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1 CS 385 Fall 2006 Chapter 15 Introduction to PROLOG.

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1 1 CS 385 Fall 2006 Chapter 15 Introduction to PROLOG

2 2 PROLOG A logic programming language (based on first order predicate calculus) declarative semantics* –father(bob,ted). // the meaning is in the set of clauses and is –father(ted, mary). // independent of the way PROLOG executes them built-in unification Syntax Predicate CalculusProlog  Pnot P P  QP, Q P  QP; Q P → QQ :- P P ≡ QP = Q * These would mean the same if they were reordered

3 3 Syntax (cont) Variables begin with uppercase. Predicate CalculusProlog  X person(X) → mortal(X) mortal(X) :- person(X).  X isa(X, bird) → flies(X) flies(X) :- isa(X, bird). AND:flies(X) :- isa(X, bird), not is(X, emu). flies(X) :- isa(X, bird), not flightless(X). flightless(emu). flightless(ostrich). OR:happy(X) :- healthy(X); wise(X). Better (separate predicates):happy(X) :- healthy(X). happy(X) :- wise(X).

4 4 AMZI Environment Download from 385 site Current version at amzi.com: more elaborate than needed. Apologies to Mac friends Listener: an interactive environment for developing a set of predicates posing queries to be resolved by unification Help on 385 site

5 5 Recursion 1.predecessor(X, Z):- parent(X, Z). 2.predecessor(X, Z):- parent(X, Y), predecessor(Y, Z). 3.parent(ann, bob). 4.parent(bob, connie). How conclude predecessor(ann, connie)? unify rhs of 1 and 3 to conclude 5. predecessor(ann, bob) unify rhs 2 with 4 and 5 to conclude 6. predecessor(ann, connie). Typical query: predecessor(ann, X).

6 6 family2.pro Given predicates for parent and female: parent(pam, bob). female(pam). How would we describe mother? mother(X, Y) :- parent(X, Y), female(X). Sister? sister(X, Y) :- parent(Z, X), parent(Z, Y), female(X), not(X = Y).

7 7 Comparison FEATUREC++PROLOG Language model:procedural programming object-oriented logic programming Data types:integer, float, array, struct, pointer atom, predicate, list Data manipulation:assignment parameter passing variable binding through unification Program control:sequence, branch, loop, recursion pattern-directed search Program structure:blockrules and facts in a database Variable scoping:global and nested local scopes confined to a single rule or fact

8 8 Definitions predecessor is a predicate of arity 2 parent is a predicate of arity 2 predecessor has 2 clauses. They are rules. parent has 3 clauses. They are facts. predecessor(X, Z):- parent(X, Z). predecessor(X, Z):- parent(X, Y), predecessor(Y, Z). parent(ann, bob). parent(bob, connie). parent(joe, sue).

9 9 Commands assertadd a predicate retractremove a predicate consultload a file into the database read to terminal write to terminal see file input (later, maybe) tell file output (later, maybe) listinglist current database Good resource: Adventure in Prolog in the PROLOG documentation

10 10 Lists in PROLOG The major way to represent data enclosed by brackets list elements separated by commas list elements can be lists. E.g: [ ] [liz] [liz, pat, bob] [liz, pat, [bob, harry]] Lists have a head and a tail: [X|Y] E.g. [liz, pat, bob] X = liz //an atom y = [pat, bob] //a list

11 11 Lists (cont) member(X, [X|T]).(1) member(X,[Y|T]) :- member(X,T).(2) Trace member(b, [a,b,c]). call (1). {b/X, [a, b, c]/[X|T] } fail since b ≠a call (2). member(X, [X|T]) unifies to member(b, [a,b,c]) with {b/X, a/Y, [b,c]/T} member(b, [a,b,c])? implied by lhs member(b, [b,c]) member(b, [b,c])? call (1) X = b, [X|T] = [b, c]. yes to call (1) since b = b. yes to call (2)

