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Chapter 16

Chapter 16. Logic Programming Languages. Chapter 16 Topics. Introduction A Brief Introduction to Predicate Calculus Predicate Calculus and Proving Theorems An Overview of Logic Programming The Origins of Prolog The Basic Elements of Prolog Deficiencies of Prolog

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Chapter 16

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  1. Chapter 16 Logic Programming Languages

  2. Chapter 16 Topics Introduction A Brief Introduction to Predicate Calculus Predicate Calculus and Proving Theorems An Overview of Logic Programming The Origins of Prolog The Basic Elements of Prolog Deficiencies of Prolog Applications of Logic Programming

  3. Introduction Definition: logic programming is programming that uses a form of symbolic logic as a programming language Definition: logic programming languages are languages based on symbolic logic (also called declarative languages) Use a logical inferencing process to produce results Declarative rather that procedural: only specification of results are stated (not detailed procedures for producing them)

  4. Advantages Programs are based on logic • likely to be logically organized and written Processing is naturally parallel • interpreters can take advantage of multi-processor machines Programs are concise • development time is decreased • good for prototyping

  5. A Brief Introduction to Predicate Calculus Overview Propositions Clausal Form

  6. Predicate CalculusOverview Definition: a proposition is a logical statement that is true or not true Consists of 1. objects, and 2. relationships of objects to each other Definition: formal logic is a system of describing propositions and checking them for validity

  7. Predicate CalculusOverview Definition: symbolic logic is a system meeting the three basic needs of formal logic: (The system must be able to) 1. express propositions 2. express relationships between propositions 3. describe proposition inference (used to create new propositions from other propositions) The particular form of symbolic logic used for logic programming is called First Order Predicate Calculus

  8. Predicate CalculusPropositions - Objects Objects in propositions are represented by simple terms: either constants or variables Constant a symbol that represents an object Variable a symbol that can represent different objects at different times (different from variables in imperative languages)

  9. Predicate CalculusProposition Types Atomic propositions • consist of compound terms Compound term • one element of a mathematical relation • written like a mathematical function • A mathematical function is a mapping • Can be written as a table

  10. Predicate CalculusPropositions – Compound Terms A compound term is composed of two parts: • Functor = function symbol providing a name • Ordered list of parameters (tuple) Examples: student(jon) like(seth, OSX) like(nick, windows) like(jim, linux)

  11. Predicate CalculusProposition Forms Propositions can be stated in two forms: Fact: proposition is assumed to be true Query: truth of proposition is to be determined Compound proposition: Have two or more atomic propositions Propositions are connected by operators

  12. Predicate CalculusLogical Operators

  13. Predicate Calculus Quantifiers

  14. Predicate CalculusClausal Form TOO MANY WAYS TO STATE THE SAME THING! So…use a standard form for propositions, called Clausal form: B1  B2 ... Bn A1  A2 ... Am this means: if all the As are true, then at least one B is true Antecedent: right side B1  B2  …  Bn A1  A2  …  Am Consequent: left side B1  B2  …  Bn A1  A2  …  Am

  15. Predicate Calculusand Proving Theorems One use of propositions is to discover new theorems that can be inferred from known axioms and theorems Definition: Resolution is a method of inferencing that allows propositions to be computed from given propositions

  16. Resolution Really a form of data reduction! Suppose P1  P2 Q1  Q2 If P1 and Q2 are identical, then Q1  P2 But it’s more complex than this...

  17. Resolution Definition: Unification is finding values for variables in propositions that allows matching process to succeed Definition: Instantiation is assigning temporary values to variables to allow unification to succeed After instantiating a variable with a value, if matching fails, then it may be necessary to backtrack and instantiate with a different value

  18. Theorem Proving Proof by Contradiction Definition: The hypotheses is a set of pertinent propositions Definition: The goal is the negation of the theorem stated as a proposition Theorem is proved by finding an inconsistency

  19. Theorem Proving When propositions are used for resolution, only a restricted “clausal form” can be used: A Horn clause can have only two forms: Headed: single atomic proposition on left side Headless: empty left side (used to state facts) Most propositions can be stated as Horn clauses

  20. An Overview ofLogic Programming Declarative semantics: There is a simple way to determine the meaning of each statement Simpler than the semantics of imperative languages Logic Programming is nonprocedural ! Logic programs do not state how a result is to be computed, but rather the form of the result.

  21. An Overview ofLogic Programming Example: Sorting a List Describe the characteristics of a sorted list, not the process of rearranging a list sort(old_list, new_list)  permute(old_list, new_list)  sorted(new_list) sorted(list)  j such that 1  j < n , list(j)  list (j+1) A

  22. The Basic Elements of Prolog Edinburgh Syntax Terma constant, variable, or structure Constantan atom or an integer Atoma symbolic value of Prolog An atom consists of either: • a string of letters, digits, and underscores beginning with a lowercase letter • a string of printable ASCII characters delimited by apostrophes

  23. The Basic Elements of Prolog Variable: any string of letters, digits, and underscores beginning with an uppercase letter Instantiation: binding of a variable to a value Lasts only as long as it takes to satisfy one complete goal Structure: represents atomic proposition <functor>(<parameter list>)

  24. Fact Statements Used for the hypotheses Headless Horn clauses student(jonathan). sophomore(ben). brother(tyler, cj).

  25. Rule Statements Used for the hypotheses Headed Horn clause Right side: antecedent (if part) May be single term or conjunction Left side: consequent(then part) Must be single term Conjunction: multiple terms separated by logical AND operations (usually implied)

  26. Goal Statements For theorem proving, theorem is in form of proposition that we want system to prove or disprove – goal statement Same format as headless Horn student(james) Conjunctive propositions and propositions with variables also legal goals father(X,joe)

