Unifying Version Space Representations: Part II

Unifying Version Space Representations: Part II E.N. Smirnov, I.G. Sprinkhuizen-Kuyper, and H.J. van den Herik IKAT, Department of Computer Science, Maastricht University, P.O.Box 616, 6200 MD Maastricht, The Netherlands {smirnov,kuyper,herik}@cs.unimaas.nl Abstract In this paper we continue our research on unification of version-space representations. We consider the open question how to unify one-sided versionspace representations. To answer the question we introduce a new family of version-space representations that can be adapted during the learning process. The family consists of two dual representations: adaptable one-sided maximal boundary sets (AOMBS) and adaptable one-sided minimal boundary sets (AOmBS). Without loss of generality the article covers in detail only the first representation. AOMBS are defined by the positive training set and the set of maximal boundary sets indexed by the elements of a particular covering of the negative training set. We show that a version space has a space of AOMBS representations each having its own applicability requirements. This space includes the basic one-sided version-space representations: the one-sided maximal boundary sets (Hirsh, 1992) and the one-sided instance-based maximal boundary sets (Smirnov et al., 2002). So, we may conclude that AOMBS unify these representations. The task of learning AOMBS is viewed as a task of identifying a proper representation within the space of AOMBS representations depending on the applicability requirements given. This is demonstrated in an example where AOMBS are used to overcome the complexity problem of the one-sided maximal boundary sets. 1 Introduction Version spaces are nowadays an established approach to the concept-learning task (Mitchell, 1997). They are defined as sets of descriptions in concept languages that are consistent with training data. Version-space learning is an incremental process (Mitchell, 1997): if a new training instance is given, the version space is revised so that it consists of all the concept descriptions consistent with the processed training data plus the instance. To learn version spaces they have to be represented. For that purpose several version-space representations were proposed in (Hirsh, 1992; Hirsh et al., 1997; Idemstam-Almquist, 1990; Sablon et al., 1994; Sebag and Rouveirol, 2000; Smirnov, 2001; Smirnov et al., 2002; Smith and Rosenbloom, 1990). Besides the diverse nature, the representations differ in terms of applicability. Although it is a sign of the maturity of the field of version spaces, deep knowledge of the representations is required when they are chosen and applied. To facilitate the potential user we proposed in (Smirnov et al., 2004) to unify version-space representations into representations with broader scopes of applicability. The first two representations unified were the boundary sets (Mitchell, 1997) and the instance-based boundary sets (Smirnov, 2001). The result of the unification was a version-space representation called adaptable boundary sets (Smirnov et al., 2004). We showed that it has a broader scope of applicability than both the boundary sets and the instance-based boundary sets. In this paper we address the open question how to unify the families of one-sided versionspace representations. We focus on the unification of the family of one-sided boundary sets (Hirsh, 1992) and the family of one-sided instance-based boundary sets (Smirnov et al., 2002). The motivation for this research question is twofold: (1) the one-sided boundary sets have a broader scope of applicability than the boundary sets (Hirsh, 1992); and (2) the one-sided instance-based boundary sets have a broader scope of applicability than the instance-based boundary sets (Smirnov et al., 2002). The result of unification is a new family of unifying versionspace representations called adaptable one-sided boundary sets. In this paper we show that the adaptable one-sided boundary sets are the most

