1. CSC411Artificial Intelligence1 Chapter 15 An Introduction to PROLOG Syntax for Predicate Calculus Abstract Data Types (ADTs) in PROLOG A Production System Example in PROLOG Designing Alternative Search Strategies Contents

2. CSC411Artificial Intelligence2 Represent Facts and Rules Facts (propositions): e.g. male(philip). Rules (implications): e.g. parent(X, Y) := father(X, Y). parent(X, Y) := mother(X, Y). parent(X, Y) := father(X, Y); mother(X, Y). Questions (start point of execution): e.g. ?-parent(X, Y).

3. CSC411Artificial Intelligence3 Prolog as Logic Horn clause – a subset of first-order logic Logic formula: –Description – A – e.g. philip is male. –Logic OR – A  B : A; B. –Logic AND – A  B : A, B. –Logic NOT -- A : not A. –Logic implication: A  B : B :- A. Each expression terminates with a. Deductive inference – Modus ponents A A  B --------B

4. CSC411Artificial Intelligence4 A Simple Rule English description: If X is male, F is the father of X, M is the mother of X, F is the father of Y, M is the mother of Y Then X is a brother of Y Logic formula: male(X)  father(F, X)  mother(M, X)  father(F, Y)  mother(M, Y)  brother(X, Y) Prolog: brother(X, Y) :- male(X), father(F,X), mother(M,X), father(F,Y), mother(M, Y).

5. CSC411Artificial Intelligence5 A Simple Program A Prolog program is a sequence of facts and rules brother(X, Y) :- male(X), parent(P, X),parent(P, Y). parent(X, Y):- father(X, Y); mother(X, Y). male(philip). father(bill, philip). ?- brother(philip, jane).

6. CSC411Artificial Intelligence6 Program Execution Start from the question Matching – match the question with facts and rules by substitution (try) Unification – looking for unified solution (consistent solution) Backtracking – once fails, go back to try another case Divide-and-conquer –divide problem into sub problems –solve all sub problems –integrate sub solutions to build the final solution –Solutions to all sub problems must be consistent

7. CSC411Artificial Intelligence7 Program Trace Tree

8. CSC411Artificial Intelligence8 Another Example Facts mother(jane, george). father(john, george). Brother(bill, john). Rules: parent(X, Y) :- mother(X, Y). parent(X, Y) :- father(X, Y). uncle(X, Y) :- parent(P, Y), brother(X, P). Question: ?- uncle(X, george).

9. CSC411Artificial Intelligence9 Program Execution Trace uncle(X, george) parent(P, Y)brother(X, P) mother(X, Y)father(X, Y) brother(bill, john) mother(jane, george)father(john, george) Y/george P/X, Y/george X/jane X/john X/bill, P/john

10. CSC411Artificial Intelligence10 Unification and Backtracking First matching (dashed line): –parent(jane, george)-- P  jane –brother(bill, john)-- P  john which is not consistent. Backtracking (solid line): –parent(john, george) -- P  john –Brother(bill, john)-- P  john which is unified Multiple solutions –Interactive program –Interpreter –Once the program outputs, the user can respond with ; to ask for more solutions –If no more solutions, the program answers “no”

11. CSC411Artificial Intelligence11 Prolog Environment Add new predicates –assert(P) – add new predicate P to database –asserta(P) – add P to the beginning of database –assertz(P) – add P to the end of database Delete predicates –retract(P) – remove P fromdatabase Import database from a file –consult(File) Input/output –read(X) – read a term from the input stream to X –write(X) – put the term X in the output stream –see(File) – open a file for reading (input stream) –tell(File) – open a file for writing (output stream)

12. CSC411Artificial Intelligence12 Prolog Program Execution Tracing List all clauses with the predicate name –listing(P) – regardless of the number of arguments Monitor the program progress –trace – print every goal –exit – when a goal is satisfied –retry – if more matches –fail – enforce fail –notrace – stop the trace Trace Predicates –spy – print all uses of predicates More predicates exist and are version- dependent

13. CSC411Artificial Intelligence13 Recursion Base case – a set of rules for direct solution Normal case – another set of rules for indirection solution E.g. child(X, Y) :- mother(Y, X). child(X, Y) :- father(Y, X). descendant(X, Y) :- child(X, Y).  base case descendent(X, Y) :- child(X, Z), descendent(Z, Y). E.g.Fibnacci number: fib(0) = 0, fib(1) = 1,  base case fib(n) = fib(n-1) + fib(n-2)  normal case fib(n) = fib(n-1) + fib(n-2)  normal case Prolog program: Facts:fib(0, 0). fib(1, 1). Rules:fib(X, N) :- N > 1, N1 is N - 1, N2 is N – 2, fib(Y, N1), fib(Z, N2), X is Y + Z. Question:? :- fib(X, 5). X = 5. ? :- fib(5, N). N = 5. ? :- fib(X, 8). X = 21.

