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Logic Programming Lecture 2: Unification and proof search.

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1 Logic Programming Lecture 2: Unification and proof search

2 James CheneyLogic ProgrammingSeptember 22, 2014 Outline for today Quick review Equality and unification How Prolog searches for answers

3 James CheneyLogic ProgrammingSeptember 22, 2014 Quick review Atoms bart ‘Mr. Burns’ Variables X Y Z Predicates p(t 1,...,t n ) Terms Facts father(homer, bart). Goals p(t 1,...,t n ),..., q(t 1 ',...,t n '). Rules p(ts) :- q(ts’),..., r(ts’’).

4 James CheneyLogic ProgrammingSeptember 18, 2014 Infix operators Prolog has built-in constants and infix operators Examples: Equality: t = u (or =(t,u)) Pairing: (t,u) (or,(t,u)) Empty list: [] List concatenation: [X|Y] (or.(X,Y)) You can also define your own infix operators!

5 James CheneyLogic ProgrammingSeptember 18, 2014 General observations Prolog is untyped everything is a "term" Prolog is declarative "predicates" with side effects, such as print, are the exception, not the rule Prolog does not have explicit control flow constructs (while, do) the search strategy allows us to simulate iteration but this is not usually the best way to program Therefore, try to forget what you already know from other languages

6 James CheneyLogic ProgrammingSeptember 18, 2014 Terms Also have... Numbers: 1 2 3 42 -0.12345 Additional constants and infix operators More on these later.

7 James CheneyLogic ProgrammingSeptember 22, 2014 Unification (I) The equation t = u is a basic goal with a special meaning What happens if we ask: ?- X = c ?- f(X,g(Y,Z)) = f(c,g(X,Y)) ?- f(X,g(Y,f(X))) = f(c,g(X,Y)) And how does it do that?

8 James CheneyLogic ProgrammingSeptember 22, 2014 Unification (II) ?- X = c. X=c yes ?- f(X,g(Y,Z)) = f(c,g(X,Y)). X=c Y=c Z=c yes ?- f(X,g(Y,f(X))) = f(c,g(X,Y)). no

9 James CheneyLogic ProgrammingSeptember 22, 2014 Unification (III) A substitution is a mapping from variables to terms X 1 =t 1,...,X n =t n Given two terms t and u with free variables X 1...X n a unifier is a substitution that makes t and u equal

10 James CheneyLogic ProgrammingSeptember 22, 2014 Example (I) f(X,g(Y,Z)) = f(c,g(X,Y)) ff gg YZ c XY X X = c Y = X Z = Y

11 James CheneyLogic ProgrammingSeptember 22, 2014 Example (I) f(X,g(Y,Z)) = f(c,g(X,Y)) ff gg YZ c XY X X = c Y = c Z = c c cccc

12 James CheneyLogic ProgrammingSeptember 22, 2014 Example (II) f(X,g(Y,f(X))) = f(c,g(X,Y)) ff gg Y c XY X f X X = c Y = X

13 James CheneyLogic ProgrammingSeptember 22, 2014 Example (II) f(X,g(Y,f(X))) = f(c,g(X,Y)) ff gg Y c XY X f X X = c Y = c Y = f(X) f(X) = c?? c ccc c

14 James CheneyLogic ProgrammingSeptember 22, 2014 Robinson's Algorithm (I) Consider a general unification problem t 1 = u 1, t 2 = u 2,..., t n = u n Reduce the problem by decomposing each equation into one or more "smaller" equations Succeed if we reduce to a "solved form", otherwise fail

15 James CheneyLogic ProgrammingSeptember 22, 2014 Robinson's Algorithm (II) Two constants unify if they are equal. c = c, P → P c = d, P → fail.

16 James CheneyLogic ProgrammingSeptember 22, 2014 Robinson's Algorithm (III) Two function applications unify if the head symbols are equal, and the corresponding arguments unify. f(t 1,...,t n ) = f(u 1,...,u n ), P → t 1 = u 1,... t n = u n, P Must have equal numbers of arguments f(...) = g(...), P → fail f(...) = c, P → fail.

