Other Visual Programming Paradigms

1. Other Visual Programming Paradigms • In the remainder of the “programming paradigms section” we will look at • Visual Logic Programming • Programming with Relations (Database Programming) • Basic Structure of Prolog: classical (textual) logic programming • Execution Model of Prolog • Visualizations of Prolog-Variants: • (TPM, MPL, Heterogenenous Visual Logic Programming, • Predicates and Pixels) • Visual Functional Programming • Clarity • A Functional (set-based) View of Visual Logic Programming • Visual Concurrent Constraint Programming • Pictorial Janus and Toontalk

2. Rule-based Visual Programming • Universal Programming Paradigms • Visual Logic Programming (Visual Variants of Prolog, TPM etc.) • Visual Constraint Programming (Pictorial Janus, Toontalk) • Programming Pradigms for Programming with Pictures, particularly visual Simulations • Production-system languages pixel-based: BitPict high-level image based: Agentsheets (http://www.agentsheets.com) Visual production systems generally have the form SearchPicture => Modification This is interpreted as “if you find the picture SearchPicture in the current picture then apply the right-hand side modification to the current picture”

3. Visual Production Systems • used for implementing computations that directly operate on pictures. • Mainly experimental research vehicle. Only real-world application: Visual Simulation / Creating AnimationExample: • BITPICT, a system for reasoning purely by diagram transformation. • (For more information visit http://www.si.umich.edu/~furnas/) The given rules (right) count the number of trees in a “tangled forrest” by reducing each tree to a single dot.

4. Visual Logic Programming Experimental visual logic programming paradigm languages include: “Predicates and Pixels" [1], CUBE [2], Pictorial Janus [3], VLP [4], VPP [5], Mpl [6] and picture LP [7]. [1] Ringwood 1989 "Predicates and Pixels”. G. Ringwood New Generation Computing, 7, 1989, pp. 59-70. [2] "The CUBE Language” Marc A. Najork and Simon M. Kaplan. IEEE Workshop on Visual Languages 1991, Kobe, Japan. [3] "Complete Visualizations of Concurrent Programs and their Executions”. Kenneth M. Kahn and Vijay A. Saraswat. IEEE Workshop on Visual Languages 1990, Skokie, Illinois. [4] "VLP: A Visual Logic Programming Language”. Dider Ladret and Michel Rueher Journal of Visual Languages and Computing (1991) 2, 163-188. [5] "Visual Logic Programming”. L. F. Pau and H. Olason Journal of Visual Languages and Computing (1991) 2, 3-15. [6] "Mpl - a graphical programming environment for matrix processing”. Ricky Yeung IEEE Workshop of Visual Languages 1988. [7] "Pictures Depicting Pictures: On the Specification of Visual Languages by Visual Grammars”. Bernd Meyer. IEEE Workshop on Visual Languages 1992, Seattle, Washington.

5. Visual Logic Programming • There are three aspects of a visual logic program that can be visualized: • Program Execution (i.e. visualize application of rules): TPM • Used in some commercial prolog implementations, • in particular in some tutoring environments • Data (data structure and/or the database): MPL, “Predicates and Pixels”, etc. • Almost exclusively used in experimental / research languages, • but useful where the data has a natural depiction (e.g. matrices, trees etc.) • Program Structure (i.e. structure of rules): Pictorial Janus, Toontalk, etc. • The most “visually complete” form of programming, can form a good basis for execution visualization (e.g. Pictorial Janus)

6. Programming with Logic and Relations • Relations (Tables) are a universal data representation • Relations are used as the basis of Databases • Prolog is a language for programming with logic & relations Database Programming

7. P1 P2 P3 P4 Relational Structures Example: Web-Sites • Represented as a graph • V = { Page1, Page2, Page3, Page4 } • E = { (Page1, Page2), (Page2, Page1), (Page1, Page4), • (Page2, Page3), (Page3, Page4) } • Represented as relational structure • Links = { (Page1, Page2), (Page2, Page1), (Page1, Page4), • (Page2, Page3), (Page3, Page4) }

8. Representations as Tables Marriage Couple Husband Wife Couple1 Benjamin Stella Couple2 Lisa Scott Couple3 Lydia Tom Children Couple Child Couple1 Roger Couple1 Lisa Couple2 Tom Couple2 Martin --> the basis of relational databases

