![FACTS
MATCH EXECUTE
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Fig. 1 the rule Interpreted cycles through a
Match- Execute sequence](https://image.slidesharecdn.com/chapter6knowledgerepresentationusingrules1-170906232825/85/knowledge-representation-using-rules-17-320.jpg)
knowledge representation using rules
- 1. Ch6: Knowledge Representation Using Rules Procedural vs. Declarative Knowledge Logic Programming Forward vs. backward reasoning Matching Control knowledge Slide 1
- 2. Procedural vs. Declarative Knowledge Q Declarative representation –Knowledge is specified but the use is not given. –Need a program that specifies what is to be done to the k nowledge and how. –Example: • Logical assertions and Resolution theorem prover –A different way: Logical assertions can be viewed as a pr ogram, rather than as data to a program. => Logical assertions = Procedural representations of kno wledge Slide 2
- 3. Procedural vs. Declarative Knowledge Q Procedural representation –The control information that is necessary to use the kno wledge is considered to be embedded in the knowledge it self. –Need an interpreter that follows the instructions given in the knowledge. –The real difference between the declarative and the pro cedural views of knowledge lines in where control informa tion resides. • Kowalski’s equation: Algorithm = Logic + Control Slide 3
- 4. Slide 4 Procedural knowledge Declarative knowledge – Knowledge about "how to do something"; e.g., to determine if Peter or Robert is older, first find their ages. – ◊ Focuses on tasks that must be performed to reach a particular objective or goal. – ◊ Examples : procedures, rules, strategies, agendas, models. – Knowledge about "that something is true or false". e.g., A car has four tyres; Peter is older than Robert; – ◊ Refers to representations of objects and events; knowledge about facts and relationships; – ◊ Example : concepts, objects, facts, propositions, assertions, semantic nets, logic and descriptive models. –
- 5. Procedural vs. Declarative Knowledge The real difference between the declarative and the proce dural views of knowledge lies in where control information reside s. Example: man(Marcus) man(Caesar) person(Cleopatra) x: man(x) person(x) person(x)? X is to be binded to a particular value for which person is true. O ur knowledge base justifies any of the following answers y=Marcus y=ceasar Y=Cleopatra. Slide 5
- 6. Procedural vs. Declarative Knowledge •Because there is no more than one value that satisfies the predic ate, but only one value is needed, the answer depends on the ord er in which the assertions are examined. •Declarative assertions do not say how they will be examined. •y=cleopatra is the answer for the question when viewed declarati vely. When viewed procedurally, the answer is Marcus.this happens be cause the first statement the person goal is the inference rule x: man(x) person(x) Slide 6
- 7. Procedural vs. Declarative Knowledge •This rule sets up a subgoal to find a man.Again the statements ar e examined from the beginning and now Marcus is found to satisfy the subgoal and thus also the goal. •So Marcus is reported as the answer. •There is no clear cut answer whether declarative or procedural kn owledge representation frameworks are better. Slide 7
- 8. Logic Programming •Logic Programming is a programming language paradigm o n which logical assertions are viewed as programs. •PROLOG program is described as a series of logical asserti ons each of which is a Horn Clause. Prolog program = {Horn Clauses} –Horn clause: disjunction of literals of which at most one is p ositive literal p,¬pVq,and pq are horn clauses. => Prolog program is decidable –Control structure: Prolog interpreter = backward reasoning + depth-first with backtracking Slide 8
- 9. Logic Programming Q Logic: X: pet(X) ^ small(X) apartmentpet(X) X: cat(X) v dog(X) pet(X) X: poodle(X) dog(X) ^ small(X) poodle(fluffy) Q Prolog: apartmentpet(X) :- pet(X) , small(X). pet(X) :- cat( X). pet(X) :- dog(X). dog(X) :- poodle(X). small(X) :- poodle(X). poodle(fluffy). Slide 9
- 10. Logic Programming Q Prolog vs. Logic –Quantification is provided implicitly by the way the variabl es are interpreted. • Variables: begin with UPPERCASE letter • Constants: begin with lowercase letters or number –There is an explicit symbol for AND (,), but there’s none f or OR. Instead, disjunction must be represented as a list of alternative statements –“p implies q” is written as q :- p. Slide 1 0
- 11. Logic Programming Logical negation cannot be represented explicitly in pure Prolog. – Example: x: dog(x) cat(x) => problem-solving strategy: NEGATION AS FAILURE ?- cat(fluffy). => false b/c it’s unable to prove Fluffy is a cat. Q Negation as failure requires: CLOSED WORLD ASSUM PTION which states that all relevant ,true assertions are contained in our knowledge base or derivable from asser tions that are so contained Slide 1 1
