Learning Agents LaboratoryComputer Science Department

George Mason University

Prof. Gheorghe Tecuci

Multistrategy learningMultistrategy learning

MULTISTRATEGY LEARNINGSUMMARY

This tutorial presents an overview of methods, systems and applications of multistrategy learning. Multistrategy learning is concerned with developing learning systems that integrate two or more inferential and/or computational strategies. For example, a multistrategy system may combine empirical induction with explanation-based learning, symbolic and neural net learning, deduction with abduction and analogy, quantitative and qualitative discovery, symbolic and genetic algorithm-based learning. Due to the complementary nature of various strategies, multistrategy learning systems have a potential for a wide range of applications. This tutorial describes representative multistrategy learning systems, and their applications in areas such as automated knowledge acquisition, planning, scheduling, manufacturing, technical and medical decision making, and computer vision.

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OUTLINE

1. Multistrategy concept learning: methods, systems and applications

2. Multistrategy knowledge base improvement: methods, systems and applications

3. Summary, current trends and frontier research

4. References

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1. MULTISTRATEGY CONCEPT LEARNING:

METHODS, SYSTEMS AND APPLICATIONS

1.1 The class of learning tasks

1.2 Integration of empirical inductive learning and explanation-based learning

1.3 Integration of empirical inductive learning, explanation-based learning, and learning by analogy

1.4 Integration of genetic algorithm-based

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learning and symbolic empirical inductive learning

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1.1 MULTISTRATEGY CONCEPT LEARNING

The Class of Learning Tasks

INPUT: one or more positive and/or negative examples of a concept

BK: a weak, incomplete, partially incorrect, or complete domain theory (DT)

GOAL:learn a concept description characterizing the example(s) and consistent with the DT

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by combining several learning strategies

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Illustration of a Learning Task

INPUT: examples of the CUP conceptcup(o1) color(o1, white), shape(o1, cyl), volume(o1,

8), made-from(o1, plastic), light-mat(plastic), has-handle(o1), has-flat-bottom(o1), up-concave(o1).

BK: a theory of vessels (domain rules)cup(x) liftable(x), stable(x), open-vessel(x).liftable(x) light(x), graspable(x).stable(x) has-flat-bottom(x).

GOAL: learn an operational concept description of CUP

cup(x) made-from(x, plastic), light-mat(plastic),

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graspable(x), has-flat-bottom(x), up-concave(o1).

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1.2 INTEGRATION OF EMPIRICAL INDUCTIVE LEARNING

AND EXPLANATION-BASED LEARNING

• Empirical Inductive Learning (EIL)

• Explanation-Based Learning (EBL)

• Complementary nature of EIL and EBL

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• Types of integration of EIL and EBL

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EMPIRICAL INDUCTIVE LEARNING

• Compares the examples in terms of their similarities and differences, and creates a generalized description of the similarities of the positive examples

• Is data intensive (requires many examples)

• Performs well in knowledge-weak domains

Positive examples of cups: P1 P2 ...

Negative examples of cups: N1

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...

Description of the cup concept: has-handle(x), ...

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EXPLANATION-BASED LEARNING• Proves that the training example is an

instance of the target concept and generalizes the proof

• Is knowledge intensive (requires a complete DT)

• Needs only one example

Prove that o1 is a CUP: Generalize the proof:

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cup(o1)

stable(o1)liftable(o1)

graspable(o1)light(o1)

light-mat(plastic)

made-from(o1,plastic)

......

cup(x)

stable(x)liftable(x)

graspable(x)light(x)

light-mat(y)made-from(x,y)

......

has-handle(o1) has-handle(x)

• "has-handle(o1)" is needed to prove "cup(o1)" • the material the cup is made• "color(o1,white)" is not needed to prove "cup(o1)"from need not be "plastic"

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COMPLEMENTARY NATURE OFEIL AND EBL

Examples

EIL EBL MSL

Domain

many

weak

one

complete

several

incompleteor partiallyincorrect

(EIL+EBL)

Theory is enough

needs

Effect onsystem'sbehavior

improvescompetence

improvesefficiency

improvescompetence & efficiency

Types ofinference induction deductioninduction and

deduction

to be

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MSL METHODS INTEGRATING EIL AND EBL

• Explanation before induction

• Induction over explanations

• Combining EBL with Version Spaces

• Induction over unexplained

• Guiding induction by domain

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theories

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EXPLANATION BEFORE INDUCTION

OCCAM (Pazzani, 1988, 1990)

OCCAM is a schema-based system that learns to predict the outcome of events by applying EBL or EIL, depending on the available background knowledge and examples. It may answer questions about the outcome of hypothetical events.

