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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 1 Inductive Logic Programming

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 2 Content Introduction to ILP Basic ILP techniques An overview of the different ILP systems The application field of ILP Summary

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 3 Introduction to ILP Inductive logic programming (ILP) = Inductive concept learning (I) Logic Programming (LP) Goal: Develop a theoretical framework for induction Build practical algorithms for inductive learning of relational concepts described in the form of logic programs Background: ILP theory based on the theory of LP ILP algorithms based on experimental and application oriented ML research Motivation: Use of an expressive representational formalism as proportional logic Use background knowledge in learning (in AI the use of domain knowledge is essential for achieving intelligent behaviour)

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 4 Introduction to ILP 2 Inductive learning with background knowledge: Given a set of training examples E and background knowledge B find a hypothesis H, expressed in some concept description language L, such that H is complete and consistent with respect to the background knowledge B and the examples E A hypothesis H is complete with regard to the background knowledge B and examples E if it covers all the positive examples i.e., if A hypothesis H is consistent with respect to the background knowledge B and examples E if it covers none of the negative examples i.e.,

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 5 Introduction to ILP 3

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 6 Introduction to ILP 4 Example: The task is to define the target relation daughter(X,Y) Background knowledge consists of ground facts about the predicates female(X) and parent(Y,X): parent(ann, mary)female(ann) parent(ann, tom)female(marry) parent(tom, eve)female(eve) parent(tom, ian) Training examples: +: daughter(marry, ann)daughter(eve, tom) -: daughter(tom, ann)daughter(eve, ann) Possible target relation: Here the target relation is:

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 7 Introduction to ILP 5 Dimension of ILP Learning either a single concept or multiple concepts Requires all the training examples to be given before the learning process (batch learners) or accepts examples one by one (incremental learners) The learner may rely on an oracle to verify the validity of generalisation and/or classify examples generated by the learner (interactive; non interactive) The learner may try to learn a concept from scratch or can accept an initial hypothesis (theory) which is then revised in the learning process. The latter system is called theory revision. Existing ILP systems Empirical ILP system: Batch non-interactive system that learns single predicates from scratch Interactive ILP system: Interactive and incremental theory revision system that learns multiple predicates

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 8 Content Introduction to ILP Basic ILP techniques Generalisation techniques Specialisation techniques An overview of the different ILP systems The application field of ILP Summary

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 9 -subsumption STRUCTURING THE HYPOTHESIS SPACE: Introducing partial ordering into a set of clauses based on the -subsumption Def: A substitution is a function from variables to terms. The application of a substitution to a W is obtained by replacing all occurences of each variable in W by the same term Def: Let c and c' be two program clauses. Clause c -subsumes c' if there exits a substitution, such that Def: Two clauses c and d are -subsumption equivalent if c -subsumes d and d -subsumes c. Def: A clause is reduced if it is not -subsumption equivalent to any proper subset of itself.

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 10 -subsumption (2) Example1: Let c be the clause: c = daughter(X,Y) parent(Y,X). A substitution applied to clause c is obtained by applying to each of its literal: c = daughter(mary, ann) parent(ann, mary). Example2: Clause c -subsumes the clause c' = daughter(X,Y) female(X), parent(Y,X) under the empty substitution Example3: Clause c -subsumes the clause c' = daughter(mary,ann) female(mary),parent(ann,mary),parent(ann,tom) under the substitution

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 11 -subsumption (3) -subsumption introduces the syntactic notation of generality: Clause c is at least as general as clause c' ( ), if c - subsumes c' Clause c is more general than clause c' ( ), if holds and does not hold c' is a refinement (specialisation) of c c is a generalisation of c'

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 12 -subsumption (4) -subsumption is important for learning: It provides a generality order for hypotheses, thus structuring the hypothesis space It can be used to prune large parts of the search space: If generalising c to c' all the examples covered by c will be also covered by c' This property is used to prune the search of more general clauses when e is a negative example: if c is inconsistent then all its generalisations will also be inconsistent. Hence, the generalisation of c do not need to be considered. When specialising c to c' an example not covered by c will not be covered by any of its specialisations either. This property is used to prune the search of more specific clauses: if c does not cover a positive example none of its specialisation will do. Hence, the specialisations of c do not need to be considered.