- 12. Forward vs. Backward Reasoning •Reason backward from the goal states: Begin building a tree of mov e sequences that might be solutions by starting with the goal configu rations at the root of the tree. Generate the next level of the tree by fi nding all the rules whose right side match the root node. Use the left sides of the rules to generate the nodes at this second level of the tr ee. Generate the next level of the tree by taking each node at the pr evious level and finding all the rules whose right sides match it. The n use the corresponding left sides to generate the new nodes. Conti nue until a node that matches the initial state is generated. This met hod of reasoning backward from the desired final state if often called goal-directed reasoning. Slide 12
- 13. Forward vs. Backward Reasoning Q Forward: from the start states. Q Backward: from the goal states. •Reason forward from the initial states: Begin building a tree of move sequences that might be solutions by starting with the initial configur ations at the root of the tree. Generate the next level of the tree by f inding all the rules whose left sides match the root node and using th eir right sides to create the new configurations. Continue until a confi guration that matches the goal state is generated. •Reason backward from the goal states: Begin building a tree of mov e sequences that might be solutions by starting with the goal configu rations at the root of the tree. Generate the next level of the tree by fi nding all the rules whose right side match the root node. Slide 13
- 14. Forward vs. Backward Reasoning Q Four factors influence forward or Backward? –Move from the smaller set of states to the larger set of sta tes –Proceed in the direction with the lower branching factor –Proceed in the direction that corresponds more closely wi th the way the user will think –Proceed in the direction that corresponds more closely wi th the way the problem-solving episodes will be triggered Slide 14
- 15. Forward vs. Backward Reasoning Q To encode the knowledge for reasoning, we need 2 kinds of rules: – Forward rules: to encode knowledge about how to respo nd to certain input. – Backward rules: to encode knowledge about how to achi eve particular goals. Slide 15
- 16. KR Using rules IF . . THEN ECA (Event Condition Action) RULES . APLLICATIONS EXAMPLES 1. If flammable liquid was spilled, call the fire depart ment. 2. If the pH of the spill is less than 6, the spill materi al is an acid. 3. If the spill material is an acid, and the spill smells li ke vinegar, the spill material is acetic acid. ( are used to represent rules)
- 17. FACTS MATCH EXECUTE [ ] [ ] [ ] [ ] [ ] [ ] Fig. 1 the rule Interpreted cycles through a Match- Execute sequence
- 18. FACTS A flammable liquid was sp illed The pH of the spill is < 6 Spill smells l ike vinegar The spill ma terial is an a cid MATCH EXECUTE If the pH of the spill is less than 6,the spill material is acid RULES Fig.2 Rules execution can modify the facts in the knowledge base New fact added to the KB
- 19. FACTS A flammable liquid was sp illed The pH of the spill is < 6 Spill smells l ike vinegar The spill ma terial is an a cid ACETIC ACID MATCH EXECUTE If the spill material is an acid and the spill smells like vinegar, the spill material is acet ic acid RULES Fig.3 Facts added by rules can match rules
- 20. FACTS A flammable liquid was sp illed The pH of the spill is < 6 Spill smells l ike vinegar MATCH EXECUTE If a flammable liquid was spilled, call the fi re department RULES Fig.4 Rule execution can affect the real world Fire d ept is called
- 21. The pH of th e spill is < 6 The spill ma terial is an a cid Spill smells l ike vinegar The spill ma terial is an a cetic acid Fig.5 Inference chain for inferring the spill material
- 22. A B G C E H D A E G C B H B F A E G C H D Z A G F D E H B C MATCH MATCH MATCH EXECUTE EXECUTE EXECUTE F &B Z C &D F A D F &B Z C &D F A D F &B Z C &D F A D RULES RULES RULES Fig. 6 An example of forward chaining
- 23. A D C F B Z Fig. 7 Inference chain produced by Fig. 6
- 24. FACTS FACTS FACTS FACTS FACTS FACTS FACTS FACTS FACTS Step 1 2 3 4 5 6 7 8 RULES RULES RULESRULESRULESRULESRULESRULESRULES A E H G CB A E HG B C A E G H B C C C C C C C A A A A A A E E E E E E G G G G G G H H H H H H B B B B B BD F D FZ F&B Z C&D F A D F&B Z C&D F A D F&B Z C&D F A D F&B Z C&D F A D F&B Z C&D F A D F&B Z C&D F A D F&B Z C&D F A D F&B Z C&D F A D F&B Z C&D F A D Need to get F B Z not here Want Z Z h e r e Get C D F not here Want F F here C here Want C Need to Get A D not here Want D Want A A here Have C & D Have F & B Have Z Execute Execute Execute D h e r e Fig. 8 An example of Backward Chaining
- 25. Matching Q How to extract from the entire collection of rules that can be appli ed at a given point? => Matching between current state and the precondition of the rule s Indexing • One way to select applicable rules is to do a simple search throug h all the rules, comparing each one’s preconditions to the current state and extracting all the ones that match. But there are two pro blems with this simple solution: • It will be necessary to use a large number of rules. scanning throu gh all of them at every step of the search would be hopelessly ine fficient. • It is not always immediately obvious whether a rule’s precondition ’s are satisfied by a particular state. Slide 25
