1. CS 3304 Comparative Languages
Lecture 21: Logic Languages – Perspective
3 April 2012

2. Extended Example: Tic-Tac-Toe
Ordering allows the programmer to indicate that certain resolutions are preferred and should be considered before other “fallback” options.
Tic-tac-toe example - a game played by two players on a 3x3 grid of squares:
Two players, X and O, take turns placing markers in empty squares.
A player wins when placing three markers in a row, horizontally, vertically, or diagonally.
Uses a built-in predicate \+ that succeeds if its argument (a goal) cannot be proven.

3. Squares in a Row
ordered_line(1,2,3). ordered_line(4,5,6). ordered_line(7,8,9). ordered_line(1,4,7). ordered_line(2,5,8). ordered_line(3,6,9). ordered_line(1,5,9). ordered_line(3,5,7). line(A,B,C) :- ordered_line(A,B,C). line(A,B,C) :- ordered_line(A,C,B). line(A,B,C) :- ordered_line(B,A,C). line(A,B,C) :- ordered_line(B,C,A). line(A,B,C) :- ordered_line(C,A,B). line(A,B,C) :- ordered_line(C,B,A). 1 2 3 4 5 6 7 8 9

4. Moves
move(A) :- good(A),empty(A). full(A) :- x(A). full(A) :- o(A). empty(A) :- \+full(A). %strategy: good(A) :- win(A). good(A) :- block_win(A). good(A) :- split(A). good(A) :- strong_build(A). good(A) :- weak_build(A). O X

5. Strategies
win(A) :- x(B),x(C),line(A,B,C). block_win(A) :- o(B),o(C),line(A,B,C). split(A) :- x(B),x(C),different(B,C), line(A,B,D),line(A,C,E),empty(D),empty(E). same(A,A). different(A,B) :- \+(same(A,B)). strong_build(A) :- x(B),line(A,B,C),empty(C), \+(risky(C)). risky(C) :- o(D),line(C,D,E),empty(E). weak_build(A) :- x(B),line(A,B,C),empty(C), \+(double_risky(C)). double_risky(C) :- o(D),o(E),different(D,E), line(C,D,F),line(C,E,G),empty(F),empty(G).

6. Default Pick
good(5). good(1). good(3). good(7). good(9). good(2). good(4). good(6). good(8). 1 2 3 4 5 6 7 8 9

7. Imperative Control Flow
Cut - a zero-argument predicate ! (exclamation point):
Always succeeds.
Side effect: commits the interpreter to whatever choices have been made since unifying the parent goal with the left hand side of the current rule.
Example - list membership:
No cut: member(X, [X | _]). member(X, [_ | T]) :- member(X, T).
Cut: member(X, [X | _) :- !. member(X, [_ | T]) :- member(X, T).
Alternative: member(X, [X | _). member(X, [H | T]) :- X \= H, member(X, T).
X \= H means X and H will not unify.

8. Cut Implementing \+: \+(P) :- call(P), !, fail. \+(P).
The call predicate takes a term as argument and attempts to satisfy it as a goal.
The fail predicate always fail.
It is possible to replace all ! with \+, i.e. confine ! within \+:
Often makes it easier to read.
Often makes it less efficient.
Used to create the effect of if...then...else: statement :- condition, !, then_part. statement :- else_part.

9. Loops
The fail predicate can be used in conjunction with a “generator” to implement a loop.
Example generator: append([], A, A). append([H | T], A, [H | L]) :- append(T, A, L).
Example loop: print_partitions(L) :- append(A, B, L), write(A), write(' '), write(B), nl, fail. print_partitions(_).
Output: [] [a, b, c] [a] [b, c] [a, b] [c] [a, b, c] [] Yes

10. Generate-and-Test
The programming idiom - an unbounded generator with a test-cut terminator:
Example generator: natural(1). natural(N) :- natural(M, N is M+1.
Example test-cut: my_loop(N) :- natural(I), write(I), nl, I = N, !.
Generally used in conjunction with side effects:
Modification of the program database.
I/O:
Output: write, nl, put.
Input: read, get, consult, reconsult: get(X) :- repeat, get0(X), X >=32, !.

