMONAS the identity of ORGANISM-2 may be KLEBSIELLA the identity of ORGANISM-3 may be ENTEROBACTER My preferred therapy recommendation is as follows: Give the following in combination: GENTAMYCIN Dose: 1.7 mg/kg Q8H - IV or IM Comments: Modify dose in renal failure CARBENICILLIN...

60. Control Strategies Concerned with the general issue of how to control the reasoning process so that it efficiently finds a solution. Control in Search refers to the order or selection of nodes/states to explore. Control in Rule Bases refers to the order or selection of rules/actions to execute at each cycle of a rule­based system. (conflict resolution) output of matching => list of applicable rules and their variable bindings output of conflict resolution => which rule to apply

61. Conflict Resolution Strategies 1. Assign absolute priorities to each rule in advance. ControlPanel(x) and Dusty(x) => Action Dust(x) ControlPanel(x) and MeltdownLightOn(x) => Action Evacuate(x) 2. Assign relative priorities to the rules, depending upon the problem state.

62. Conflict Resolution Strategies 2 3. Choose the most specific rule (i.e., the rule with conditions that most closely match the current situation). Mammal(x) => add(Legs(x; 4)) Mammal(x) and Human(x) => add(Legs(x; 2)) 4. Choose a rule that uses assertions that were made most recently.

63. Semantic Approaches to Conflict Resolution Preferences based on objects that matched -- Add knowledge to rules to indicate more important objects Preferences based on the action that the matched rule would perform Treat conflict resolution as (search) problem in its own right

64. Control Rules Some systems use control rules (sometimes called meta­rules) to make the heuristic search strategy explicit and easily modifiable. This is called search control knowledge.

65. Control Rules Meta­rule Example Under conditions A and B, Rules that do {not} mention X {at all, in their LHS, in their RHS } will {definitely be useless, probably be useless,... probably be especially useful, definitely be especially useful }

66. Control knowledge can take a variety of forms 1. Knowledge about which states are preferable 2. Knowledge about which rule is best to apply when 3. Knowledge about how to order subgoals 4. Knowledge about useful rule sequences

67. Handling uncertainty Not all our knowledge is 100% certain Different approaches to uncertainty can be viewed along the following dimensions: What knowledge and data can be represented as being uncertain? What is this representation? How are different pieces of evidence combined? How do different levels of certainty affect what the system does?

68. Uncertainty in MYCIN Both knowledge and data can be represented as being uncertain. Rules (knowledge): IF the stain of the organism is Gram negative AND the morphology of the organism is rod AND the aerobicity of the organism is aerobic THEN the class of the organism is enterobacteriaceae with confidence 0.8

69. Uncertainty in MYCIN Data: the stain of the organism is definitely Gram negative (1.0). the morphology is rod, with confidence 0.8. the morphology is coccus, with confidence 0.2 the aerobicity is aerobic, with confidence 0.6 the class is enterobacteriaceae, with confidence 0.48

70. Uncertainty in MYCIN The representation uses Certainty Factors (CF) -1  CF  +1 Data: CF = +1the fact is certainly true CF = 0we know nothing about whether the fact is true or not CF = -1the fact is certainly not true

71. Uncertainty in MYCIN Rules: CF = +1 if the premise is known to be true then the conclusion is known to be true CF = 0 the premise brings no evidence for or against the conclusion CF = -1 if the premise is known to be true then the conclusion is known to be false