applicable version-space representations. The family of adaptable one-sided boundary sets consists of two dual representations: adaptable one-sided maximal boundary sets (AOMBS) and adaptable one-sided minimal boundary sets (AOmBS). We cover in detail only the AOMBS representation. The paper starts in section 2 with a necessary formalisation. The AOMBS are introduced in section 3. We show that a version space can have a space of AOMBS each having its own applicability requirements. We prove that this space includes the one-sided maximal boundary sets (Hirsh, 1992) and the one-sided instancebased maximal boundary sets (Smirnov et al., 2002). Thus, the AOMBS unify these two representations. The task of learning AOMBS is considered as identifying a proper representation in the space of AOMBS representations depending on the applicability requirements given. For that purpose a general learning algorithm, two merging algorithms, and other useful algorithms of the AOMBS are given in sections 4, 5, and 6. Section 7 provides an example how AOMBS overcome the complexity problem of one-sided maximal boundary sets. The dual representation of the AOMBS, adaptable one-sided minimal boundary sets (AOmBS), is given in section 8. In section 9 conclusions are given. 2 Formalisation Let I be the universe of all the instances. Concepts C are defined as subsets of I. They are represented in a concept language Lc. The language Lc is a set of descriptions c representing each exactly one concept. Instances are related to concept descriptions by a cover relation M. The relation M(c, i) holds for c Lc, i I iff the instance i is a member of the concept given by c. A description c Lc is said to cover an instance i I iff the relation M(c, i) holds. As a rule any target concept C is incompletely defined by training sets I + I and I I of positive and negative instances such that I + C and I C =. The concept-learning task in this case is to find descriptions of C in Lc. To find the descriptions of a target concept, we specify them by the consistency criterion: a description c is consistent iff c correctly classifies the training data. Mitchell (1997) defined the set of all consistent descriptions w.r.t. the training sets I + and I of a target concept as the version space VS(I +, I ): {c Lc ( i I + )M(c, i) ( i I ) M(c, i)}. To learn version spaces, they have to be compactly represented. It is possible usually if concept languages are ordered by a relation more general ( ). The relation c 1 c 2 holds for descriptions c 1, c 2 Lc iff: ( i I)(M(c 1, i) M(c 2, i)). A concept language Lc with the relation is partially ordered. In our study we are interested in sets C Lc that have a maximal set MAX (C): {c C ( c C)((c c) (c = c))} and a minimal set MIN (C): {c C ( c C)((c c ) (c = c))}. The maximal and minimal sets of version spaces are known as maximal and minimal boundary sets (Mitchell, 1997). Concept languages for which the boundary sets can be defined for each version space are called admissible. More formally, a concept language Lc is admissible iff each subset C Lc is bounded; i.e., for each c C there exist g MAX (C) and s MIN (C) s.t. g c and c s. 3 Adaptable One-Sided Maximal Boundary Sets Adaptable one-sided maximal boundary sets (AOMBS) are a version-space representation that consists of (1) the training set I + of positive instances and (2) the indexed set of maximal boundary sets based on the elements of a particular covering 1 of the training set I of negative instances. AOMBS are formally defined in definition 1. The definition uses the notion of the set of all the coverings of the set I denoted as SP(I ). Definition 1 (AOMBS) Consider an admissible concept language Lc and the nonempty training set I I. Then 1 A covering of a nonempty set I is a set consisting of nonempty subsets of I such that the union of those subsets is the whole set.