9. married Couple Husband Wife couple1 benjamin stella couple2 scott lisa couple3 tom lydia child Couple Child couple1 roger couple1 lisa couple2 tom couple2 martin Database Programming In Prolog: married(couple1, benjamin, stella). married(couple2, lisa, scott). married(couple3, tom, lydia). child(couple1, roger). child(couple1, lisa). child(couple2, tom). child(couple2, martin). Queries: “Who is Lisa married to?” ?- married(Couple, Husband, lisa). -> Husband=scott, Couple=couple2 “Who are the Children of Lisa and Scott?” ?- child(couple2, Child). -> Child=tom -> Child=martin

10. Rules • The Idea of deductive databases (and of Prolog) is to specify • explicit data (as relations) • implicit data (via rules) • but not (!) the way in which implicit data is derived. married(couple1, benjamin, stella). married(couple2, lisa, scott). married(couple3, tom, lydia). child(couple1, roger). child(couple2, martin)... father(TheFather, Child) :- married(Couple, TheFather, Mother), child(Couple, Child).

11. Logic Programming The Dream of Declarative Programming - don’t program how to find solution - model the problem and use a universal problem solving procedure to find the solution Algorithm = Logic + Control

12. Procedural Interpretation of Rules Logical deduction is the basis of programming A if B1 and B2 and … Bn Is re-interpreted as to solve A, first solve B1 then B2 then … then Bnor to execute A, first execute B1 then B2 then … then Bn

13. A short history of Prolog Early 1970’s Kowalski’s procedural interpretation of logic: Algorithm = Logic+Control Colmerauer’s implementation of Prolog (Programmation en Logique) Late 1970’s D.H.D. Warren develops Prolog-10 Compiler (written in Prolog) Prolog is not too well received in the US (Prolog / Lisp wars in AI) 1981 starts of Japanese Fifth Generation Project 1980’s Europe and Australia start large Prolog projects (ECRC, Esprit…) Height of “Decductive Databases” Late 1980’s numerous commercial Prolog implementations, Edinburgh standard, Development of Constraint logic Programming Late 1980’s / 1990’s Prolog widely used in AI

14. Prolog Material • Books: • The Art of Prolog, Leon Sterling, Ehud Shaprio. Addison-Wesley, 1986. • Prolog Programming for Artifical Intelligence. I. Bratko. Addison-Wesley, 1990. • Programming in Prolog. W.F. Clocksin, C.S. Mellish. Springer, 1987. • Implementation & Manuals: • Sicstus Prolog • http://www.sics.se • http://www.csse.monash.edu.au/software/sicstus/

15. Syntax • Variable • sequence of alphanumeric characters + “_” • starting with uppercase alphabetic or underscore • Valid: X, Name, BlaBla, Test3, _, _1, _lowercase • Invalid: x, 1X, testing, :bla, $notavar • Constant • sequence of alphanumeric characters + “_” • starting with lowercase alphabetic • numeric sequence • any sequence of characters in single quotes • some special characters (&+-./:;<=>?) • Valid: x, x1, x_1, test7, ‘this is a const’, 8992 • Invalid: NotAConst, “kdhkjf”, _22, 8…9

16. Rules and Predicates (the equivalent of procedures) • Atom: p(T1, …, Tn) • p is an n-ary predicate symbol (constant syntax) • T1 … Tn are terms (variable and constants, more forms later) • Rule / Clause: Head :- Body • head(T1, …, Tk) :- atom1(S1,1, …, S1,n1), …, atomk(Sk,1, Sk,nk). • Example: mother(M,C) :- child(X,C), married(X,P,M). a rule describes “one case” of a predicate • Logic meaning: head is true if body is true • Procedural meaning: to solve head, solve atom1(S1,1, …, S1,n1), then … then atomk(Sk,1, Sk,nk).

17. Rules and Predicates (cont’d) • Fact: person(joe). a predicate with an empty body. • Predicate definition:a set of rules for the same predicate symbol. Interpreted as a disjunction. parent(Person, Parent) :- father(Person, Parent). parent(Person, Parent) :- mother(Person, Parent). • Query: syntactically just a body normally with free variables that are bound in the answer. parent(martin, X). -> the answer is X=scott, X=lisa.