- 26. Matching Q Indexing –A large number of rules => too slow to find a rule –Indexing: Use the current state as an index into rules an d select the matching ones immediately –There’s a trade-off between the ease of writing rules (hig h-level descriptions) and the simplicity of the matching pr ocess Slide 26
- 27. Matching – RETE gains efficiency from three major sources. – The temporal nature of data. rules usually do not alter the state description radically. Instead a rule will add one or t wo elements or delete one or two elements but the state r emains the same.RETE maintains a network of rule condi tions and it uses changes in the state description to deter mine which new rules might apply. – Structural similarity in rules.Eg.one rule concludes jaguar( X)if mammal(x),feline(x),carnivorous(x) and has-spots(x). Another rule concludes tiger(x) and is identical to the first r ule except that it replaces has-spots with has-stripes. If tw o rules are matched independently, a lot of work is repeat ed unnecessarily. RETE stores rules so that they share str uctures in memory.sets of conditions that appear in sever al rules are matched once per cycle. Slide 27
- 28. Matching -Persistence of variable binding consistency: while all the individual preconditions of a rule might be met there may b e variable binding conflicts that prevent the rule from firing. Son(Mary,joe) and son (Bill,Bob) . The individual preconditions of the rule can be matched Son(x,y)A son(y,z)grandparent(x,z) Can be matched, but not in a manner that satisfies the con straint imposed by the variable y. Slide 28
- 29. Matching Q Approximate matching –Rules should be applied if their preconditions approxim ately match the current situation –Example: A speech-understanding program • Rules: A description of a physical waveform to p hones (a, e, …) • Physical signal: differences in the way individuals speak, result of background noise, … Slide 29
- 30. Matching ELIZA (Weizenbaum, 1966): Person: Men all are alike. ELIZA: In what way? Person: They're always bugging us about something or other. ELIZA: Can you think of specific example? Person: Well, my boyfriend made me come here. ELIZA: Your boyfri end made you come here. Person: He says I'm depressed much of the time. ELIZA: I am sorry to hear you are depressed. Slide 30
- 31. Matching ELIZA (Weizenbaum, 1966): Person: It's true. I am unhap py. ELIZA: Do you think coming here will help you not to be u nhappy? Person: I need some help, that much seems certain. ELIZ A: What would it mean to you if you got some help? Person: Perhaps I could learn to get along with my brothe r. ELIZA: Tell me more about your family. .... Slide 31
- 32. Matching ELIZA (Weizenbaum, 1966): (X me Y) (I remember X) (X you Y) (Why do you remember X just now?) (My {family-member} is Y) (Who else in your family is Y?) (X {family-member} Y) (Tell me more about your family) .... Person: My brother is mean to me. ELIZA: Who else in your family is mean to you? .... Slide 32
- 33. Matching Conflict resolution: The result of the matching process is a list of rules whose antecedents –Preferences based on rules: • Specificity of rules • Physical order of rules –Preferences based on objects: • Importance of objects • Position of objects –Preferences based on action: • Evaluation of states Slide 33
- 34. Control Knowledge Knowledge about which paths are most likely to lead quickl y to a goal state is often called search control knowledge. – Which states are more preferable to others. – Which rule to apply in a given situation. – The order in which to pursue subgoals – Useful sequences of rules to apply. Search control knowledge = Meta knowledge Slide 34
- 35. Control Knowledge There are a number of AI systems that represent their control knowledge with rules. Example SOAR,PRODIGY SOAR is a general architecture for building intelligent systems. Slide 35
- 36. Control Knowledge PRODIGY is a general purpose problem solving system, th at incorporates several different learning mechanisms. It can acquire control rules in a number of ways: Through hand coding by programmers Through a static analysis of the domain’s operators. Through looking at traces of its own problem solving behav ior. PRODIGY learns control rules from its experience, but unlik e SOAR it learns from its failures. PRODIGY pursues an unfruitful path, it will try to come uo with an explanation of why that path failed. It will then us e that explanation to build control knowledge that will hel p it avoid fruitless search paths in future. Slide 36
- 37. Control Knowledge Two issues concerning control rules: • The first issue is called the utility problem. As we add mo re and more control knowledge to a system, the system i s able to search more judiciously. If there are many contr ol rules, simply matching them all can be very time consu ming. • the second issue concerns with the complexity of the pro duction system interpreter. Slide 37