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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
EBL: Explanation Based Learning
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EBL: Knowledge-Intensive LearningEBL = Analytical Learning (vs.
inductive learning)
EBL: The OriginsEBG: Explanation Based GeneralizationEBG: The
Basic EntitiesAn example – EBG: The Operations
EBL & Theory Revision: Combining EBL with inductive
learningThe NEITHER theory revision system
EBL & ILP: Inductive Logic Programming (… based
Learning)
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
EBL: Knowledge-Intensive Analytical Learning
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EBL generalize from single example by analyzing whythat example
is an instance of the target/ goal concept
EXPLAIN
� The Explanation identifies the RelevantRelevant features of
the example which constitute SufficientSufficient conditions for
describing the target concept
� The main power of EBL rests on the use of a DOMAIN
THEORYDOMAIN THEORYto drive the analysis process
Knowledge Intensive LearningAnalyticalAnalytical
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
Inductive vs AnalyticalAnalytical Learning
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prototype cases
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
Learning: What we would like
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
STRIPS: The Origins of EBL
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Fikes, Hart, Nilsson, 1972
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
EBG - Explanation Based Generalization: The ‘mature’ EBL
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Mitchell, et.al., 1986
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
EBG: The Basic Entities
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THEORY REVISION
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
EBG: The Basic Entities -2
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EBG
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
An Analytical Generalization Example
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
EBG: The Operations
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
EBG - The Basic Steps: Compute & Store a Proof
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course 11/
EBG -The Basic Steps: REGRESSION (= generalize/refine the
proof)
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
Regression: example
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
Combining EBL/EBG with Inductive Learning
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… by FOCUSING on the structured proofsand identifying conditions
(rules) toRemove/ Extend/ Add-on the proofs
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
NEITHER: An Efficient & Effective Theory Revision System
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
NEITHER: The Operations
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
ILP: Inductive Logic Programming
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ILP
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
Induction as Inverted Deduction
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
Deduction as … Resolution
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Deduction … as resolutionP V LnotL V R----------P V R
Induction is, in fact, the inverse operation of deduction, and
cannot be conceived to existwithout the corresponding operation.
Who thinks of asking whether addition or subtraction is the more
important process in arithmetic? But at the same time much
difference in difficulty may exist between a direct and inverse
operation; : : : it must be allowed that inductive investigations
are of a far higher degree of difficulty and complexity than any
questions of deduction …
(Jevons 1874)
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
Resolution & Inverting it …
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1. Given initial clauses C1 and C2, find a literal L from clause
C1 such that notL occurs in clause C2
2. Form the resolvent C by including all literals from C1 and
C2,expect for L and notL. More precisely, the set of literals
occurringin the conclusion C is:
C = (C1 – {L}) U (C2 – {notL})
Inverting Resolution: Example
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Dept. of Computer Science HY577 – Machine Learning EBLUniversity
of Crete Fall 2000 course
ILP systems
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