Download presentation

Presentation is loading. Please wait.

Published byMitchell Skinner Modified over 2 years ago

1
META-INTERPRETERS AND META-PROGRAMMING Ivan Bratko Faculty of Computer and Info. Sc. Univ. of Ljubljana

2
BASIC META-INTERPRETER % The basic Prolog meta-interpreter prove( true). prove( ( Goal1, Goal2)) :- prove( Goal1), prove( Goal2). prove( Goal) :- clause( Goal, Body), prove( Body).

3
SIMPLE TRACING META-INTERPRETER prove( true) :- !. prove( ( Goal1, Goal2)) :- !, prove( Goal1), prove( Goal2). prove( Goal) :- write( 'Call: '), write( Goal), nl, clause( Goal, Body), prove( Body), write( 'Exit: '), write( Goal), nl.

4
TRACING META-INTERPRETER prove( true) :- !. prove( ( Goal1, Goal2)) :- !, prove( Goal1), prove( Goal2). prove( Goal) :- write( 'Call: '), write( Goal), nl, clause( Goal, Body), prove( Body), write( 'Exit: '), write( Goal), nl.

5
TRACING META-INTERPRETER WITH RETRY % trace( Goal): execute Prolog goal Goal displaying trace information trace( Goal) :- trace( Goal, 0). trace( true, Depth) :- !. % Red cut; Dept = depth of call trace( ( Goal1, Goal2), Depth) :- !, % Red cut trace( Goal1, Depth), trace( Goal2, Depth).

6
trace( Goal, Depth) :- display( 'Call: ', Goal, Depth), clause( Goal, Body), Depth1 is Depth + 1, trace( Body, Depth1), display( 'Exit: ', Goal, Depth), display_redo( Goal, Depth). trace( Goal, Depth) :- % All alternatives exhausted display( 'Fail: ', Goal, Depth), fail.

7
display( Message, Goal, Depth) :- tab( Depth), write( Message), write( Goal), nl. display_redo( Goal, Depth) :- true % First succeed simply ; display( 'Redo: ', Goal, Depth), % Then announce backtracking fail. % Force backtracking

8
METAINTERPRETER FOR PROLOG WITH CONSTRAINTS solve( Goal) :- solve( Goal, [ ], Constr). % Start with empty constr. % solve( Goal, InputConstraints, OutputConstraints) solve( true, Constr0, Constr0). solve( (G1, G2), Constr0, Constr) :- solve( G1, Constr0, Constr1), solve( G2, Constr1, Constr).

9
METAINTERPRETER CTD. solve( G, Constr0, Constr) :- prolog_goal( G), % G a Prolog goal clause( G, Body), % A clause about G solve( Body, Constr0, Constr). solve( G, Constr0, Constr) :- constraint_goal( G), % G a constraint merge_constraints( Constr0, G, Constr).

10
MERGE CONSTRAINTS Predicate merge_constraints: constraint-specific problem solver, merges old and new constraints, tries to satisfy or simplify them For example, two constraints X 3 and X 2 are simplified into constraint X 2.

11
GENERATING PROOF TREES % Prolog meta-interpreter that generates a proof tree :- op( 500, xfy, <== ). % prove( Goal, ProofTree) prove( true, true) :- !. prove( ( Goal1, Goal2), (Proof1, Proof2)) :- !, prove( Goal1, Proof1), prove( Goal2, Proof2). prove( Goal, Goal <== Proof) :- clause( Goal, Body), prove( Body, Proof ).

12
EXPLANATION-BASED GENERALISATION EBG = Machine learning from one example only! Lack of examples compensated by domain theory Given: Domain theory (can answer any question) Operationality criteria Training example Find: Generalisation of training example + Operational definition of target concept (in terms of operational predicates) Note: EBG can be viewed as a program compilation technique Inefficient program (domain theory) Efficient specialised program

13
A DOMAIN THEORY ABOUT GIFTS % Figure 23.3 Two problem definitions for explanation-based generalization. % For compatibility with some Prologs the following predicates % are defined as dynamic: :- dynamic gives/3, would_please/2, would_comfort/2, feels_sorry_for/2, likes/2, needs/2, sad/1, go/3, move/2, move_list/2.

14
% A domain theory: about gifts gives( Person1, Person2, Gift) :- likes( Person1, Person2), would_please( Gift, Person2). gives( Person1, Person2, Gift) :- feels_sorry_for( Person1, Person2), would_comfort( Gift, Person2).

