1. Declarative Programming Autumn 2014 Basic syntax and sample programs

2. Syntax of terms Term Constant Variable Structure AtomNumber alpha17 gross_pay john_smith dyspepsia + =/= ’12Q&A’ 0 1 57 1.618 2.04e-27 -13.6 likes(john, mary) book(dickens, Z, cricket) f(x) [1, 3, g(a), 7, 9] -(+(15, 17), t) 15 + 17 - t X Gross_pay Diagnosis _257 _ Names an individual Stands for an individual unable to be named when program is written Names an individual that has parts

3. Symbols used Uppercase letters A,B,C,...,Z Lowercase letters a,b,c,...,z Digits 0,1,2,...,9 Special symbols +,–,*,/,\,,=,:,.,,,&,_,~,[,],(,),... (there are some that may not be allowed - e.g. %)

4. Atoms string of letters, digits and _, starting with a lowercase letter string of special symbols string of any symbols, delimited by ‘

5. Numbers Depend from implementation... Some examples: 0 1 57 1.618 2.04e-27 -13.6

6. Variables string of letters, digits and _, starting with an uppercase letter or _ variable _ is called anonymous variable and has a special semantic meaning

7. Structures parents(spot, fido, rover) The parents of Spot are Fido and Rover. Functor (an atom) of arity 3. components (any terms) It is possible to depict the term as a tree: parents roverfidospot

8. Structures =/=(15+X, (0*a)+(2<<5)) Some atoms have built-in operator declarations so they may be written in a syntactically convenient form. The meaning is not affected. This example looks like an arithmetic expression, but might not be. It is just a term. << 2 + a * 0 X + 15 =/= 5

9. More about operators Any atom may be designated an operator. The only purpose is for convenience; the only effect is how the term containing the atom is parsed. Operators are ‘syntactic sugar’. Operators have three properties: position, precedence and associativity.

10. Examples of operator properties PositionOperator SyntaxNormal Syntax Prefix:-2-(2) Infix:5+17+(17,5) Postfix:N!!(N) Associativity: left, right, none. X+Y+Z is parsed as (X+Y)+Z because addition is left-associative. Precedence: an integer. X+Y*Z is parsed as X+(Y*Z) because multiplication has higher precedence.

11. The last point about structures… Constants are structures of arity 0. badger means the same as badger()

12. Prolog programs Programs consist of procedures. Procedures consist of clauses. Each clause is a fact or a rule. Programs are executed by posing queries. An example…

13. Example elephant(george). elephant(mary). elephant(X) :- grey(X), mammal(X), hasTrunk(X). Procedure for elephant Predicate Clauses Rule Facts

14. Example ?- elephant(george). yes ?- elephant(jane). no Queries Replies

15. Clauses: Facts and Rules Head :- Body.This is a rule. Head.This is a fact. ‘if’ ‘provided that’ Full stop at the end.

16. Body of a (rule) clause contains goals likes(mary, X) :- human(X), honest(X). Head Body Goals

17. Clauses can be given a declarative reading or a procedural reading. H :- G 1, G 2, …, G n. “That H is provable follows from goals G 1, G 2, …, G n being provable.” Declarative reading: Procedural reading: Form of clause: Interpretation of clauses “To execute procedure H, the procedures called by goals G 1, G 2, …, G n are executed first.”

18. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat).

19. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat). ?-likes(john,mary).

20. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat). ?-likes(john,mary). yes

21. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat). ?-likes(jane,cat). yes

22. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat). ?-likes(julie,cat). no

23. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat). ?-likes(jane,X). X = icecream ? yes

24. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat). ?-likes(jane,X). X = icecream ? ; X = cat ? ; no

25. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat). ?-likes(X,Y),likes(Y,X). X = john, Y = julie ? yes

26. Sample program 1 likes(john,mary). likes(john,julie). likes(julie,john). likes(peter,jane). likes(jane,icecream). likes(jane,cat). friends(X,Y) :- likes(X,Y),likes(Y,X). ?-friends(X,Y). X = john, Y = julie ? yes

27. Sample program 2 parent(john,peter). parent(christine,peter). parent(jane,peter). parent(john,mary). parent(christine,mary). parent(jane,mary). parent(mary,frances). parent(mary,george). parent(frances,edward). parent(edward,isabel).