14. CSC411Artificial Intelligence14 Data Representation Atom – numbers, strings, etc. Structured data – pattern –Format: functor(parameter-list) where parameters in the list are either atom or structured data separated with “,” –E.g.person(name) student(person(name), age) male(bill)female(jane) –Special functor dot (.) Represents list:.(p,.pattern) Special.pattern:.()  [], empty list E.g.:.(p,.(q,.(r,.(s,.())))) List:[p, q, r, s] Operations:[X|Y]  pattern –Anonyous variable:underscore (_) –E.g. member(X, [X|_]) :- !. member(X, [_|Y]) :- member(X, Y).

15. CSC411Artificial Intelligence15 Lists A sequence of elements separated by comma and enclosed by [ and ] List elements can be other lists Examples: [1, 2, 3, 4] [a, george, [b, jack], 3] Empty list:[] Non-empty lists consists of two parts –Header – the first element –Tail – the list containing all elements except the first –Can be written [Header|Tail] Example:for list [a, 1, b, 2] Example:for list [a, 1, b, 2] –Header: a –Tail: [1, b, 2] Match: –headers match and tails match –Match [a, 1, b, 2] with [H|T] H = a T = [1, b, 2]

16. CSC411Artificial Intelligence16 List and Recursion Recursively process all elements in a list Usually the base case is the Empty list Normal cases –Process the header –Recursion on the tail Example: membership test member(X, [X|T]). member(X, [_|T]) :- member(X, T). ?- member(a, [a, b, c, d]). Yes;no ?- member(a, [1, [a, b], 2, [c, d]). no ?- member(X, [a, b, c] X = a ; X = b ; X = c ;no

17. CSC411Artificial Intelligence17 More Recursion Examples Write out a list one element to a line writelist([]). writelist([H|T) :- write(H), nl, writelist(T). Write out a list in reverse order Reverse_writelist([]). Reverse_writelist([H|T]) :- reverse_writelist(T), write(H), nl. Append a list to another append([], L, L). append([H|T], L, [H|X]) :- append(T, L, X). Reverse a list reverse([], []). reverse([H|T], X) :- reverse(T, Y), append(Y, [H], X).

18. CSC411Artificial Intelligence18 Count the number of elements Count the number of elements in a list –Base case: if the list is empty, then number of elements is 0 –Normal case: the number of elements in the list is the number of elements in the tail plus 1 –Rules?

19. CSC411Artificial Intelligence19 Count the number of elements Find the number of elements in a list length:-length([], 0) :- !. length([X|Y], N) :- length(Y, M), N is M+1.

20. CSC411Artificial Intelligence20 Recursive Search Depth-first search with backtracking Ordering of facts and rules affect the problem-solving efficiency Search a subgoal: –Top  down: matching facts and heads of rules Decomposition of goals: –Left  right Backtracking: –Matching: Top  down –Subgoals: right  left

21. CSC411Artificial Intelligence21 Closed-world assumption and negation (NOT) Question:?- X. –Assumption: If you prove X then X is true. Otherwise X is false. Question:?- not X. –Assumption: If you can prove not X then not X is true; Otherwise not X is false, meaning X is true. So, if can not prove either X or not X, then ?- X. and ?- not X. both false (true).

22. CSC411Artificial Intelligence22 Stop (cut) Backtracking ! – success once and only once Efficiency – cut backtracking E.g. fib(0, 0) :- !. fib(1, 1) :- !. fib(X, N) :- N1 = N-1, N2=N-2, fib(Y, N1), fib(Z, N2), X=Y+Z. Two uses: –Control the shape of the search tree –Control recursion

23. CSC411Artificial Intelligence23 Abstract Data Types in Prolog ADT –A set of operations –No data structure specified ADT building components –Recursion, list, and pattern matching –List handing and recursive processing are hidden in ADTs Typical ADTs –Stack, Queue, Set, Priority Queue

24. CSC411Artificial Intelligence24 The ADT Stack Characteristics –LIFO (Last-in-first-out) Operations –Empty test –Push an element onto the stack –Pop the top element from the stack –Peek to see the top element –Member_stack to test members –Add_list to add a list of elements to the Stack Implementation –empty_stack([]). –stack(Top, Stack, [Top|Stack]). Push – bind first two elements and free the third element Pop – bind the third element and free the first two Peek – same as Pop but keep the third element –member_stack(Element, Stack) :- member(Element, Stack). –add_list_to_stack(List, Stack, Result):-append(List, Stack, Result).

25. CSC411Artificial Intelligence25 Print a Stack in Reverse Order empty_stack([]). stack(Top, Stack, [Top|Stack]). member_stack(E, Stack) :- member(E, Stack). add_list_to_stack(L, S, R) :- append(L, S, R). reverse_print_stack(S) :- empty_stack(S), !. reverse_print_stack(S) :- stack(H, T, S), reverse_print_stack(T), write(H), nl. ?- reverse_print_stack([1, 2, 3, 4, 5]).

26. CSC411Artificial Intelligence26 The ADT Queue Characteristics –FIFO (First-in-first-out) Operations –Empty test –Enqueue – add an element to the end –Dequeue – remove the first element –Peek – see the first element –Member test –Merge two queues Implementation –empty_queue([]). –enqueue(E, [], [E]). enqueue(E, [H|T], [H|Tnew]) :- enqueue(E, T, Tnew). –dequeue(E, [E|T], T). –dequeue(E, [E|T], _).% peek –member_queue(E, Q) :- member(E, Q). –Add_list_to_queue(L, Q, R) :- append(Q, L, R).