A learned economic sanctions schema:When a country threatens a wealthy country

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by refusing to sell a commodity, then the sanctions will fail because an alternative supplier will want to make a large profit by selling the commodity at a premium.

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Integration of EBL and EIL in OCCAM

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ExplainExample

Yes

No

No

Schema = EBL(Example)

Schemata

EnoughExamples

Remember Example

Schema = EIL(Examples)Yes

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INDUCTION OVER EXPLANATIONS (IOE)

WYL (Flann and Dietterich, 1989)

Limitations of EBL• The learned concept might be too specific

because it is a generalization of a single example

• Requires a complete DT

IOE• Learns from a set of positive examples

• May discover concept features that are

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not explained by the DT (i.e. incomplete DT)

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The IOE Method

• Build an explanation tree for each example

• Find the largest common subtree

• Apply EBL to generalize the subtree and retain the leaves as an intermediate concept description (ICD)

• Specialize ICD to reflect the similarities between the training

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examples:- replace variables with constants (e.g. v = c)- introduce equality constraints (e.g. v1 = v2)

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IllustrationExplanation tree for example1:

has-handle(o1)

cup(o1)

stable(o1)liftable(o1) open-vessel(o1)

has-flat-bottom(o1)graspable(o1)light(o1)

up-concave(o1)light-mat(plastic)

made-from(o1,plastic)

Explanation tree for example2:cup(o2)

stable(o2)liftable(o2) open-vessel(o2)

width(o2,small)insulating(plastic)

has-flat-bottom(o2)

graspable(o2)light(o2)

up-concave(o2)made-from(o2,plastic)

light-mat(plastic)made-from(o2,plastic)

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Generalization of the common subtree:cup(x)

stable(x)liftable(x) open-vessel(x)

has-flat-bottom(x)

graspable(x)light(x)

up-concave(x)light-mat(y)

made-from(x, y)

ICD

Specialization of ICD:in example1: (y = plastic)in example2: (y = plastic)in ICD: (y = plastic)

Learned concept:cup(x) made-from(x, plastic), light-mat(plastic), graspable(x),

has-flat-bottom(x), up-concave(x).

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COMBINING EBL WITH VERSION SPACES (EBL-VS)

(Hirsh, 1989, 1990)

Limitations of IOE• Learns only from positive examples

• Needs an "almost" complete domain theory (DT)

EBL-VS• Learns from positive and negative

examples

• Can learn with an incomplete DT

• Can learn with a special type of incorrect

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DT

• Can learn with different amounts of knowledge, from knowledge-free to knowledge-rich

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The EBL-VS Method

• Apply EBL to generalize the positive and the negative examples

• Replace each example that has been generalized with its generalization

• Apply the version space method (or the incremental version space merging method) to the new set of examples

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INDUCTION OVER UNEXPLAINED (IOU)

(Mooney and Ourston, 1989)

Limitations of EBL-VS• Assumes that at least one

generalization of an example is correct and complete

IOU• DT could be incomplete but correct

- the explanation-based generalization

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of an example may be incomplete- the DT may explain negative examples

• Learns concepts with both explainable and conventional aspects

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The IOU Method

• Apply EBL to generalize each positive example

• Disjunctively combine these generalizations (this is the explanatory component Ce)

• Disregard negative examples not satisfying Ce and remove the features mentioned in Ce from all the examples

• Apply EIL to determine a generalization

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of the reduced set of simplified examples(this is the nonexplanatory component Cn)

The learned concept is Ce & Cn

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Illustration

Positive examples of cups: Cup1, Cup2

Negative examples: Shot-Glass1, Mug1, Can1

Domain Theory: incomplete (contains a definition of

drinking vessels but no definition of cups)

Ce = has-flat-bottom(x) & light(x) & up-concave(x) &

{[width(x,small) & insulating(x)] ⁄ has-handle(x)}

Ce covers Cup1, Cup2, Shot-Glass1, Mug1 but

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not Can1

Cn = volume(x,small)

Cn covers Cup1, Cup2 but not Shot-Glass1, Mug1

C = Ce & Cn

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GUIDING INDUCTION BY DOMAIN THEORY

The ENIGMA System(Bergadano, Giordana, Saitta et al. 1988, 1990)

Limitations of IOU

• DT rules have to be correct

• Examples have to be noise-free

ENIGMA

• DT rules could be partially incorrect

• Examples may be noisy

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The Learning Method(trades-off the use of DT rules against the coverage of

examples)

• Successively specialize the abstract definition D of the concept to be learned by applying DT rules

• Whenever a specialization of the definition D contains operational predicates, compare it with the examples to identify the covered and the uncovered ones

• Decide between performing:- a DT-based deductive specialization of D- an example-based inductive modification of D

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The learned concept is a disjunction of leaves of the specialization tree built.

