2003, g.tecuci, learning agents laboratory 1 learning agents laboratory computer science department...

1 2003, G.Tecuci, Learning Agents Laboratory

Learning Agents LaboratoryComputer Science Department

George Mason University

Prof. Gheorghe Tecuci

10 Multistrategy Learning10 Multistrategy Learning


OverviewOverview

Introduction

Combining EBL with Version Spaces

Induction over Unexplained

Basic references

Guiding Induction by Domain Theory

Plausible Justification Trees

Research Issues


Multistrategy learningMultistrategy learning

Multistrategy learning is concerned with developing learning agents that synergistically integrate two or more learning strategies in order to solve learning tasks that are beyond the capabilities of the individual learning strategies that are integrated.


Examples

Learningfrom examples

Explanation-based learning

Multistrategy

Knowledge

many one several

learning

needed

Effect onagent'sbehavior

improves

competence

improves

efficiency

improvescompetence or/ and efficiency

Type ofinference induction deduction

induction and/ or deduction

Complementariness of learning strategiesComplementariness of learning strategies

complete incompleteknowledge

very littleknowledge

needed

Case Study:Inductive Learning vs Explanation-based Learning


Multistrategy concept learningMultistrategy concept learning

Input

Background Knowledge (Domain Theory)

Goal

The Learning Problem

One or more positive and/or negative examples of a concept

Weak, incomplete, partially incorrect, or complete

Learn a concept description characterizing the example(s) and consistent with the background knowledge by combining several learning strategies


Multistrategy knowledge base refinementMultistrategy knowledge base refinement

The Learning Problem:Improve the knowledge base so that the Inference Engine solves (classifies) correctly the training examples.

Similar names: background knowledge – domain theory – knowledge baseknowledge base refinement - theory revision

Training

Knowledge Knowledge BaseRefinement

Multistrategy

Examples

Base (DT)

InferenceEngine

KnowledgeBase (DT)

InferenceEngine

Improved


Types of theory errors (in a rule based system)Types of theory errors (in a rule based system)

graspable(x) has-handle(x)

IncorrectKB (theory)

OverlySpecific

OverlyGeneral

MissingRule

ExtraRule

MissingPremise

AdditionalPremise

shape(x, round)

graspable(x) insulating(x)graspable(x)

width(x, small)& insulating(x)&

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

What is the effect of each error on the system’s ability to classify graspable objects, or other objects that need to be graspable, such as cups?

Proofs for some positive examples cannot be built:

Proofs for some negative examples can be built:

Positive examples that are not round, or have a handle

Negative examples that are round, or are insulating but not small

How would you call a KB where some positive examples are not explained (classified as positive)?

How would you call a KB where some negative examples are wrongly explained (classified as positive)?

+ + + _

Positive examples Negative examples


OverviewOverview

Introduction



Basic references



Research Issues


EBL-VS: Combining EBL with Version SpacesEBL-VS: Combining EBL with Version Spaces

• Apply explanation-based learning to generalize the positive and the negative examples.

• Replace each example that has been generalized with its generalization.

• Apply the version space method to the new set of examples.

Produce an abstract illustration of this algorithm.


EBL-VS features EBL-VS features

• Learns from positive and negative examples

Justify the following feature, considering several cases:

• Apply explanation-based learning to generalize the positive and the negative examples.• Replace each example that has been generalized with its generalization.• Apply the version space method to the new set of examples.



Justify the following feature:


• Can learn with an incomplete background knowledge





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


EBL-VS features summary and references EBL-VS features summary and references

• Learns from positive and negative examples

• Can learn with an incomplete background knowledge

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

Hirsh, H., "Combining Empirical and Analytical Learning with Version Spaces," in Proc. of the Sixth International Workshop on Machine Learning, A. M. Segre (Ed.), Cornell University, Ithaca, New York, June 26-27, 1989.

Hirsh, H., "Incremental Version-space Merging," in Proceedings of the 7th International Machine Learning Conference, B.W. Porter and R.J. Mooney (Eds.), Austin, TX, 1990.