27. CSC411Artificial Intelligence27 The ADT Priority Queue Characteristics –Each element is associated with a priority –Always remove the “smallest” one Operations –Empty test –Member test –Add an element to the priority queue –Min – find the minimum element –Remove the minimum element –Add a list to a priority queue Implementation –empty_pq([]). –member_pq(X, P) :- member(X, P). –insert_pq(E, [], [E]) :- !. insert_pq(E, [H|T], [E, H|T]) :- E < H. insert_pq(E, [H|T], [H|Tnew]) :- insert_pq(E, T, Tnew). –min_pq(E, [E|_]). –remove_min_pq(E, [E|T], T). –add_list_pq([], L. L). add_list_pq([H|T], L, R) :- insert_pq(H, L, L2), add_list_pq(T, L2, Lnew).

28. CSC411Artificial Intelligence28 The ADT Set A collection of elements –No order –No duplicates Operations –Empty test –Member test –Add an element to the set –Remove an element from the set –Union two sets –Intersect two sets –Difference two sets –Subset test –Equality test

29. CSC411Artificial Intelligence29 The Set Implementation empty_set([]). member_set(E, S) :- member(E, S). delete_from_set(E, [], []). delete_from_set(E, [E|T], T) :- !. delete_from_set(E, [H|T], [E|Tnew]) :- delete_from_set(E, T). add_to_set(E, S, S) :- member(E, S), !. add_to_set(E, S, [X|S]). union([], S, S). union([H|T], S, R) :- union(T, S, S2), add_to_set(H, S2, R). subset([], _). subset([H|T], S) :- member(H, S), subset(T, S). intersection([], _, []). intersection([H|T], S, [H|R]) :- member(H, S), intersection(T, S, R), !. intersection([_|T], S, R) :- intersection(T, S, R), !. Set_diff([], _, []). set_diff([H|T], S, R) :- member(H,S), set_diff(T, S, R), !. set_diff([H|T], S, [H|R]) :- set_diff(T, S, R), !. equal_set(S1, S2) :- subset(S1, S2), subset(S2, S1).

30. CSC411Artificial Intelligence30 A Production System in Prolog Farmer, wolf, goat, and cabbage problem –A farmer with his wolf, goat, and cabbage come to the edge of a river they wish to cross. There is a boat at the river’s edge, but, of course, only the farmer can row. The boat also can carry only two things, including the rower, at a time. If the wolf is ever left alone with the goat, the wolf will eat the goat; similarly if the goat is left alone with the cabbage, the goat will eat the cabbage. Devise a sequence of crossings of the river so that all four characters arrives safely on the other side of the river. Representation –state(F, W, G, C) describes the location of Farmer, Wolf, Goat, and Cabbage –Possible locations are e for east bank, w for west bank –Initial state is state(w, w, w, w) –Goal state is state(e, e, e, e) –Predicates opp(X, Y) indicates that X and y are opposite sides of the river –Facts: opp(e, w). opp(w, e).

31. CSC411Artificial Intelligence31 Sample crossings for the farmer, wolf, goat, and cabbage problem.

32. CSC411Artificial Intelligence32 Portion of the state space graph of the farmer, wolf, goat, and cabbage problem, including unsafe states.

33. CSC411Artificial Intelligence33 Unsafe states unsafe(state(X, Y, Y, C)) :- opp(X, Y). unsafe(state(X, W, Y, Y)) :- opp(X, Y). Move rules move(state(X, X, G, C), state(Y, Y, G, C))) :- opp(X, Y), not(unsafe(state(Y, Y, G, C))), writelist([‘farms takes wolf’, Y, Y, G, C]). move(state(X, W, X, C), state(Y, W, Y, C)) :- opp(X, Y), not(unsafe(state(Y, W, Y, C))), writelist([‘farmers takes goat’, Y, W, Y,C]). move(state(X, W, G, X), state(Y, W, G, Y)) :- opp(X, Y), not(unsafe(state(Y, W, G, Y))), writelist(‘farmer takes cabbage’, Y, W, G, Y]). move(state(X, W, G, C), state(Y, W, G, C)) :-opp(X, Y), not(unsafe(state(Y, W, G, C))), writelist([‘farmer takes self’, Y, W, G, C]). move(state(F, W, G, C), state(F, W, G, C)) :- writelist([‘Backtrack from ‘, F, W, G, C]), fail. Production Rules in Prolog

34. CSC411Artificial Intelligence34 Path rules path(Goal, Goal, Stack) :- write(‘Solution Path Is: ‘), nl, reverse_print_stack(Stack). path(State, Goal, Stack) :- move(State, Next), not(member_stack(Next, Stack)), stack(Next, Stack, NewStack), path(Next, Goal, NewStack), !. Start rule go(Start, Goal) :- empty_stack(EmptyStack), stack(Start, EmptyStack, Stack), path(Start, Goal, Stack). Question ?- go(state(w, w, w, w), state(e, e, e, e) Production Rules in Prolog