15
would_please( Gift, Person) :- needs( Person, Gift). would_comfort( Gift, Person) :- likes( Person, Gift). feels_sorry_for( Person1, Person2) :- likes( Person1, Person2), sad( Person2). feels_sorry_for( Person, Person) :- sad( Person).

16
% Operational predicates operational( likes( _, _)). operational( needs( _, _)). operational( sad( _)). % An example situation likes( john, annie). likes( annie, john). likes( john, chocolate). needs( annie, tennis_racket). sad( john).

17
THEORY ABOUT LIFTS % Another domain theory: about lift movement % go( Level, GoalLevel, Moves) if % list of moves Moves brings lift from Level to GoalLevel go( Level, GoalLevel, Moves) :- move_list( Moves, Distance), % A move list and distance travelled Distance =:= GoalLevel - Level. move_list( [], 0). move_list( [Move1 | Moves], Distance + Distance1) :- move_list( Moves, Distance), move( Move1, Distance1). move( up, 1). move( down, -1). operational( A =:= B).

18
LIFTS % Another domain theory: about lift movement % go( Level, GoalLevel, Moves) if % list of moves Moves brings lift from Level to GoalLevel go( Level, GoalLevel, Moves) :- move_list( Moves, Distance), % A move list and distance travelled Distance =:= GoalLevel - Level. move_list( [], 0). move_list( [Move1 | Moves], Distance + Distance1) :- move_list( Moves, Distance), move( Move1, Distance1). move( up, 1). move( down, -1). operational( A =:= B).

19
GIFTS THEORY gives( P1, P2, G) likes(P1,P2) would_please(G,P2) feels_sorry_for(P1,P2) would_comfort(G,P2) P1=P2 needs( P2, G) likes(P1,P2) sad(P2) sad(P1) likes(P,G)

20
Training example gives( P1, P2, G) likes(P1,P2) would_please(G,P2) feels_sorry_for(P1,P2) would_comfort(G,P2) P1=P2 needs( P2, G) likes(P1,P2) sad(P2) sad(P1) likes(P,G) gives(john,john,chocolate)

21
EBG AS PROLOG META-INTERPRETER % ebg( Goal, GeneralizedGoal, SufficientCondition) if % SufficientCondition in terms of operational predicates % guarantees that generalization of Goal, GeneralizedGoal, is true. % GeneralizedGoal must not be a variable

22
ebg( true, true, true) :- !. ebg( Goal, GenGoal, GenGoal) :- operational( GenGoal), call( Goal). ebg( (Goal1,Goal2), (Gen1,Gen2), Cond) :- !, ebg( Goal1, Gen1, Cond1), ebg( Goal2, Gen2, Cond2), and( Cond1, Cond2, Cond). % Cond = (Cond1,Cond2) simplified ebg( Goal, GenGoal, Cond) :- not operational( Goal), clause( GenGoal, GenBody), copy_term( (GenGoal,GenBody), (Goal,Body)), % Fresh copy of % (GenGoal,GenBody) ebg( Body, GenBody, Cond). EBG, CTD.

23
% and( Cond1, Cond2, Cond) if % Cond is (possibly simplified) conjunction of Cond1 and Cond2 and( true, Cond, Cond) :- !. % (true and Cond) Cond and( Cond, true, Cond) :- !. % (Cond and true) Cond and( Cond1, Cond2, ( Cond1, Cond2)).

24
WHY: copy_term( (GenGoal,GenBody), (Goal,Body)) Proofs of Goal and GenGoal follow the same structure They both must use the same program clause: GenGoal :- GenBody. This is checked by matching: (Goal :- Body) = (GenGoal :- GenBody) But, this check must be done without changing GenBody Therefore: copy_term( (GG,GB),...) produces a copy (GG’,GB’), and this is matched: (GG’,GB’) = (Goal,Body)

25
EXPLANATION-BASED GENERALISATION What is the logical relation between generalised goal GENGOAL and derived operational condition COND? What is the role of the example in EBG? What is the difference between EBG and goal unfolding?