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 13 -subsumption (5).Techniques based on the -subsumption: Bottom-up: creating the least general generalisation from the training examples, relative to the background knowledge Top-down searching of refinement graphs

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 14 Least General Generalisation Properties of -subsumption: If c -subsumes c' then c logically entails c', the reverse is not always true The relation defines a lattice over the set of reduced clauses. This means that any two clauses have a least upper bound (lub) and a greatest lower bound (glb). Def: The least general generalisation (lgg) of two reduced clauses c and c', denoted by lgg(c,c'), is the least upper bound of c and c' in the -subsumption lattice. Example: Let c and c' be two clauses: c= daughter(mary,ann) female(mary), parent(ann,mary). c'= daughter(eve,tom) female(eve), parent(tom,eve). lgg of c and c': daughter(X,Y) female(X), parent(Y,X).

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 15 Least General Generalisation 2 Computation of lgg with -subsumption: lgg of terms and V is a variable which represents and at least one of s and t is a variable in this case, V is a variable which represents Example: where V stands for lgg(a,b)

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 16 Least General Generalisation 3 lgg of atoms If atoms have the same predicate symbol p lgg of literals If and are atoms, then is computed as defined above If both and are negative literals and then If is a positive and is a negative literal, or vice versa, is undefined Example:

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 17 Least General Generalisation 4 lgg of clause Let and. Then Examples: If and then where X stands for lgg(mary,eve) and Y stands for lgg(ann,tom)

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 18 Least General Generalisation 5 Search lgg(, ) Input: and are atoms or literals Output: lgg(, ) = function lgg(, ): begin if head( ) head( ) then := ; return( ) else := ; := while do find_position(,,, ); generate_variable ; substitute(,,,, ) endwhile := ; return( ) endif end

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Least General Generalisation 6 Input: and are terms or literals Output: and terms procedure find_position(,,, ) begin if then return(, ) endif if ( is atomic or is atomic) then return(, ) endif if then return(, ) endif i:=1 while do find_position(,,, ) if then return(, ) endif i:= i+1 endwhile end

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Least General Generalisation 7 Input: and are terms or literals, and terms, X variable Output: and with substitution X for and procedure substitute(,,,, X) begin if and then return(X,X) endif if then return(, ) endif if ( is atomic or is atomic) then return(, ) endif ; if then return(, ) endif i:=1 while do substitute(,,,, X); i:= i+1 endwhile end

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 21 Generalisation techniques Start from the most specific clause that covers a given positive example and then generalise the clause until it cannot be further generalised without covering negative examples. Generalisation operator: Let L be a language bias, a generalisation operator maps a clause c to a set of clauses which are generalisations of c: Generalisation operators perform two basic syntactic operations on a clause: Apply an inverse substitution to the clause Remove a literal from the body of the clause Basic generalisation techniques: Relative least generalisation (rlgg) Inverse resolution

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 22 Relative Least Generalisation 1 Relative least generalisation: The relative least generalisation relative to background knowledge B K is the conjunction of ground facts and are positive example Example: Positive example: and and B as before where K is a conjunction result:

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Relative Least Generalisation 2 Search for rlgg(, ) Input: and are two clauses in the form = { } and = { } Output: rlgg(, ) = function rlgg(, ): begin k := 1; l := 1; while k < n do while l < m do lgg(,, L); ; l := l+1 endwhile k := k+1 endwhile ; return( ) end

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 24 Inverse resolution Basic idea: invert the resolution rule of deductive inference (Robinson) e.g., invert the SLD-Resolution proof procedure for definite programs Example: Given the theory suppose we want to derive u proposition w resolves with to give v which is then resolved with derive u.