28. Sample program 2 parent(john,peter). parent(christine,peter). parent(jane,peter). parent(john,mary). parent(christine,mary). parent(jane,mary). parent(mary,frances). parent(mary,george). parent(frances,edward). parent(edward,isabel). ?-parent(jane,peter). yes

29. Sample program 2 parent(john,peter). parent(christine,peter). parent(jane,peter). parent(john,mary). parent(christine,mary). parent(jane,mary). parent(mary,frances). parent(mary,george). parent(frances,edward). parent(edward,isabel). siblings(X,Y) :- parent(X,Z),parent(Y,Z). ?-siblings(john,X). X = christine ? yes

30. Sample program 2 parent(john,peter). parent(christine,peter). parent(jane,peter). parent(john,mary). parent(christine,mary). parent(jane,mary). parent(mary,frances). parent(mary,george). parent(frances,edward). parent(edward,isabel). grandparent(X,Y) :- parent(X,Z),parent(Z,Y). ?-grandparent(christine,X). X = frances ? ; X = george ? ; no

31. Sample program 2 parent(john,peter). parent(christine,peter). parent(jane,peter). parent(john,mary). parent(christine,mary). parent(jane,mary). parent(mary,frances). parent(mary,george). parent(frances,edward). parent(edward,isabel). ancestor(X,Y) :- parent(X,Y). ancestor(X,Y) :- parent(X,Z),ancestor(Z,Y). ?-ancestor(christine,isabel). yes

32. Sample program 2 parent(john,peter). parent(christine,peter). parent(jane,peter). parent(john,mary). parent(christine,mary). parent(jane,mary). parent(mary,frances). parent(mary,george). parent(frances,edward). parent(edward,isabel). ancestor(X,Y) :- parent(X,Y). ancestor(X,Y) :- ancestor(X,Z),parent(Z,Y). ?-ancestor(christine,isabel). yes

33. Sample program 2 parent(john,peter). parent(christine,peter). parent(jane,peter). parent(john,mary). parent(christine,mary). parent(jane,mary). parent(mary,frances). parent(mary,george). parent(frances,edward). parent(edward,isabel). ancestor(X,Y) :- ancestor(X,Z),parent(Z,Y). ancestor(X,Y) :- parent(X,Y). ?-ancestor(christine,isabel).

34. How PROLOG answers queries How to answer a query ?-Q.? find the first fact P. or rule P:-R 1,...,R n. with lhs P unifiable with Q if P corresponds to fact, return yes together with the instantiation values of variables from Q if P corresponds to rule, answer the queries R 1,..,R n (in this order, keeping the instantiated variables from the previous ones) if all queries R1,..,Rn are successful, return yes otherwise find next fact or rule unifiable with Q if there are no more such facts or rules, return no

35. How PROLOG answers queries Goals and sub-goals: A logical sentence to be proved: succeed (satisfy) or fail. A goal could be a query or the conditions of a rule: the relationship between queries and rules Rule: “goal:-goal”, the relationship is “true”, but the truth values of condition and conclusion are unknown Prolog breaks a complex goal into a sequence of sub-goals to evaluate the truth value of a (complex) goal

36. How PROLOG answers queries Searching, Matching (unifying) and backtracking S-M-B is the way in which goals are established A goal is always broken into ground sub-goals consisting of individual predicates, which are then searched from left to right with due regard for the logical meaning. ?-a,b;c,d. Searching: systematically (depth-first) searches the knowledge to find a match for a ground sub-goal.