AOMBS of the version space VS(I +, I ) are an ordered pair I +, {G(I +, I n )} I, where P SP(I ) and G(I +, I n ) = MAX (VS(I +, I n )). Theorem 1 (Correctness of AOMBS) Let VS(I +, I ) be a version space with AOMBS: I +, {G(I +, I n )} I. If the concept language Lc is admissible, then a concept description c Lc is a member of the version space VS(I +, I )) iff: ( p I + )M(c, p) and ( I n P )( g G(I +, I n ))(g c))). By theorem 1, given the AOMBS of a version space VS(I +, I ), the descriptions in VS(I +, I ) are those that are (1) consistent with the set I +, and (2) more specific than an element of each set G(I +, I n ). Thus, the AOMBS are a compact version-space representation. A version space has a space of AOMBS representations. Below this space is formally defined. Definition 2 (Space of AOMBS representations) Given I, the space R(I +, I ) of AOMBS representations of the version space VS(I +, I ) is defined as: { I +, {G(I +, I n )} I P SP(I )}. The space R(I +, I ) is partially ordered by a relation more homogeneous : AOMBS: I +, {G(I +, In )} I are more homogeneous 1 than AOMBS: I +, {G(I +, In )} I iff each 2 set In P1 is a union of one or more sets In P2. The least element of R(I+, I ) is the representation I +, {G(I +, {n})} n I defined for P = i I {{i}}. According to (Smirnov et al., 2002) this AOMBS representation equals instance-based maximal boundary sets. The greatest element of R(I +, I ) is the representation I +, {G(I +, I )} defined for P = {I }. According to (Hirsh, 1992) this AOMBS representation equals one-sided maximal boundary sets. Since the space of AOMBS representations includes the instance-based maximal boundary sets and the one-sided maximal boundary sets, the AOMBS unify these two representations. More homogeneous AOMBS are not always more comprehensible. For example, AOMBS, which covering P is the power set of the set I, are more homogeneous than the AOMBS equal to the instance-based maximal boundary sets, but they are not more comprehensible. Thus, we say that (AOMBS) 1 are more comprehensible than (AOMBS) 2 if the covering P1 contains less elements than the covering P2. The AOMBS in the space R(I +, I ) are different also in terms of the conditions when they are finite, (efficiently) computable and allow (efficiently) implementing the basic version-space operations (Hirsh, 1992; Hirsh et al., 1997; Smirnov, 2001). For example, we proved in (Smirnov et al., 2002) that the conditions for finiteness and (efficient) computation of the onesided maximal boundary sets are more restrictive than those for finiteness and efficient computation of the instance-based maximal boundary sets. In addition, we showed that both representations can be used for implementing most version-space operations. Nevertheless, there are operations that can be implemented either with one-sided maximal boundary sets or with instance-based maximal boundary sets. From the above we conclude that learning AOMBS requires setting in advance applicability requirements. The requirements can be the levels of homogeneity and comprehensibility, and the conditions when the representations are finite, (efficiently) computable and allow (efficiently) implementing the version-space operations. Once the requirements are set, the task of learning AOMBS is to identify a proper representation in the space of AOMBS representations. In the next two sections we present learning and merging algorithms designed for this purpose. 4 General Learning Algorithm The general learning algorithm updates the AOMBS of a version space w.r.t. a training instance. It is given for admissible concept languages and has two parts. The first part of the algorithm is applied only if the new training instance i is negative. The instance is added to the training set I. The covering P of the updated set I = I {i} is formed from the covering P of the set I s.t. i is added to some elements of P and/or the set

{i} is added to P. The covering P is called an extension of the covering P. Formally, this relation is defined for P and P if for each In P : ( I n P )(I n ( In P )(In = {i})). (I n = In ) = In {i}) We note that the first part of the algorithm is not completely specified. It is determined depending on the applicability requirements imposed on the AOMBS to be learned. The second part of the general learning algorithm is applied for both positive and negative training instances. If the instance i is positive, given the AOMBS: I +, {G(I +, In )} I of the version space VS(I +, I ) and the set I + = I + {i}, the algorithm computes the AOMBS: I +, {G(I +, In )} I of the version space VS(I +, I ). If the instance i is negative, given the AOMBS: I +, {G(I +, In )} I of the version space VS(I +, I ) and the covering P of the training set I (I = I {i}), the algorithm computes the AOMBS: I +, {G(I +, In )} I of the version space VS(I +, I ). The second part of the algorithm is based on theorems 2 and 3 given below. Theorem 2 Let VS(I +, I ) be a version space represented by AOMBS: I +, {G(I +,I n )} I, and let VS(I +, I ) be a version space represented by AOMBS: I +, {G(I +, I n )} I, where I + = I + {i}. If the concept language Lc is admissible, then for each I n P : G(I +, I n ) = {g G(I +, I n ) M(g, i)}. Theorem 3 Let VS(I +, I ) be a version space represented by AOMBS: I +, {G(I +, In )} I, and let VS(I +, I ) be a version space represented by AOMBS: I +, {G(I +, In )} I, where I = I {i}. If the concept language Lc is admissible, then: for In P s.t. ( In P )(In = In ): G(I +, In ) = G(I +, In ) for In P s.t. ( In P )(In = In {i}): G(I +, In )=MAX ({c VS(I +, In ) M(c, i)}) if {i} P : G(I +, {i}) = MAX (VS(I +, {i})). To facilitate implementation we formulate the second part of the algorithm in detailed steps. Given a new training instance i I and the AOMBS: I +, {G(I +, I n )} I of a version space VS(I +, I ), this part of the algorithm is as follows: if the instance i is positive then I + = I + {i}. for each I n P : - form the set G(I +, I n ) from the elements of the set G(I +, I n ) that cover i; return AOMBS: I +, {G(I +, I n )} I of VS(I +, I ). if the instance i is negative and a covering P of the training set I =I {i} is created, then for I n P s.t. ( I n P )(I n = I n ): - take the set G(I +, I n ) from the AOMBS: I +, {G(I + n, I )} I ; for In P s.t. ( In P )(In =In {i}): - form the set G(I +, In ) as the maximal set of the subset of the version space VS(I +, In ) which elements do not cover the instance i; if {i} P then generate the set G(I +, {i}); return AOMBS: {I +, {G(I +, I n )} I of VS(I +, I ). The algorithm can be tuned to learn different AOMBS. If the covering P is formed s.t. P = i I {{i}}, then instance-based maximal boundary sets are learned. If the covering P is formed s.t. P = {I }, then one-sided maximal boundary sets are learned. In all other cases the algorithm learns some AOMBS in the space R(I +, I ) that are different from the least and greatest elements. 5 Merging Algorithms Given the AOMBS of a version space, merging algorithms form more homogeneous AOMBS of

the same version space. In this section we propose two such algorithms that differ w.r.t. the requirements the concept language satisfies. 5.1 General Merging Algorithm The general merging algorithm is given for admissible concept languages. It consists of two parts. To describe the first part of the algorithm we introduce the notion of merged regroupment. Let P and P be coverings of the set I. If P P, then P is a merged regroupment of P iff: ( I n P )( Q P )(I n = I n Q I n ). Given the AOMBS: I +, {G(I +, I n )} I of a version space VS(I +, I ), the first part of the algorithm computes the merged regroupment P of the covering P. This computation is specified depending on the applicability requirements imposed on the AOMBS to be merged. The second part of the algorithm is based on theorem 4 below. Theorem 4 Let VS(I +,I ) be a version space given by two AOMBS: I +, {G(I +,In )} I and I +, {G(I +, In )} I s.t. P is a merged regroupment of P. If the concept language Lc is admissible, then for In P the set G(I +, In ) is equal to: {c MS({G(I +, I n )} I n Q ) ( p I + )M(c, p)} where Q SP(In ), Q P, and MS({G(I +, In )} I n Q ) is the maximal set of {c Lc ( In Q )( g G(I +, In ))(g c)}. Below we describe the second part of the general merging algorithm. Given the AOMBS: {I +, {G(I +, In )} I of a version space VS(I +, I ) and the covering P (computed by the first part), the second part of the algorithm computes the merged AOMBS: {I +, {G(I +, In )} I of the same version space VS(I +, I ). This is done in the following steps: for each set I n P s.t. I n P : - take the set G(I +, I n ) from the AOMBS I +, {G(I +, I n )} I ; for each set I n P s.t. I n / P : - compute the set MS({G(I +, I n )} I n Q ) where Q P s.t. I n = I n Q I n ; - form the maximal boundary set G(I +, In ) from those elements of the set MS({G(I +, In )} I n Q ) that cover all the instances in I + ; return the resulting merged AOMBS: I +, {G(I +, In )} I of VS(I +, I ). 