12 12 Short Version member(b, [a,b,c]) call (1). {b/X, [a, b, c]/[X|T] } fail since b ≠a call (2). member(X, [X|T]) unifies to member(b, [a,b,c]) with {b/X, a/Y, [b,c]/T} member(b, [a,b,c])? implied by lhs member(b, [b,c]) member(b, [b,c])? call (1) X = b, [X|T] = [b, c]. yes to call (1) since b = b. yes to call (2) call (1). fail since b ≠a call (2). b=X, a=Y, [b,c]=T member(b, [b,c])? call (1) X = b, [X|T] = [b, c]. yes to call (1) since b = b. yes to call (2) member(X, [X|T]). (1) member(X,[Y|T]) :- member(X,T). (2)

13 13 Debugger member(b, [a,b,c]) call (1). fail since b ≠a call (2). b=X, a=Y, [b,c]=T member(b, [b,c])? call (1) X = b, [X|T] = [b, c]. yes to call (1) since b = b. yes to call (2) (only tracks paths followed) CALL:(2) member / 2 b [a,b,c] CALL:(1) member / 2 b [b,c] EXIT: member / 2 b [b,c] EXIT: member / 2 b [a,b,c] member(X, [X|T]). (1) member(X,[Y|T]) :- member(X,T). (2)

14 14 With the debugger and a driver test1:- member(b, [a, b, c]). member(X, [X|T]). member(X,[Y|T]) :- member(X,T). (1) test1: (1) member(b, [b|T])? no (2) member[b, [b, c] (1) member(b, [b |[c]]) yes CALL:(1) test1 / 0 CALL:(2) member / 2 b [a,b,c] CALL:(1) member / 2 b [b,c] EXIT: member / 2 b [b,c] EXIT: member / 2 b [a,b,c] EXIT: test1 / 0

15 15 With the Clauses Reversed member(X,[Y|T]) :- member(X,T). (1) member(X, [X|T]). (2) (1) member(b, [a,b,c])? (1) member[b, [b, c] (1) member(b, [c]) (1) member(b, [ ]) fail (can't apply rule 1 or 2) fail (and can't apply rule 1 0r 2) (2) member(b, [b|[c]]) succeeds

16 16 Other Commands test1:- member(b, [a, b, c]). //to save typing write(X) //writes X to the screen nl //writes a newline Example: writelist([ ]). writelist(H|T) :- write(H), nl, writelist(T). How would you reverse a list? reverse_writelist([ ]). reverse_writelist(H|T) :- reverse_writelist(T), nl, write(H).

17 17 Recursive Search: the knights tour Can a knight (chess) visit each square on a board once? Board and legal moves: move(1,6) move(1,8) move(2,7) move(2,9)... move(9,2) Is there a path from one square to another? path(Z,Z). path(X,Y):- move(X,W), path(W,Y). knight0.pro Try path(1,2)

18 18 Trace path(1,2) move(1,6) try path(6,2) move(6,7) try path(7,2) move(7,6) try path(6,2). Whoops. Resolution (knight1.pro): path(X,Y):- move(X,W), not(been(W)), assert(been(W)), path(W,Y). But this adds been(6) to the database. Try path(1,6) twice.

19 19 New definition: path(Z,Z,L). path(X,Y,L):- move(X,Z), not(member(Z, L), path(Z, Y, [Z|L]). Trace path(1,7, [1]). Text has a longer one. Try yourself. How does the result get printed?

20 20 Prolog, Take 2 What have we done so far –simple logical inference (am I my own grandfather?) –production systems e.g. knight’s tour and tea ceremony There are many other ways to control the search –cut (!) to prevent backtracking beyond a certain point –could bog down here

21 21 Cut! A goal with no arguments that –succeeds the first time encountered –fails each other time family3.pro: grandparent(X, Y) :-// all possible pairs parent(X, Z), parent(Z, Y). grandparent(X, Y) :- // X =pam, Z = pat parent(X, Z), !, // X =pam, Z = pat parent(Z, Y). grandparent(X, Y) :- // X= pam, Z = ann parent(X, Z), parent(Z, Y), !.

22 22 ADT Stack stack(Top, Stack, [Top|Stack]) To push an element onto the stack, specify Top and Rest and use S ?- stack(a, [b, c, d], S). returns S = [a, b, c, d] To pop an element from the stack specify S and use Top and Rest ?- stack(Top, Rest, [a, b, c, d]). returns Top = a Rest = [b,c,d] To peek at the top element on the stack: ?- stack(Top, _, [a, b, c, d]). returns Top = a Exercise: build functions for write_bottom and to test whether the top element is repeated in the stack.