17 James CheneyLogic ProgrammingSeptember 22, 2014 Robinson's Algorithm (IV) Otherwise, a variable X unifies with a term t provided X does not occur in t. X = t, P → P[t/X] (occurs-check: X must not be in Vars(t)) Proceed by substituting t for X in P.

18 James CheneyLogic ProgrammingSeptember 22, 2014 Occurs check What happens if we try to unify X with something that contains X? ?- X = f(X). Logically, this should fail there is no (finite) unifier! Most Prolog implementations skip this check for efficiency reasons can use unify_with_occurs_check/2

19 James CheneyLogic ProgrammingSeptember 22, 2014 Execution model The query is run by trying to find a solution to the goal using the clauses Unification is used to match goals and clauses There may be zero, one, or many solutions Execution may backtrack Formal model called SLD resolution which you'll see in the theory lectures

20 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (I) Idea: To solve atomic goal A, If B is a fact in the program, and θ(A) = θ(B), then return answer θ Else, if B :- G 1,...,G n is a clause in the program, and θ unifies A with B, then solve θ( G 1 )... θ(G n ) Else, give up on this goal. Backtrack to last choice point Clauses are tried in declaration order Compound goals are tried in left-right order

21 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (II) Prolog normally tries clauses in order of appearance in program. Assume: foo(a). foo(b). foo(c). Then: ?- foo(X). foo(X)

22 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (II) Prolog normally searches for clauses in order of appearance in database. Assume: foo(a). foo(b). foo(c). Then: ?- foo(X). X = a foo(X) X=a done

23 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (II) Prolog normally searches for clauses in order of appearance in database. Assume: foo(a). foo(b). foo(c). Then: ?- foo(X). X = a; X = b foo(X) X=a done X=b done

24 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (II) Prolog normally searches for clauses in order of appearance in database. Assume: foo(a). foo(b). foo(c). Then: ?- foo(X). X = a; X = b; X = c foo(X) X=a done X=b done X=c done

25 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (II) Prolog normally searches for clauses in order of appearance in database. Assume: foo(a). foo(b). foo(c). Then: ?- foo(X). X = a; X = b; X = c; no foo(X) X=a done X=b done X=c done

26 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). bar(X),baz(X)

27 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). bar(X),baz(X) X=b

28 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). bar(X),baz(X) X=b baz(b)

29 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). bar(X),baz(X) X=b baz(b)

30 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). bar(X),baz(X) X=b baz(b)

31 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). bar(X),baz(X) X=b X=c baz(b)

32 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). bar(X),baz(X) X=b X=c baz(c)baz(b)

33 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). X = c bar(X),baz(X) X=b X=c baz(c)baz(b) done

34 James CheneyLogic ProgrammingSeptember 22, 2014 Depth-first search (III) Prolog backtracks to the last choice point if a subgoal fails. Assume: bar(b). bar(c). baz(c). Then: ?- bar(X),baz(X). X = c; no bar(X),baz(X) X=b X=c baz(c)baz(b) done

35 James CheneyLogic ProgrammingSeptember 22, 2014 "Generate and test" Common Prolog programming idiom: find(X) :- generate(X), test(X). where test(X) checks if X is a solution generate(X) searches for solutions Can use to constrain (infinite) search space Can use different generators to get different search strategies besides depth- first

36 James CheneyLogic ProgrammingSeptember 22, 2014 Limitations of depth- first search Recursion needs to be handled carefully to avoid loops Rule order and goal order matter More in next lecture Not complete "in practice" legitimate answers may be missed due to loops

37 James CheneyLogic ProgrammingSeptember 22, 2014 Other search strategies Breadth-first search / iterative deepening Explore all alternatives, interleaved Price: memory overhead Bottom-up (forward chaining) Compute all possible answers derivable from facts & rules Only viable for "Datalog" programs with "flat" data (only constants and variables) Supported by commercial tools for big data (LogicBlox, DLV)

38 James CheneyLogic ProgrammingSeptember 22, 2014 Next time Recursion Lists, trees, data structures Further reading: LPN ch. 2 Tutorial #1 will be up soon

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