5.2 Specialised Merging Algorithm The specialised merging algorithm is proposed for computing more homogeneous AOMBS of which the sizes are reduced. It is applicable for admissible concept languages if the intersectionpreserving property holds (Smirnov, 2001). Definition 3 (Intersection-Preserving Property (IP)) An admissible concept language has the intersection preserving property when for each nonempty set C Lc there exists the greatest lower bound glb(c) Lc s.t. an instance i I is covered by all the elements of C iff the instance i is covered by glb(c). The specialised merging algorithm is based on theorem 5 below. Theorem 5 Consider a set I, a covering P SP(I ), and a nonempty set Q P s.t. ( In Q ) G(I +, In ) = 1. If the concept language Lc is admissible and the property IP holds, then: G(I +, I n Q I n ) = {glb( I n Q G(I +, I n ))}. Below we describe the specialised merging algorithm. The input is an AOMBS: I +, {G(I +, In )} I of a version space VS(I +, I ). The output is a more homogeneous AOMBS of VS(I +, I ). The first part of the algorithm determines the new regroupment P of the covering P. This is done in four steps: - initialise a set Q to be empty;

- determine each maximal boundary set G(I +, I n ) in the AOMBS s.t. G(I +, I n ) = 1 and add I n to Q ; - form the set I n = I n Q I n ; - P = (P \ Q ) {I n }. If P P the second part of the algorithm is executed in three steps: - compute glb( I n Q G(I +, I n )); - G(I +, I n ) = {glb( I n Q G(I +, I n ))} (according to theorem 5); - return AOMBS: I +, {G(I +, I n )} I of VS(I +, I ). According to step 2 the size of the set G(I +, In ) is equal to one. Thus, the algorithm guarantees that the size of the merged AOMBS is reduced. 6 Other Useful Algorithms In this section we propose two useful algorithms for the AOMBS. The first one is an algorithm for version-space collapse. It checks whether version spaces are empty. The second algorithm is a classification algorithm. 6.1 Algorithm for Version-Space Collapse The algorithm for version-space collapse is proposed for admissible languages with the property IP. It is based on theorem 6 showing how to use version spaces VS(I +, I n ) for I n P to check if the version space VS(I +, I ) is empty. Theorem 6 Consider an admissible concept language Lc s.t. the property IP holds. If the set I is nonempty, then: (VS(I +, I ) ) ( I n P )(VS(I +, I n ) ). If the property IP holds, then according to theorem 6 in order to check the version space VS(I +, I ) for collapse we need to check for collapse the version spaces VS(I +, In ) for In P. Since VS(I +, In ) are represented by their maximal boundary sets G(I +, In ) in the AOMBS of VS(I +, I ), we have to determine a relation between the maximal boundary sets G(I +, In ) and the version spaces VS(I +, In ). The desired relation is given in theorem 7 taken from (Smirnov, 2001). Theorem 7 states that the version space VS(I +, I ) is not empty if and only if the maximal boundary set G(I +, I ) is not empty. Theorem 7 If the concept language Lc is admissible, then: (VS(I +, I ) ) (G(I +, I ) ). Theorems 6 and 7 imply corollary 1. It states that if the property IP holds and the training set I is not empty, then the version space VS(I +, I ) is not empty iff for each In P the maximal boundary set G(I +, In ) is not empty. Corollary 1 Consider an admissible concept language Lc s.t. the property IP holds and a training set I with a covering P SP(I ). If I, then: (VS(I +, I ) ) ( I )(G(I +, I n ) ). Below we describe the algorithm for version-space collapse. The input is an AOMBS: I +, {G(I +, In )} I of a version space VS(I +, I ). The output is true if VS(I +, I ) = ; otherwise, it is false. The algorithm is as follows: for all I n P : if G(I +, I n ) = then return true (by corollary 1); return false. 6.2 Classification Algorithm The classification algorithm is based on unanimous voting (Mitchell, 1997): an instance i is classified if all the descriptions in a version space VS(I +, I ) agree on a classification. The algorithm is given for admissible languages with the property IP. It is based on dual theorems 8 and 9 from (Smirnov, 2001). Theorem 8 states that the subset of VS(I +, I ) of which the elements do not cover the instance i is empty iff all the descriptions of VS(I +, I ) cover the instance i. Theorem 9 can be explained by duality.