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Examples (4 pos, 4 neg)*

Positive example4 (p4):

Cup(o4) light(o4), support(o4, b), body(o4, a), above(a, b), up-concave(o4).

Domain TheoryCup(x) Liftable(x), Stable(x), Open-vessel(x).Liftable(x) light(x), has-handle(x).Stable(x) has-flat-bottom(x).Stable(x) body(x, y), support(x, z), above(y, z).Open-vessel(x) up-concave(x).

DT: - overly specific (explains only p1 and p2)- overly general (explains n3)

* Operational predicates start with a lower-case letter

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induction

deduction

Cup(x)

Liftable(x), Stable(x), Open-vessel(x)

has-flat-bottom(x)

light(x), has-handle(x)Stable(x),Open-vessel(x)

light(x)Stable(x),Open-vessel(x)

light(x),

Open-vessel(x)

light(x), body(x, y),

Open-vessel(x)

p1,p2,n3 p1,p2,p3,p4,n2,n3,n4

p1,p3,n2,n3 p2,p4,n4

support(x, z), above(y, z)

has-flat-bottom(x),light(x),

up-concave(x)

p1,p3,n2,n3

has-flat-bottom(x),light(x),

has-small-bottom(x)

p1,p3light(x), body(x, y),

up-concave(x)

p2,p4

support(x, z), above(y, z),

Open-vessel(x)

deduction

deductiondeduction induction

deduction

deduction

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The Learned Concept

Cup(x) light(x), has-flat-bottom(x), has-small-bottom(x).

Covers p1, p3

Cup(x) light(x), body(x, y), support(x, z), above(y, z), up-concave(x).

Covers p2, p4

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Application of ENIGMA(Bergadano et al. 1990)

• Diagnosis of faults in electro-mechanical devices through an analysis of their vibrations

• 209 examples and 6 classes

• Typical example: 20 to 60 noisy measurements taken in different points and conditions of the device

• A learned rule:IF the shaft rotating frequency is w0 and the

harmonic at w0 has high intensity and the

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harmonic at 2w0 has high intensity in at least two

measurementsTHEN the example is an instance of C1 (problems in

the joint), C4 (basement distortion) or C5

(unbalance)

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Comparison Betweenthe KB Learned by ENIGMA and

the Hand-coded KB of the Expert System MEPS

Ambiguityof rules

ENIGMA

MEPS

Recognitionrate time

Development

1.21

1.46

0.95

0.95 18 months

4 months

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1.3 INTEGRATION OF EMPIRICAL INDUCTIVE LEARNING, EXPLANATION-

BASED LEARNING,AND LEARNING BY ANALOGY

DISCIPLE (Tecuci, 1988; Tecuci and Kodratoff, 1990)

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based mode based mode

Empirical learning mode

explanation analogy criterion

...

AnalogyExplanation

problem1solution1

problem2solution2

problemnsolutionn

generalrule

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1.4 INTEGRATION OF GENETIC ALGORITHMS AND SYMBOLIC

INDUCTIVE LEARNINGGA-AQ (Vafaie and K.DeJong, 1991)

GeneticAlgorithm

feature set best feature

featuresubset

evaluationfunction

(EIL based)

goodness ofrecognition

training examples

subset

Application: Texture recognition

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18 initial features, 9 final features

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2. MULTISTRATEGY KNOWLEDGE BASE IMPROVEMENT (THEORY

REVISION): METHODS, SYSTEMS AND APPLICATIONS

2.1 The class of learning tasks

2.2 Cooperating learning modules

2.3 Integrating elementary inferences

2.4 Applying learning modules in a problem solving environment

2.5 Applying different computational

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strategies

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2.1 MULTISTRATEGY KNOWLEDGE BASE IMPROVEMENT (THEORY

REVISION)The class of learning tasks

Training

Knowledge TheoryReviser

Multistrategy

Examples

Base (DT)