11. Database Manipulation
Prolog is homoiconic: it can represent itself.
It can also modify itself.
Add clause with the built-in predicate assert.
Remove clause with the built-in predicates retract and retractall.
clause predicate attempts to match its two arguments against the head and body of some existing clause in the database.
Individual terms can be created, or their contents extracted, using the built-in predicates functor, arg, and =.. .
functor(T, F, N) succeeds if and only if T is a term with functor F and arity N.
arg(N, T, A) succeeds if and only if its first two arguments are instantiated, N is a natural number, T is a term, and A is the Nth argument of T.
Infix predicate =.. “equates” a term with a list.

12. Theoretical Foundations
In mathematical logic, a predicate is a function that maps constants (atoms) or variables to the values true and false.
Predicate calculus provides a notation and inference rules for constructing and reasoning about propositions (statements) composed of predicate applications, operators, and the quantifiers ∀ and ∃.
Operators include and (∧), or (∨), not (¬), implication (→), and equivalence (↔).
Quantifiers are used to introduce bound variables in an appended proposition, much as λ introduces variables in the lambda calculus.
The universal quantifier, ∀, indicates that the proposition is true for all values of the variable.
The existential quantifier, ∃, indicates that the proposition is true for at least one value of the variable.
Clausal form provides a unique expression for every preposition.

13. Execution Order
While logic is inherently declarative, most logic languages explore the tree of possible resolutions in deterministic order.
Prolog provides a variety of predicate to control the execution order and to manipulate database explicitly during execution.
An example for declarative way: declarative_sort(L1, L2) :- permutation(L1, L2), sorted(L2). permutation([], []). permutation(L, [H | T]) :- append(P, [H | S], L), append(P, S, W), permutation(W, T).
An example for imperative way: quicksort([], []). quicksort([A | L1], L2) :- partition(A, L1, P1, S1), quicksort(P1, P2), quicksort(S1, S2), append(P2, [A | S2], L2). partition(A, [], [], []). partition(A, [H | T], [H | P], S) :- A >= H, partition(A, T, P, S). partition(A, [H | T], P, [H | S]) :- A =< H, partition(A, T, P, S).
Distinguish between the specification of a program and its implementation.

14. “Closed World” Assumption
Closed world assumption: the database is assumed to contain everything that is true.
When the database doe not have information to prove the query, the query is assumed to be false.
Prolog can prove that a goal is true but it cannot prove that the goal is false.
Assumption: if a goal cannot be proven true, it is false.
Prolog is a true/fail system, not true/false system.
The problem of the closed-world assumption is related to the negation problem.

15. Negation
A collection of Horn clauses does not include any things assumed to be false: purely “positive” logic.
\+ predicate is different from logical negation – it can succeed simply because our current knowledge is insufficient to prove it.
Negation in Prolog occurs outside any implicit existential quantifiers on the right-hand side of the rule:
\+(takes(X, his201)). where X is uninstantiated means: ¬∃X[takes(X, his201)] rather than ∃X[¬takes(X, his201)]
A complete characterization of the values of X for which ¬takes(X, his201) is true would require a complete exploration of the resolution tree something that Prolog does only when all goals fails or when repeatedly prompted with semicolons.

16. 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)
Example:
trace.
distance(chevy, Chevy_Distance).

(1) 1 Call: distanc(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

17. Trace Example
likes(jake,chocolate). likes(jake,apricots). likes(darcie,licorice). likes(darcie,apricots). 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

18. Summary
A logic program is a set of axioms from which the computer attempts to construct a proof.
Unlike imperative (Turing machine) and functional (lambda calculus) languages, Prolog provides limited generality of resolution theorem proving: space/time efficiency tradeoff.
Prolog is homoiconic: programs look like ordinary data structures, and can be created, modified, and executed on the fly.
Imperative features tend to be responsible for more than their share of program bugs but some tasks (e.g., graphical I/O) are almost impossible to accomplish without side effects.