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 25 Inverse resolution 2 Example: first order derivation tree for family example and and Let Suppose we want to derive the fact from

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 26 Inverse resolution 3 Inverse resolution inverts the resolution process using the generalisation operator based on the inverting substitution Given a W, an inverse substitution of a substitution is a function that maps terms in to variable such that Example: Take and the substitution : Applying the inverse substitution the original clause is obtained Example: Inverse substitution with places Let and Applying to W: The specifies that the first occurrence of the subterm ann in the term is replaced by variable X and the second occurrence is replaced by Y. The use of places ensures that

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 27 Inverse resolution 4 Example: Inverse Resolution B consists of two clauses and Let The learner encounters the positive example: The inverse resolution processes as follows: It attempts to find which will together with entail and can be added to the current hypothesis Choosing the inverse resolution step generates becomes the current hypothesis H such that It takes and By computing using it generalise with respect to B, yielding In the H can be replaced by which together b entails The induced hypothesis is

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 28 Inverse resolution 4

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 29 Specialisation techniques They search the hypothesis space in top-down manner, from general to specific hypotheses using -subsumption based on a specialisation operator (refinement operator) Refinement operator: Given a language bias L, a refinement operator maps a clause c to a set of clause which are specialisations (refinements) of c This operator typically computes only the set of minimum (most general) specialisations of a clause under -subsumption It employs two basic syntactic operations on a clause: Apply a substitution to the clause Add a literal to the body of the clause

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 30 Specialisation techniques (2) Basic specialisation technique is top-down search of the refinement graph Top-down learners start from the most general clauses and repeatedly refine them until they no longer cover negative examples

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 31 Specialisation techniques 2 For a selected L and a given B the hypothesis space of program clauses is a lattice structured by the -subsumption generality ordering In this lattice a refinement graph can be defined and used to direct the search from general to specific hypotheses The refinement graph is a directed, acyclic graph in which nodes are program clauses and edges correspond to the basic refinement operators: substituting a variable with a term, adding a literal to the body of the clause. First used Model Inference System (MIS, Shapiro 1983)

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 32 Specialisation techniques 3

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 33 Specialisation techniques 4 Generic Top-Down-Algorithm: Input: E the set of positive examples, B the background knowledge L the description language Output: Hypothesis H procedure top_down_ILP( E, B, H) begin ; repeat choose repeat find the best refinement ; until C is acceptable ; until hypothesis H satisfies stopping criterion return( H) end

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 34 Content Introduction to ILP Basic ILP techniques An overview of the different ILP systems The application field of ILP Summary

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 35 An overview of the different ILP systems Prehistory(1970) Plotkin lgg, rlgg Early enthousiasm ( ) Vere 1975,1977, Summers 1977, Kodratoff and Jouanaud 1979 Shapiro 1981, 1983 Dark ages ( )

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 36 An overview of the different ILP systems 2 Renaissance ( ) MARVIN - Sammut & Banereji 1986, DUCE Muggleton 1987 Helft 1987 QuMAS - Mozetic 1987 Linus Lavrac et. al CIGOL - Muggleton &Buntine 1988 ML-SMART Bergadano Giordana & Saitta 1988 CLINT - De Raedt & Bruynooghe 1988 BLIP, MOBAL Morik, Wrobel et. al GOLEM - Muggleton &Feng 1990 FOIL - quinland 1990 mFOIL - Dzerowski 1991 FOCL - Brunk & Pazzani FFOIL - Quinland 1996 CLAUDIEN De Raedt & Bruynooghe 1993 PROGOL - Muggleton et al 1995 FORS- Karalic & Bratko 1996 TILDE - Blockeel & De Raedt Studies Comparing the inductive learning system Foil with its versions FOCL, mFOIL, FFOIL, FOIL-I, FOSSIL, FOIDL and IFOIL

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 37 Content Introduction to ILP Basic ILP techniques An overview of the different ILP systems The application field of ILP Summary

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 38 The application field of ILP Application areas: Knowledge acquisition for expert systems Knowledge discovery in databases Scientific knowledge discovery Logic programs synthesis and verification Inductive data engineering Successful application Finite element mesh design Structure-activity prediction for drug design Protein secondary-structure prediction Predicting mutagenicity of chemical compounds

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Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 39 Summary Inductive logic programming (ILP) = Inductive concept learning (I) Logic Programming (LP) Empirical vs. Interactive ILP systems Generalisation vs. specialisation techniques

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