26
ABDUCTIVE REASONING Types of logical reasoning: deduction induction abduction Abduction useful for explanation, construction, planning, e.g.: patient’s symptoms systems failures Disease D causes symptom S; patient P has symptom S; So, infer that P has D Abduction considered as unsound rule of inference

27
FORMALLY Given a Theory and an Observation Find an Explanation, such that Theory & Explanation |== Observation Compare with definition of ILP: given BK and Examples, find Hypothesis such that: BK & Hypothesis |-- Examples Discuss differences between abductive and inductive reasoning

28
% An example from Missiaen, Bruynooghe, Denecker 92 :- dynamic faulty/1, lamp/1, current_break/1. % Needed in SICStus by clause/2 faulty( L) :- % Device L faulty - doesn't work lamp( L), % Device is a lamp broken( L). % Lamp broken faulty( L) :- lamp( L), current_break( L). % No electric current in lamp current_break( L) :- fuse( L, F), % Fuse F connected to lamp L melted( F). % Fuse F is melted current_break( L) :- general_power_failure. lamp( a). lamp( b). abducible( broken( Device)). abducible( fuse( Device, Fuse)). abducible( melted( Fuse)). abducible( general_power_failure).

29
ABDUCING METAINTERPRETER % abduce( Goal, Delta): Delta is a list of abduced literals abduce( Goal, Delta) :- abduce( Goal, [ ], Delta). % abduce( Goal, Delta0, Delta): % Delta 0 is "accumulator" variable with Delta as its final value abduce( true, Delta, Delta) :- !. abduce( ( Goal1, Goal2), Delta0, Delta) :- !, abduce( Goal1, Delta0, Delta1), abduce( Goal2, Delta1, Delta). abduce( Goal, Delta0, Delta) :- clause( Goal, Body), abduce( Body, Delta0, Delta).

30
ABDUCING METAINTERPRETER, CTD. % Now abduction reasoning steps: abduce( Goal, Delta, Delta) :- member( Goal, Delta). % Already abduced abduce( Goal, Delta, [Goal | Delta]) :- abducible( Goal), retract( index( I)), !, % Lowest free index for new constants numbervars( Goal, I, J), % Replace variables by Skolem constants asserta( index( J)). % Next free index

31
ABDUCTION, AUX. PREDICATES :- dynamic index/1. index( 1). member( X, [X | L]). member( X, [Y | L]) :- member( X, L).

32
ABDUCTION, AUX. PREDICATES USUALLY PROLOG BUILT-IN numbervars( Term, N0, N) :- var( Term), !, Term = var/N0, N is N0 + 1 ; atomic( Term), !, N = N0 ; Term =.. [Fun | Args], numberargs( Args, N0, N). numberargs( [], N, N). numberargs( [ Arg | Args], N0, N) :- numbervars( Arg, N0, N1), numberargs( Args, N1, N).

33
PATTERN-DIRECTED SYSTEMS Set of program modules executed in parallel Module’s execution triggered by patterns in their data environment Similar to Blackboard Systems Data environment

34
EXAMPLE Greatest common divisor D of A and B: while A and B not equal, do if A > B replace A := A - B else replace B := B - A D = A (or B) Pattern directed modules ConditionPart ---> ActionPart [ Cond1, Cond2,...] ---> [ Action1, Action2,...]

35
PATTERN-DIRECTED PROGRAM FOR GCD :- op( 800, xfx, --->). :- op( 300, fx, num). [ num X, num Y, X > Y ] ---> [ NewX is X - Y, replace( num X, num NewX) ]. [ num X] ---> [ write( X), stop ]. % An initial database num 25. num 10. num 15. num 30.

36
TRACE OF THIS PROGRAM Data environment initially contains: 25 10 15 30 Program works for any number of numbers! Trace execution here

37
% A small interpreter for pattern-directed programs % The system's database is manipulated through assert/retract :- op( 800, xfx, --->). % run: execute production rules of the form % Condition ---> Action until action `stop' is triggered run :- Condition ---> Action, % A production rule test( Condition), % Precondition satisfied? execute( Action).

38
% test( [ Condition1, Condition2,...]) if all conditions true test( []). % Empty condition test( [First|Rest]) :- % Test conjunctive condition call( First), test( Rest). % execute( [ Action1, Action2,...]): execute list of actions execute( [ stop]) :- !. % Stop execution execute( []) :- % Empty action (execution cycle completed) run. % Continue with next execution cycle execute( [First | Rest]) :- call( First), execute( Rest). replace( A, B) :- % Replace A with B in database retract( A), !, % Retract once only assert( B).

Similar presentations

OK

Logic Programming Lecture 8: Term manipulation & Meta-programming.

Logic Programming Lecture 8: Term manipulation & Meta-programming.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on power system analysis Ppt on file system in unix shell Ppt on uses of soil Ppt on do's and don'ts of group discussion strategies Ppt on power system transients A ppt on right to education Download ppt on social problems in india Ppt on needle stick injury product Animated ppt on solar system Ppt on ministry of corporate affairs govt