18. Example: Database Programming Which predicates are required to model this?

19. State Example is_state_of(nsw, australia). is_state_of(nt, australia). is_state_of(qld, australia). is_state_of(vic, australia). is_state_of(tas, australia). is_state_of(wa, australia). is_state_of(act, australia). Queries: ?- border(sa,S). ?- is_state_of(S, australia). ?- is_state_of(vic, C). ?- border(vic, X) *** No border(wa,nt). border(wa,sa). border(sa,nt). border(sa,qld). border(sa,vic). border(sa,nsw). border(nt,qld). border(qld,nsw). border(nsw,vic). Rules: border(X,Y) :- border(Y,X)

20. Rules Define Additional “derived” Relations nt qld wa sa nsw vic tas Example of a derived relation: west_of(wa,sa), etc. west_neighbor(X,Y) :- border(X,Y), west_of(X,Y).

21. Execution of Prolog Programs father(charles, philip). father(ana, george). father(philip, tom). mother(charles, ana). parent(Pers, Par) :- father(Pers, Par). parent(Pers, Par) :- mother(Pers, Par). grandparent(Pers, Gpar) :- parent(Pers, Par), parent(Par, Gpar).

22. Simplified Interpreter (for ground goals) • Input: A ground goal G and a program P • Output: yes if G is a consequence of P (“is true in P”), • no otherwise • Initialize resolvent to G • Algorithm: • While (resolvent A1, …, An is not empty) do • choose a goal A from the resolvent • choose a ground instance of a clause • A’ :- B1, …, Bn from P • such that A and A’ are identical • (exit with “no” if no such clause) • replace A by B1, …, Bn in the resolvent • If (resolvent is empty) answer “yes” else answer “no”

23. Multiple Solutions father(charles, philip). father(ana, george). father(philip, tom). mother(charles, ana). parent(Pers, Par) :- father(Pers, Par). parent(Pers, Par) :- mother(Pers, Par). grandparent(Pers, Gpar) :- parent(Pers, Par), parent(Par, Gpar).

24. Backtracking: Example node(a). node(b). node(c). etc. edge(a,b). edge(a,c). edge(b,c). etc. neighbors(Node1, Node2) :- edge(Node1, Node2). neighbors(Node1, Node2) :- edge(Node2, Node1).

25. neighbors(Node1, Node2) :- edge(Node1, Node2).neighbors(Node1, Node2) :- edge(Node2, Node1).

26. Execution Visualization in VPL Mostly based on tree-representations of the derivation structure similar to those we have used when analyzing Prolog derivations. The standard are And/Or-Trees, in which the branches to children denote either disjunctions of conjunctive subgoals, corresponding to the different bodies of a predicate.. A more elaborate visualization is used in the “Transparent Prolog Machine” (TPM), a system for teaching Prolog. These diagrams are called Aorta diagrams: However, visualization of execution is closer to algorithm animation and visual debugging than to visual programming, since the actual programming process is completely textual For detailed information on the TPM see The Transparent Prolog Machine : Visualizing Logic Programs by Marc Eisenstadt, Mike Brayshaw, Jocelyn Paine Kluwer Academic Publishers, 1992.

27. Aorta Diagrams in the TPM (And / OR Trees, Augmented) The basic unit is the procedure status box goal status: fail (cross) / succeed (tick) / processing (?) / exhausted (cross, tick) = finally failed on backtracking

28. Dynamics of Procedure Status Box Status boxes during executionof eats(X, rubbish) for the program eats(joe, hamburgers). eats(fred, X). eats(X, bread) Watch how the clause counter is incremented and the status of the separate clauses are indicated. The annotation indicates the parameters unification (see below)

29. Search Space Display in AORTA The clause boxes c1/c2 indicate the disjunction, The connecting lines for the subgoals indicate conjunction. fun(X) :- red(X), car(X). fun(X) :- blue(X), bike(X). red(apple1). red(block1). red(car1). car(honda1). car(toyota2). blue(flower1). blue(glass1). blue(honda3). bike(myBike). bike(yourBike). bike(honda3)

30. Full AORTA Execution Example Note the execution timestamps given by the small italics and the argument passing arrows older(X,Y) :- age(X, AgeOfX), age(Y, AgeOfY), AgeOfX > AgeOfY. age(john, 27). age(tom, 18). age(sue, 24).