OverviewOverview

Introduction



Basic references



Research Issues


IOU: Induction Over Unexplained IOU: Induction Over Unexplained

Limitation of EBL-VS• Assumes that at least one generalization of an example is correct and complete

IOU• Knowledge base could be incomplete but correct: - the explanation-based generalization of an example may be incomplete; - the knowledge base may explain negative examples.• Learns concepts with both explainable and conventional aspects

Justify the following limitation of EBL-VS:


IOU method IOU method

• Apply explanation-based learning 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 empirical inductive learning to determine a generalization of the reduced set of simplified examples (this is the non-explanatory component Cn)

The learned concept is Ce & Cn


IOU: illustration IOU: illustration

Positive examples of cups: Cup1, Cup2

Negative examples: Shot-Glass1, Mug1, Can1

Domain Theory: incomplete - contains a definition of a generalization of the concept to be learned (e.g. 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 not Can1

Cn = volume(x,small)

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

C = Ce & Cn

Mooney, R.J. and Ourston, D., "Induction Over Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects,", in Proc. of the Sixth International Workshop on Machine Learning, A.M. Segre (Ed.), Cornell University, Ithaca, New York, June 26-27, 1989.


OverviewOverview

Introduction



Basic references



Research Issues


Enigma: Guiding Induction by Domain TheoryEnigma: Guiding Induction by Domain Theory

Limitations of IOU• Knowledge base rules have to be correct• Examples have to be noise-free

ENIGMA• Knowledge base rules could be partially incorrect• Examples may be noisy

Justify the following limitations of IOU:


Enigma: methodEnigma: method

Trades-off the use of knowledge base rules against the coverage of examples:

• Successively specialize the abstract definition D of the concept to be learned by applying KB 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 KB-based deductive specialization of D- an example-based inductive modification of D

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


Enigma: illustrationEnigma: illustration

Examples (4 positive, 4 negative)

Positive example4 (p4):light(o4) & support(o4,b) & body(o4,a) & above(a,b) & up-concave(o4) Cup(o4)

Background Knowledge

Liftable(x) & Stable(x) & Open-vessel(x) Cup(x)

light(x) & has-handle(x) Liftable(x)

has-flat-bottom(x) Stable(x)

body(x, y) & support(x, z) & above(y, z) Stable(x)

up-concave(x) Open-vessel(x)

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

Operational predicates start with a lower-case letter


Enigma: illustration (cont.)Enigma: illustration (cont.)

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

(to cover p3,p4)

(to uncover n2,n3)

Classification is based only on operational features:


Learned conceptLearned concept

light(x) & has-flat-bottom(x) &has-small-bottom(x) Cup(x) Covers p1, p3

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

Covers p2, p4


ApplicationApplication

• 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 harmonic at 2w0 has high intensity in at least two measurements

THEN the example is an instance of C1 (problems in the joint), C4 (basement distortion) or C5 (unbalance)


Application (cont.)Application (cont.)

Comparison between the KB learned by ENIGMA andthe 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

Bergadano, F., Giordana, A. and Saitta, L., "Automated Concept Acquisition in Noisy Environments," IEEE Transactions on Pattern Analysis and Machine Intelligence, 10 (4), pp. 555-577, 1988.

Bergadano, F., Giordana, A., Saitta, L., De Marchi D. and Brancadori, F., "Integrated Learning in a Real Domain," in B.W. Porter and R.J. Mooney (Eds. ), Proceedings of the 7th International Machine Learning Conference, Austin, TX, 1990.

Bergadano, F. and Giordana, A., "Guiding Induction with Domain Theories," in Machine Learning: An Artificial Intelligence Approach Vollume 3, Y. Kodratoff and R.S. Michalski (Eds.), San Mateo, CA, Morgan Kaufmann, 1990.


OverviewOverview

Introduction



Basic references



Research Issues


MTL-JT: Multistrategy Task-adaptive Learning

based on Plausible Justification Trees

MTL-JT: Multistrategy Task-adaptive Learning

based on Plausible Justification Trees• Deep integration of learning strategies

Integration 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 strategies

The 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)

• Different types of knowledge pieces in the knowledge base

(e.g. facts, examples, implicative relationships, plausible determinations)


MTL-JT: assumptionsMTL-JT: assumptions

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 knowledge base so as to integrate new input information


Plausible justification treePlausible justification tree

P (a,b)

P (a,c) P (d) P (b)

P (a,f) P (g,a) P (b,e)P (h)

. . . . . . . . . . . .