InferenceEngine

KnowledgeBase (DT)

InferenceEngine

Improved

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Types of Theory Errors(in a rule based system)

IncorrectTheory

OverlySpecific

OverlyGeneral

MissingRule

ExtraRule

MissingPremise

AdditionalPremise

positiveexamples

are not

graspable(x) width(x, small), insulating(x), shape(x, round).

graspable(x) insulating(x).

graspable(x) has-handle(x).

graspable(x) shape(x, round).

explained

some

examples are

negative

explained

some

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2.2 COOPERATING LEARNING MODULES

(deduction, abduction and empirical induction)

EITHER (Mooney and Ourston, 1991)

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Knowledge

examples

DeductionModule

AbductionModule

Empirical InductionModule

Minimal Cover andRule Retractor

new rules and

deleted

rules

Minimal Cover andAntecedent Retractor

partialproofs undeletable rules

ungeneralizable rules

generalizedrules specialized rules

rules

Base

unprovablepositive examples

proofs ofnegative examples

to be

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Positive and negative examples of cupscup(o1) width(o1, small), light(o1), color(o1, red),

styrofoam(o1), shape(o1, hem), has-flat-bottom(o1), up-concave(o1), volume(o1,8).

Imperfect Theory of Vesselscup(x) stable(x), liftable(x), open-vessel(x).stable(x) has-flat-bottom(x).liftable(x) light(x), graspable(x).graspable(x) has-handle(x). graspable(x) width(x, small), insulating(x).insulating(x) styrofoam(x).insulating(x) ceramic(x).open-vessel(x) up-concave(x).

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Applications of EITHERI. Molecular Biology: recognizing

promoters and splice-junctions in DNA sequences

.

100

90

40

50

60

70

800 20 40

60

80EITHER

ID3

Training examples

Corr

ect

ness

on

test

data

II. Plant Pathology: diagnosing soybean

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diseases

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2.3 INTEGRATING ELEMENTARY INFERENCES

MTL-JT (Tecuci, 1993, 1994)

• Deep integration of learning strategiesintegration of the elementary inferences that are employed by the single-strategy learning methods (e.g. deduction, analogy, empirical inductive prediction, abduction, deductive generalization, inductive generalization, inductive specialization, analogy-based generalization)

• Dynamic integration of learning strategiesthe order and the type of the integrated strategies depend of the relationship between the input information, the background knowledge and the learning goal

• Different types of input(e.g. facts, concept examples, problem solving episodes)

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• Different types of DT knowledge pieces(e.g. facts, examples, implicative relationships, plausible

determinations)

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Assumptions for the DevelopedFramework and Method

Input:• correct (noise free)• one or several examples, facts, or problem solving episodes

Knowledge Base:• incomplete and/or partially incorrect• may include a variety of knowledge types (facts, examples,

implicative or causal relationships, hierarchies, etc.)

Learning Goal:• extend, update and/or improve the KB so as to integrate new

input information

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An Illustration from the Domain of Geography

Knowledge Base

Facts:terrain(Philippines, flat), rainfall(Philippines, heavy), water-in-soil(Philippines,

high)

Examples (of fertile soil):soil(Greece, fertile-soil) soil(Greece, red-soil)soil(Egypt, fertile-soil) terrain(Egypt, flat) & soil(Egypt, red-soil)

Plausible determination:water-in-soil(x, z) <≈ rainfall(x, y)

Deductive rules:soil(x, fertile-soil) soil(x, loamy)temperature(x, warm) climate(x, subtropical)temperature(x, warm) climate(x, tropical)grows(x, rice) water-in-soil(x, high) & temperature(x, warm) & soil(x,

fertile-soil)

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Positive and negative examples of "grows(x, rice)"

Positive Example 1:grows(Thailand, rice)

rainfall(Thailand, heavy) & climate(Thailand, tropical) & soil(Thailand, red-soil) & terrain(Thailand, flat) & location(Thailand, SE-Asia)

Positive Example 2:grows(Pakistan, rice)

rainfall(Pakistan, heavy) & climate(Pakistan, subtropical) & soil(Pakistan, loamy) & terrain(Pakistan, flat) & location(Pakistan, SW-Asia)