Theorem 8 For i I : VS(I +, I {i}) = iff (( c VS(I +, I ))M(c, i). Theorem 9 For i I : VS(I + {i}, I ) = iff (( c VS(I +, I )) M(c, i). The classification algorithm is described as follows. Its input is an instance i and the AOMBS of a version space VS(I +, I ). The algorithm outputs: + if all the descriptions in VS(I +, I ) cover i; if no descriptions in VS(I +, I ) cover i; or? otherwise. The algorithm consists of three steps: - form the AOMBS of the version space VS(I +, I {i}); if VS(I +, I {i}) = then return + ; - form the AOMBS of the version space VS(I + {i}, I ); if VS(I + {i}, I ) = then return - ; - return?. We note that the classification algorithm is applicable only if the property IP holds because of the use of the algorithm for version-space collapse. 7 Example of AOMBS Consider the concept-learning task from Table 1: the instance universe I and the concept language Lc are 1-CNF languages with 8 Boolean attributes s.t. I Lc 2. The training instances are chosen s.t. G({i + 1 }, {i 2, i 3, i 4, i 5 }) = 2 {i 2,i 3,i 4,i 5 } = 16; i.e., the size of one-sided boundary sets is exponential in the number of negative instances. Our goal is to learn AOMBS of which the size is polynomial in the size of training data. The goal is achievable since the space of AOMBS representations contains instance-based maximal boundary sets of which the size is polynomial in the number of training instances (Smirnov et al., 2002). Therefore, we tune the general learning algorithm to form the new covering P s.t. the upper size limit of the 2 Note that the concept language Lc is admissible and the property IP holds. desired AOMBS is the size of the instance-based maximal boundary sets 3. The initialisation and the trace of the general learning algorithm are given in the second column of Table 1. They are explained as follows. Before the instance i 4 the AOMBS are equal to one-sided maximal boundary sets, since the sizes of the one-sided maximal boundary sets and the instance-based maximal boundary sets are equal. When the instance i 4 is given, the algorithm has two choices: (1) to update the set G({i + 1 }, {i 2, i 3 }); or (2) to create the set G({i + 1 }, {i 4 }). If the first choice is made the size of the AOMBS becomes 9. If the second choice is made the size of the AOMBS becomes 7; i.e., it is equal to the size of the instance-based maximal boundary sets. Thus, the algorithm makes the second choice. When the instance i 5 is given, the algorithm can be analogously explained. When the positive instance i + 6 is processed, the sets G({i + 1 }, {i 2, i 3 }) and G({i+ 1 }, {i 4, i 5 }) are updated, and the sets G({i + 1, i+ 6 }, {i 2, i 3 }) and G({i + 1, i+ 6 }, {i 4, i 5 }) are created and added to the AOMBS. The resulting AOMBS are processed by the specialised merging algorithm because the property IP holds and the G sets contain just one element. Thus, the G part of the final AOMBS are given by G({i + 1, i+ 6 }, {i 2, i 3, i 4, i 5 }) = {(1,?, 1,?, 1,?, 1,?)}; i.e., the final AOMBS equals the one-sided boundary sets. 8 Adaptable One-Sided Minimal Boundary Sets Adaptable one-sided minimal boundary sets (AOmBS) can be derived by duality from the previous sections. The AOmBS unify the onesided minimal boundary sets (Hirsh, 1992) and the one-sided instance-based minimal boundary sets (Smirnov et al., 2002). We refrain from providing further details. 9 Conclusions This paper considered the open question how to unify one-sided version-space representations. 3 In reality the worst-case size of instance-based maximal boundary sets is used. For 1-CNF languages with Boolean attributes it equals ( I + + I )A, where A is the number of attributes. Thus, the size can be computed without computing the representation. It is not used in our example because of space limits.