P (b)

P (a,b)

...

} Input fact

} Facts from KB

Different types of inference (deductive, analogical, abductive, etc.)

n

o

u

4

q

1 2 i

v w

A plausible justification tree is like a proof tree, except that some of individual inference steps are deductive, while others are non-deductive or only plausible (e.g. analogical, abductive, inductive).


Learning methodLearning 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 to entail Tu

• For each new positive example Ii:- generalize Tu so as to cover a plausible justification tree of Ii- generalize the KB 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 to entail the new Tu without entailing the previous Tu

• Learn different concept definitions:- extract different concept definitions from the general justification tree Tu


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

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

Plausible determination:rainfall(x, y) >= water-in-soil(x, z)

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

grows(x, rice)

MTL-JT: illustration from GeographyMTL-JT: illustration from Geography

Knowledge Base


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

Positive Example 1:rainfall(Thailand, heavy) & climate(Thailand, tropical) & soil(Thailand, red-soil) & terrain(Thailand, flat) & location(Thailand, SE-Asia) grows(Thailand, rice)

Positive Example 2:rainfall(Pakistan, heavy) & climate(Pakistan, subtropical) & soil(Pakistan, loamy) & terrain(Pakistan, flat) & location(Pakistan, SW-Asia) grows(Pakistan, rice)

Negative Example 3:rainfall(Jamaica, heavy) & climate(Jamaica, tropical) & soil(Jamaica, loamy) & terrain(Jamaica, abrupt) & location(Jamaica, Central-America) ¬ grows(Jamaica, rice)


Build a plausible justification of the first exampleBuild 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:rainfall(Thailand, heavy) & soil(Thailand, red-soil) & terrain(Thailand, flat) & location(Thailand, SE-Asia) & climate(Thailand, tropical) grows(Thailand, rice)








analogy



deduction


Example 1:rainfall(Thailand, heavy) & soil(Thailand, red-soil) & terrain(Thailand, flat) & location(Thailand, SE-Asia) &climate(Thailand, tropical) grows(Thailand, rice)

Justify the inferences from the above tree: analogy










analogy



deduction



Justify the inferences from the above tree: deduction

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

grows(x, rice)








analogy



deduction



Justify the inferences from the above tree: inductive prediction & abduction



Multitype generalizationMultitype 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)|||



GENERALIZATION

grows(x1, rice)




DEDUCTIVE




ANALOGY





Justify the generalizations from the above tree:

generalization based on analogy





GENERALIZATION

grows(x1, rice)




DEDUCTIVE




ANALOGY





Justify the generalizations from the above tree:

Inductive generalization



Build the plausible generalization Tu of TBuild 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)



ANALOGY



Positive example 2Positive example 2

Instance of the current Tu corresponding to Example 2

water-in-soil(Pakistan, high) temperature(Pakistan, warm) soil(Pakistan, fertile-soil)

rainfall(Pakistan, heavy) climate(Pakistan, tropical) soil(Pakistan, red-soil)

grows(Pakistan, rice)

analogy deduction

d e d u c t i o n


falsefalse

Plausible justification tree T2 of Example 2:

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

rainfall(Pakistan, heavy) climate(Pakistan, subtropical)


analogy deduction

d e d u c t i o n

soil(Pakistan, fertile-soil)

soil(Pakistan, loamy)

deduction


Positive example 2Positive example 2

The explanation structure S2:

The new Tu:

generalization

grows(x1, rice)

water-in-soil(x1, high)

temperature(x1, warm)


rainfall(x1, heavy)


soil(x1, red-soil) soil(x3, loamy)


temperature(x2, warm)

climate(x2, subtropical)

deductive

generalizationdeductive

___

___

grows(x, rice)

deductive generalization

generalizationbased on analogy

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)

or


or inductive deductivegeneralization

deductivegeneralization

deductivegeneralizationgeneralization


Negative example 3Negative example 3

Instance of Tu corresponding to the Negative Example 3:

The new Tu:

analogydeduction

d e d u c t i o n

deduction

water-in-soil(Jamaica, high) temperature(Jamaica, warm) soil(Jamaica, fertile-soil)

rainfall(Jamaica, heavy)

climate(Jamaica, tropical)

soil(Jamaica, loamy)

grows(Jamaica, rice)

soil(Jamaica, red-soil)

orfalse

inductiveprediction

climate(Jamaica, subtropical)

or

false

deduction

water-in-soil(x, high)

rainfall(x, heavy) terrain(x, flat)

grows(x, rice)



temperature(x, warm) soil(x, fertile-soil)

climate(x, subtropical)soil(x, red-soil) soil(x, loamy)

or


or inductive deductivegeneralizationdeductive


generalization

generalization inductivespecialization


The plausible generalization tree

corresponding to the three input examples

The plausible generalization tree

corresponding to the three input examples

GENERALIZATIONS

grows(x, rice)



rainfall(x, heavy)

climate(x, subtropical)

soil(x, red-soil)

soil(x, loamy)

terrain(x, flat)

DEDUCTIVE


OROR



GENERALIZATIONDEDUCTIVEANALOGY


SPECIALIZATION INDUCTIVE


Learned knowledgeLearned knowledge

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

water-in-soil(Thailand, high) temperature(Thailand, warm) soil(Thailand, fertile-soil)

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

analogy deductionabduction

d e d u c t i o n



water-in-soil(Pakistan, high) temperature(Pakistan, warm) soil(Pakistan, fertile-soil)

rainfall(Pakistan, heavy) climate(Pakistan, tropical) soil(Pakistan, red-soil)


analogy deduction

d e d u c t i o n


falsefalse

Why is it reasonable to consider these facts to be true?



water-in-soil(Thailand, high) temperature(Thailand, warm) soil(Thailand, fertile-soil)

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

analogy deductionabduction

d e d u c t i o n




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




Specialized plausible determination:rainfall(x, y) & terrain(x, flat) >= water-in-soil(x, z)

GENERALIZATIONS

grows(x, rice)



rainfall(x, heavy)

climate(x, subtropical)

soil(x, red-soil)

soil(x, loamy)

terrain(x, flat)

DEDUCTIVE


OROR



GENERALIZATIONDEDUCTIVEANALOGY


SPECIALIZATION INDUCTIVE

Positive Example 1:rainfall(Thailand, heavy) & climate(Thailand, tropical) & soil(Thailand, red-soil) & terrain(Thailand, flat) & location(Thailand, SE-Asia) grows(Thailand, rice)

Positive Example 2:rainfall(Pakistan, heavy) & climate(Pakistan, subtropical) & soil(Pakistan, loamy) & terrain(Pakistan, flat) & location(Pakistan, SW-Asia) grows(Pakistan, rice)

Negative Example 3:rainfall(Jamaica, heavy) & climate(Jamaica, tropical) & soil(Jamaica, loamy) & terrain(Jamaica, abrupt) & location(Jamaica, Central-America) ¬ grows(Jamaica, rice)


Learned knowledge: concept definitionsLearned knowledge: concept definitions

Operational definition of "grows(x, rice)":rainfall(x,heavy) & terrain(x,flat) & [climate(x,tropical) climate(x,subtropical)] & [soil(x,red-soil) soil(x,loamy)]

grows(x, rice)

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

grows(x, rice)

water-in-soil(x, high)


grows(x, rice)



temperature(x, warm) soil(x, fertile-soil)

climate(x, subtropical)soil(x, red-soil) soil(x, loamy)

or


or inductive deductivegeneralizationdeductive


generalization

generalization inductivespecialization


Learned knowledge: example abstractionLearned knowledge: example abstraction

Abstraction of Example 1:water-in-soil(Thailand, high) & temperature(Thailand, warm) & soil(Thailand, fertile-soil) grows(Thailand, rice)






analogy



deduction



Features of the MTL-JT method and referenceFeatures of the MTL-JT method and reference

• 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

• Exhibits synergistic behavior

• May behave as any of the integrated strategies

Tecuci, G., "An Inference-Based Framework for Multistrategy Learning," in Machine Learning: A Multistrategy Approach Volume 4, R.S. Michalski and G. Tecuci (Eds.), San Mateo, CA, Morgan Kaufmann, 1994.