Negative Example 3:¬ grows(Jamaica, rice)

rainfall(Jamaica, heavy) & climate(Jamaica, tropical) & soil(Jamaica, loamy) &

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terrain(Jamaica, abrupt) & location(Jamaica, Central-America)

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The learned knowledge

Improved KB

New facts:water-in-soil(Thailand, high), water-in-soil(Pakistan, high)

New plausible rule:soil(x, fertile-soil) soil(x, red-soil)

Specialized plausible determination:water-in-soil(x, z) <≈ rainfall(x, y), terrain(x, flat)

Concept definitions

Operational definition of "grows(x, rice)":grows(x, rice) rainfall(x,heavy) & terrain(x,flat) & [climate(x,tropical)

⁄ climate(x,subtropical)]& [soil(x,red-soil) ۷soil(x,loamy)]

Abstract definition of "grows(x, rice)":grows(x, rice) water-in-soil(x, high) & temperature(x, warm) & soil(x, fertile-soil)

Abstraction of Example 1:

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grows(Thailand, rice) water-in-soil(Thailand, high) & temperature(Thailand, warm) &

soil(Thailand, fertile-soil)

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The Learning Method

• For the first positive example I1:- build a plausible justification tree T of I1- build the plausible generalization Tu of T- generalize the KB so that to entail Tu

• For each new positive example Ii:- generalize Tu so as to cover a plausible justification

tree of Ii- generalize the KB so that to entail the new Tu

• For each new negative example Ii:- specialize Tu so as not to cover any plausible

justification of Ii- specialize the KB so that to entail the new Tu without

entailing the previous Tu

• Learn different concept definitions:

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- extract different concept definitions from the general justification tree Tu

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Build a plausible justification of the first example

water-in-soil(Thailand, high)

temperature(Thailand, warm)

soil(Thailand, fertile-soil)

rainfall(Thailand, heavy)

climate(Thailand, tropical)soil(Thailand, red-soil)

analogy

deductionand abduction

grows(Thailand, rice)

deduction

inductive prediction

Example 1:grows(Thailand, rice) rainfall(Thailand, heavy) & soil(Thailand,

red-soil) & terrain(Thailand, flat) & location(Thailand, SE-Asia) & climate(Thailand, tropical)

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Multitype generalization

GENERALIZATION

grows(x1, rice)

water-in-soil(x1, high)temperature(x1, warm)

soil(x1, fertile-soil)

rainfall(x2, heavy) soil(x4, red-soil)

DEDUCTIVE

climate(x3, tropical)

DEDUCTIVE GENERALIZATION

GENERALIZATIONINDUCTIVE

ANALOGY

GENERALIZATIONBASED ON

|||water-in-soil(x2, high)

|||temperature(x3, warm)

soil(x4, fertile-soil)|||

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Build the plausible generalization Tu of T

GENERALIZATION

grows(x, rice)

water-in-soil(x, high)temperature(x, warm)

soil(x, fertile-soil)

rainfall(x, heavy) soil(x, red-soil)

DEDUCTIVE

climate(x, tropical)

DEDUCTIVE GENERALIZATION

GENERALIZATIONINDUCTIVE

ANALOGY

GENERALIZATIONBASED ON

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The plausible generalization treecorresponding to the three input examples

GENERALIZATIONS

grows(x, rice)

water-in-soil(x, high)temperature(x, warm)

soil(x, fertile-soil)

rainfall(x, heavy)

climate(x, subtropical)

soil(x, red-soil)

soil(x, loamy)

terrain(x, flat)

DEDUCTIVE

climate(x, tropical)

OROR

DEDUCTIVE GENERALIZATION

GENERALIZATIONINDUCTIVE

GENERALIZATIONDEDUCTIVEANALOGY

GENERALIZATIONBASED ON

SPECIALIZATION INDUCTIVE

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Features of the MTL-JT method

• Is general and extensible

• Integrates dynamically different elementary inferences

• Uses different types of generalizations

• Is able to learn from different types of input

• Is able to learn different types of knowledge

• Is supported by the research in Experimental Psychology

• Exhibits synergistic behavior

• May behave as any of the integrated strategies

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2.4 APPLYING LEARNING MODULES IN

A PROBLEM SOLVING ENVIRONMENT

PRODIGY (Carbonell, Knoblock and Minton, 1989)

• Performance enginePlanner based on state space

search

• Learning strategiesExplanation-based learningLearning by analogyLearning by abstraction