Training Instance Initialisation S A S B S I i + 1 =(1,1,1,1,1,1,1,1) I+ = {(1, 1, 1, 1, 1, 1, 1, 1)} i 2 =(0,0,1,1,1,1,1,1) G({i+ 1 }, {i 2 }) = {(1,?,?,?,?,?,?,?), (?, 1,?,?,?,?,?,?)} 3 3 3 Algorithm Trace i 3 =(1,1,0,0,1,1,1,1) I+ = {(1, 1, 1, 1, 1, 1, 1, 1)} G({i + 1 }, {i 2, i 3 }) = {(1,?, 1,?,?,?,?,?), (1,?,?, 1,?,?,?,?), (?, 1, 1,?,?,?,?,?), (?, 1,?, 1,?,?,?,?)} 5 5 5 i 4 =(1,1,1,1,0,0,1,1) I+ = {(1, 1, 1, 1, 1, 1, 1, 1)} G({i + 1 }, {i 2, i 3 }) = {(1,?, 1,?,?,?,?,?), (1,?,?, 1,?,?,?,?), (?, 1, 1,?,?,?,?,?), (?, 1,?, 1,?,?,?,?)} G({i + 1 }, {i 4 }) = {(?,?,?,?, 1,?,?,?), (?,?,?,?,?, 1,?,?)} 7 9 7 i 5 =(1,1,1,1,1,1,0,0) I+ = {(1, 1, 1, 1, 1, 1, 1, 1)} G({i + 1 }, {i 2, i 3 }) = {(1,?, 1,?,?,?,?,?), (1,?,?, 1,?,?,?,?), (?, 1, 1,?,?,?,?,?), (?, 1,?, 1,?,?,?,?)} G({i + 1 }, {i 4, i 5 }) = {(?,?,?,?, 1,?, 1,?), (?,?,?,?, 1,?,?, 1), (?,?,?,?,?, 1, 1,?), (?,?,?,?,?, 1,?, 1)} 9 17 9 i + 6 =(1,0,1,0,1,0,1,0) I+ = {(1, 1, 1, 1, 1, 1, 1, 1), (1, 0, 1, 0, 1, 0, 1, 0)} G({i + 1, i+ 6 }, {i 2, i 3 }) = {(1,?, 1,?,?,?,?,?)} G({i + 1, i+ 6 }, {i 4, i 5 }) = {(?,?,?,?, 1,?, 1,?)}. 4 3 6 Table 1: Execution of the General Learning Algorithm. The first column shows the training instances, the second column shows the algorithm execution; the third, fourth and fifth columns show the size S A, S B and S I of the AOMBS, the one-sided maximal boundary sets; and the instancebased maximal boundary sets. To answer the question we introduced a family of new version-space representations called adaptable one-sided boundary sets. We showed that the new representations unify the family of one-sided boundary sets (Hirsh, 1992) and the family of one-sided instance-based boundary sets (Smirnov et al., 2002). In this context we note that the latter families consist of the most applicable version-space representations known till now. Thus, we conclude that currently the adaptable one-sided boundary sets are the most applicable version-space representations. References H. Hirsh, N. Mishra, and L. Pitt. 1997. Version spaces without boundary sets. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), pages 491 496, Menlo Park, CA. AAAI Press. H. Hirsh. 1992. Polynomial-time learning with version spaces. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pages 117 122, Menlo Park, CA. AAAI Press. P. Idemstam-Almquist. 1990. Demand networks: an alternative representation of version spaces. Master s thesis, Department of Computer Science and Systems Sciences, Stockholm University, Stockholm, Sweden. T.M. Mitchell. 1997. Machine learning. McGraw- Hill, New York, NY. G. Sablon, L. DeRaedt, and M. Bruynooghe. 1994. Iterative versionspaces. Artificial Intelligence, 69(1 2):393 410. M. Sebag and C. Rouveirol. 2000. Resourcebounded relational reasoning: induction and deduction through stochastic matching. Machine Learning, 38(1-2):41 62. E.N. Smirnov, I.G. Sprinkhuizen-Kuyper, and H.J. van den Herik. 2002. New version-space representations for efficient instance retraction. In Proceedings of the First International Workshop on Knowledge Discovery in Inductive Databases (KDID-2002). E.N. Smirnov, I.G. Sprinkhuizen-Kuyper, and H.J. van den Herik. 2004. A unifying version-space representation. Annals of Math and Artificial Intelligence (in print). E.N. Smirnov. 2001. Conjunctive and disjunctive version spaces with instance-based boundary sets. Ph.D. thesis, Department of Computer Science, Maastricht University, Maastricht, The Netherlands. B.D. Smith and P.S. Rosenbloom. 1990. Incremental non-backtracking focusing: a polynomially bounded generalization algorithm for version spaces. In Proceedings of the Eight National Conference on Artificial Intelligence (AAAI-90), pages 848 853. MIT Press.