23 23 The farmer, wolf, cabbage, and goat Problem: They are all on one side of the river The wolf will eat the goat The goat will eat the cabbage The boat carries two things at once Objective: get them all across the river safely The state: state(F,W,G,C) F,W,G,C = e or w. Where is the boat? (with the farmer) Start state: state (w,w,w,w) Goal state: state (e, e, e, e )

24 24 Figure 14.1: Sample crossings for the farmer, wolf, goat, and cabbage problem.

25 25 State Space Graph: start: (w, w, w, w) (e, e, w, w) (e, w, e, w) (e, w, w, e) (e, w, w, w) (w, w, e, w) (w, w, w, e) (w, e, w, w) goal: (e, e, e, e) (underline = unsafe)

26 26 How to do this in Prolog? Ideas we want to capture: 1.move (state1, state2) –the farmer will be on opposite sides in state1 and 2 –state 2 needs to be safe 2.unsafe: –wolf and goat are together without the farmer –goat and cabbage are together without the farmer 3.opposite sides: –opp(e,w). –opp(w,e). unsafe(state(X, Y, Y, C):- opp(X,Y). unsafe(state(X, W, Y, Y):- opp(X,Y). % much easier than with the graph

27 27 Prolog Generate rules for moving the farmer/wolf/goat/cabbage E.g. the farmer moves the wolf: move(state(X,X,G,C), state(Y,Y,G,C)):- opp(X,Y), not(unsafe(state(Y,Y,G,C))), writelist(['try farmer takes wolf ', Y, Y, G, C]), nl. How do we find a path through the graph? Keep track of where you have been on a stack, Been_stack (text) a list (simpler version) Backtracking: pop from the stack remove 1st element on list

28 28 Search Strategies (15.4) Depth first search text uses a stack alternate: a (closed) list the open list is handled by PROLOG backtracking Breadth first search what's different? a queue add all children to the queue when a move is made Best first search a priority queue

29 29 Missionaries and Cannibals Three missionaries and three cannibals come to a river. They wish to cross. There is a boat that will hold only two. Any of the group is able to row. If there are more missionaries than cannibals on any side of the river, the cannibals will be converted. Get all people across the river with no conversions. States? state(M, C, B) Safe? safe(state(M, C, 0)) :- M =

30 30 NLP (15.9 ) sentence(Start, End):- nounphrase(Start, Rest), verbphrase(Rest, End). Tested by utterance(X):- sentence(X, [ ]). This works by examining the first part of the sentence trying to make a nounphrase from pieces of it passing the rest to make a verbphrase. expecting End to be empty when done utterance([man, bites]): sentence([ man, bites],[]) % Start = [man, bites] nounphrase(Start, Rest), % Start = [man], Rest = [bites] verbphrase(Rest, End). % Rest = [bites], End = []

31 31 Using nounphrase sentence(Start, End):- %1 nounphrase(Start, Rest), %2 verbphrase(Rest, End). nounphrase([Noun|End], End) :- %3 noun(Noun). noun(man). %4 verb(bites) %5 sentence([ man, bites], [ ]) %1 Start = [man, bites] nounphrase( [man, bites], ??) %2 Start = [man, bites] Rest=?? nounphrase( [man | bites], bites) %3 Start = [man, bites] →[Noun|End] = [man | bites] noun(man) %4 [End]→[bites] verbphrase([bites], [ ]) %... suceeds because of verb(bites)

32 32 More NLP Try: utterance([dog, bites, X]).% will give all possible ways to finish it. utterance(X). % will give all possible sentences. Curiosity: test2:-utterance([dog, X]). % returns yes utterance([dog, X]) % returns values for X

33 33 Context-free versus Context-sensitive Context free the rules do not depend on the rest of the sentence Context sensitive to account for noun-verb agreement. (noun singular ↔ verb singular). add singular or plural to each noun and verb. require that the nounphrase and verbphrase agree. see nlpcs.pro.


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