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Learning by experimentation

• ApplicationsMachine-shop schedulingHigh-level robotic planning

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The PRODIGY Architecture

ProblemDomain

KnowledgeEvaluation User

AbstractionCompression InterfaceBuilderEBL PROBLEM

SOLVERPlan AbstractionLibrary

Deriv.Multi-Level Hierarchy

DerivationExtractor PS Trace

Solution ExternalProcesses

ControlKnowledge

Replay

Experimenter

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Independent Learning Systemsin a Uniform Environment

MLT (LRI, ISoft, CGE-LdM, INRIA, BAe, Aberdeen, Turing Institute, GMD, Siemens,

Coimbra, Forth)

Common Interface

Consultant

APT (DISCIPLE)

MOBAL

KBG

NewID•••

10 independent ML systemsloosely integrated through:

• a common interface;

• a consultant;

• a common knowledge representation language (for communication)

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2.5 APPLYING DIFFERENTCOMPUTATIONAL STRATEGIES(symbolic rules and neural networks)

KBANN (Towell and Shavlik, 1991)

SymbolicKB

Rules to NetworkTranslator

Network TrainedNetwork

Initial

Learning

Network to RulesTranslator

Imperfect

TrainingExamples

Network

SymbolicKB

Improved

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Rules to Network Translator

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A B, C.

B not H.

B not F, G.

C I, J.

A

B

X

F

Y C

G H I J K

A

B

C

F G H I J K

Network to Rules Translator(N of M rules)

Z [2 of {A, C, F}]

6.2 6.01.01.2

1.21.06.0

A B C D E F G

Zbias=10.9

6.1 6.1

1.16.11.11.1

1.1

A C F D E B G

6.1 2 > 10.9*

Zbias=10.9

Zbias=10.9

6.16.16.1

A C F

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Error Rates

.

16

12

8

4

00.4

2.9 2.43.8

0.0

12.0

KBANNnetwork

N of M EITHER

Training set

Testing set

16

12

8

4

00.0

7.4

2.3

7.2

KBANNnetwork

N of M

Err

or

rate

Promoter Domain Splice-Junction Domain

Initial KB: 50% 39%

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3. SUMMARY, CURRENT TRENDS

AND FRONTIER RESEARCH

• Summary of application domains

• Issues in selecting a multistrategylearning method

• Current trends in multistrategy learning

• Areas of frontier research

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SUMMARY OF APPLICATION DOMAINS

• Classification: DNA concepts (EITHER, KBANN)

texture recognition (AQ-GA, GA-AQ)

• Diagnosis: mechanical trouble-shooting (ENIGMA)

plant pathology (EITHER)

• Manufacturing: loudspeakers (DISCIPLE1)

• Planning: high-level robot planning (PRODIGY)

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• Prediction: economic sanctions (OCCAM)

• Scheduling: machine-shop scheduling (PRODIGY)

• Critiquing: military courses of action (Disciple-COA)

• Identification: mil. center of gravity (Disciple-COG)

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SOME ISSUES IN SELECTING AMULTISTRATEGY LEARNING

METHOD

• Learning problem:- concept learning- theory revision

• Input data: - positive examples only- positive and negative

examples- noisy examples

• Domain theory: - weak

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- complete- incomplete- partially incorrect

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CURRENT TRENDS IN MSL

• Comparisons of learning strategies

• New ways of integrating learning strategies

• Dealing with incomplete or noisy examples

• General frameworks for MSL

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• Integration of MSL and knowledge acquisition

• Integration of MSL and problem solving

• Applications of MSL systems

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FRONTIER RESEARCH

• Synergistic integration of a wide range of learning strategies

• The representation and use of learning goals in MSL systems

• Evaluation of the certainty of the learned knowledge

• Investigation of human learning as MSL

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• More comprehensive theories of learning

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4. REFERENCES

• Proceedings of the International Conferences on Machine Learning, ICML-87, … , ICML-02, Morgan Kaufmann, San Mateo, 1987- 2002.

• Proceedings of the International Workshops on Multistrategy Learning, MSL-91, MSL-93, MSL-96, MSL-98.

• Special Issue on Multistrategy Learning, Machine Learning Journal, 1993.

• Special Issue on Multistrategy Learning, Informatica, vol. 17. no.4, 1993.

• Machine Learning: A Multistrategy Approach Volume IV, Michalski R.S. and Tecuci G. (Eds.),

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Morgan Kaufmann, San Mateo, 1994.

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