Features of the MTL-JT methodFeatures of the MTL-JT method

• Integrates dynamically different elementary inferences


GENERALIZATION

grows(x, rice)




DEDUCTIVE




ANALOGY



Features of the MTL-JT methodFeatures of the MTL-JT method

Justify the following features:

• May behave as any of the integrated strategies

Explanation-based learningMultiple-example explanation-based learning Learning by abductionLearning by analogyInductive learning from examples

What strategies should we consider for the presented illustration of MTL-PJT?


MTL-JT as explanation-based learningMTL-JT as explanation-based learning

GENERALIZATION

grows(x, rice)




DEDUCTIVE




ANALOGY


x, rainfall(x, heavy) water-in-soil(x, high)x, soil(x, red-soil) soil(x, fertile-soil)

Assume the KB would contain the knowledge:


MTL-JT as abductive learningMTL-JT as abductive learning

x, rainfall(x, heavy) water-in-soil(x, high)

Assume the KB would contain the knowledge:






analogy



deduction



MTL-JT as inductive learning from examplesMTL-JT as inductive learning from examples


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

location(Thailand, SE-Asia)

Positive Example 1:


rainfall(Pakistan, heavy)climate(Pakistan, subtropical) soil(Pakistan, loamy)

location(Pakistan, SW-Asia)

Positive Example 2:

terrain(Thailand, flat)

terrain(Pakistan, flat)


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

location(Jamaica, Central-America)

terrain(Jamaica, abrupt)

Negative Example 3:

grows(x, rice)


Empirical Generalization:

¬

[soil(x, red-soil) or soil(x, loamy)][climate(x, tropical) or climate(x, subtropical)]

[location(x, SE-Asia) orlocation(x, SW-Asia)]


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

location(Thailand, SE-Asia)

Positive Example 1:


rainfall(Pakistan, heavy)climate(Pakistan, subtropical) soil(Pakistan, loamy)

location(Pakistan, SW-Asia)

Positive Example 2:

terrain(Thailand, flat)

terrain(Pakistan, flat)


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

location(Jamaica, Central-America)

terrain(Jamaica, abrupt)

Negative Example 3:

grows(x, rice)


Empirical Generalization:

¬

[soil(x, red-soil) or soil(x, loamy)][climate(x, tropical) or climate(x, subtropical)]

[location(x, SE-Asia) orlocation(x, SW-Asia)]


MTL-JT as analogical learningMTL-JT as analogical learning

Let us suppose that the KB contains only the following knowledge that is related to Example 1:

Facts: rainfall(Philippines, heavy), water-in-soil(Philippines, high)Determination: rainfall(x, y) --> water-in-soil(x, z)

Then the system can only infer that "water-in-soil(Thailand, high)", by analogy with "water-in-soil(Philippines, high)".

In this case, the MTL-JT method reduces to analogical learning.


OverviewOverview

Introduction



Basic references



Research Issues


Research issues in multistrategy learningResearch issues in multistrategy learning

• Comparisons of learning strategies

• New ways of integrating learning strategies

• Synergistic integration of a wide range of learning strategies

• The representation and use of learning goals in multistrategy systems

• Dealing with incomplete or noisy examples

• Evaluation of the certainty of the learned knowledge

• General frameworks for multistrategy learning

• More comprehensive theories of learning

• Investigation of human learning as multistrategy learning

• Integration of multistrategy learning and knowledge acquisition

• Integration of multistrategy learning and problem solving


ExerciseExercise

Compare the following learning strategies:-Rote learning-Inductive learning from examples-Explanation-based learning-Abductive learning-Analogical learning-Instance-based learning-Case-based learningFrom the point of view of their input, background knowledge, type of inferences performed, and effect on system’s performance.


ExerciseExercise

Identify general frameworks for multistrategy learning, based on the multistrategy learning methods presented.


Basic referencesBasic references

• Proceedings of the International Conferences on Machine Learning, ICML-87, … , ICML-04, Morgan Kaufmann, San Mateo, 1987- 2004.

• 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.), Morgan Kaufmann, San Mateo, 1994.

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