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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)
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
Dept. of Computer Science HY577 – Machine Learning EBLUniversity of Crete Fall 2000 course
Inductive vs AnalyticalAnalytical Learning
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prototype cases
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
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
Dept. of Computer Science HY577 – Machine Learning EBLUniversity of Crete Fall 2000 course
EBG: The Basic Entities
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THEORY REVISION
Dept. of Computer Science HY577 – Machine Learning EBLUniversity of Crete Fall 2000 course
EBG: The Basic Entities -2
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EBG
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)
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
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
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)
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
Dept. of Computer Science HY577 – Machine Learning EBLUniversity of Crete Fall 2000 course
ILP systems
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