  27. Resolution Definition: Unification is finding values for variables in propositions that allows matching process to succeed Definition: Instantiation is assigning temporary values to variables to allow unification to succeed After instantiating a variable with a value, if matching fails, then it may be necessary to backtrack and instantiate with a different value

  28. Theorem Proving Proof by Contradiction Definition: The hypotheses is a set of pertinent propositions Definition: The goal is the negation of the theorem stated as a proposition Theorem is proved by finding an inconsistency

  29. Goal Statements For theorem proving, theorem is in form of proposition that we want system to prove or disprove – goal statement Same format as headless Horn student(james) Conjunctive propositions and propositions with variables also legal goals father(X,joe)

  30. Inferencing Process Bottom-up resolution (forward chaining) • Begin with facts and rules, then try to find a sequence that leads to the goal • Good for a large set of possible answers Top-down resolution (backward chaining) • Begin with the goal, then try to find a sequence that leads to a set of facts already known • Good for a small set of possible answers Prolog implementations use backward chaining

  31. Inferencing Process When seeking a goal with multiple subgoals, if the truth of one of the subgoals cannot be shown, then reconsider the previous subgoal to find an alternative solution (called “backtracking”), beginning the search where the previous search left off This can take lots of time and space because it may be necessary to find all possible proofs to every subgoal

  32. Simple Arithmetic Prolog supports integer variables and integer arithmetic Theis binary operator takes • right operand: an arithmetic expression • left operand: a variable A is B / 10 + C Not the same as an assignment statement!

  33. List Structures Other basic data structure (besides atomic propositions): List Listis a sequence of any number of elements Elements can be atoms, atomic propositions, or other terms (including other lists) [apple, prune, grape, kumquat] [] (empty list) [X | Y] (head X and tail Y)

  34. Prolog append Function Definition append([],List,List). append([Head|List_1], List_2, [Head|List_3]) :- append(List_1, List_2, List_3).

  35. Prolog reverse Function Definition reverse([],[]). reverse([Head|Tail],List) :- reverse(Tail,Result), append(Result,[Head],List).

  36. Rule Statements parent(kim,kathy):- mother(kim,kathy). Can use variables (universal objects) to generalize meaning: parent(X,Y) :- mother(X,Y). grandparent(X,Z) :- parent(X,Y), parent(Y,Z). sibling(X,Y) :- mother(M,X), mother(M,Y), father(F,X), father(F,Y).

  37. Inferencing Process of Prolog Queries are called goals If a goal is a compound proposition, each of the facts is a subgoal To prove a goal is true, must find a chain of inference rules and/or facts. For goal Q: B :- A C :- B ... Q :- P Process of proving a subgoal is called matching, satisfying, or resolution

  38. Inferencing Process When goal has more than one subgoal, it is possible to use either Depth-first search find a complete proof for the first subgoal before working on others Breadth-first search work on all subgoals in parallel Prolog uses depth-first search Requires fewer computer resources

  39. Trace Built-in structure that displays instantiations at each step Tracing model of execution - four events: Call beginning of attempt to satisfy goal Exit when a goal has been satisfied Redo when backtrack occurs Fail when goal fails

  40. Trace Model

  41. Prolog Example 1 speed(ford,100). speed(chevy,105). speed(dodge,95). speed(volvo,80). time(ford,20). time(chevy,21). time(dodge,24). time(volvo,24). distance(X,Y) :- speed(X,Speed), time(X,Time), Y is Speed * Time. distance(chevy, Chevy_Distance).

  42. Prolog Example 1 trace. distance(chevy, Chevy_Distance). (1) 1 Call: distance(chevy, _0)? (2) 2 Call: speed(chevy, _5)? (2) 2 Exit: speed(chevy, 105) (3) 2 Call: time(chevy, _6)? (3) 2 Exit: time(chevy, 21)? (4) 2 Call: _0 is 105*21? (4) 2 Exit: 2205 is 105*21 (1) 1 Exit: distance(chevy, 2205) Chevy_Distance = 2205

  43. Prolog Example 2 likes(jake,chocolate). likes(jake,apricots). likes(darcie,licorice). likes(darcie,apricots). trace. likes(jake,X), likes(darcie,X).

  44. Prolog Example 2 trace. likes(jake,X), likes(darcie,X). (1) 1 Call: likes(jake, _0)? (1) 1 Exit: likes(jake, chocolate)? (2) 1 Call: likes(darcie, chocolate)? (2) 1 Fail: likes(darcie, chocolate)? (1) 1 Redo: likes(jake, _0)? (1) 1 Exit: likes(jake, apricots)? (3) 1 Call: likes(darcie, apricots)? (3) 1 Exit: likes(darcie, apricots)? X = apricots

  45. Prolog append Function Definition append([],List,List). append([Head|List_1], List_2, [Head|List_3]) :- append(List_1, List_2, List_3).

  46. Prolog reverse Function Definition reverse([],[]). reverse([Head|Tail],List) :- reverse(Tail,Result), append(Result,[Head],List).

  47. Prolog reverse Function Definition % Here is a more efficient (iterative/tail recursive) version. reverse([],[]). reverse(L,RL) :- reverse(L,[],RL). reverse([],RL,RL). reverse([X|L],PRL,RL) :- reverse(L,[X|PRL],RL).

  48. Prolog member Function Definition member(Element, [Element | _]). member(Element, [_ | List]) :- member(Element, List).

  49. Logic ProgrammingAdvantages • Prolog programs are based on logic • they are likely to be more logically organized and written • Processing is naturally parallel • Prolog interpreters can take advantage of multi-processor machines • Programs are concise • development time is decreased • good for prototyping

  50. Logic ProgrammingDeficiencies Resolution Order Control Closed-World Assumption The Negation Problem Intrinsic Limitations

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