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Selecting Most Adaptable Diagnostic Solutions through Pivoting-Based Retrieval

Selecting Most Adaptable Diagnostic Solutions through Pivoting-Based Retrieval. Luigi Portinale, Pietro Torasso and Diego Margo. Teacher : C.S. Ho Student : L.W. Pan No. : M8702048 Date : 10/1/99. Why need more retrieval. Before: aim at highest similarity (surface feature)

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Selecting Most Adaptable Diagnostic Solutions through Pivoting-Based Retrieval

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  1. Selecting Most Adaptable Diagnostic Solutions through Pivoting-Based Retrieval Luigi Portinale, Pietro Torasso and Diego Margo Teacher : C.S. Ho Student : L.W. Pan No. : M8702048 Date : 10/1/99

  2. Why need more retrieval • Before: aim at highest similarity(surface feature) • Now : adaptation estimation & adaptation effort(trade off) • Can prune non-adaptable or hard to adapt cases • An approach to adaptation-guided retrieval based on a tight integration between adaptation effort estimation and retrieval of past diagnostic solutions Li-we Pan

  3. On the Adaptation of Diagnoses • ADAPtER : a diagnostic system integrating a formal theory of model-based diagnosis with CBR. • Def1: a diagnostic problem is a tuple • DP = <<T,H>,CXT,<Ψ+,Ψ->> • T : a set of logical formulae representing the behavioral model of the system to be diagnosed • H : a set of diagnostic hypotheses • CXT : the set of contextual information of the problem • Ψ+,Ψ-: the set of manifestation to be accounted for (covered) Li-we Pan

  4. Cont. • OBS:the set of observed manifestations’ • Ψ+OBS, Ψ-= {m(a)|m(b)OBS,b≠a} • MANA:abnormal manifestations • MANN:normal manifestations • Ψ+=OBSA,OBSA=MANA∩OBS • Def2: • DP = <<T,H>,CXT,<Ψ+,Ψ->> • A diagnosis a set EH • m(a)Ψ+ T  CXT  E ├ m(a); • m(a)Ψ- T  CXT  E ├ m(a); Li-we Pan

  5. Estimating Adaptation • Stored case is represented as the tuple C=<CXTall, CXTsome, OSB, SOL> • CXTall:the set of contexts relevant to every solutions of the cases; • CXTsome:the set of contexts relevant to some(but not all) solutions of the cases; • OBS:the set of manifestations observed in the case • SOL=<<H1,EXPL(H1,CXT1,)>,…,< Hn, EXPL(Hn ,CXTn)>> is the list of solutions • Hj : a set of diagnostic hypotheses • CXTj : the set of context relevant to the j-th solution • EXPL() : the derivational trace form Hj and CXTj observable features Li-we Pan

  6. How to estimate • Input case: CI =<CXTI,OBSI> • Retrieval solution Sj = <Hj,EXPL(Hj,CXTj)> • (compare CI and Sj) • Compare CXTI with CXTj • Manifestations in OBSI with those in EXPL(Hj,CXTj) • Context : Slightly or totally incompatible • Manifestation : • input case m(a) & retrieval solutions m(b) has a different value • Only input case m(a) has value Li-we Pan

  7. Heuristic estimate • Let : • ρ: the estimated cost of inconsistency removal • γ: that of explanation construction • αCONFLICT(m(a)) = • ρ +γ if m(a) to be covered and m(b) supported • γ if m(a) to be covered and m(b) not supported • ρ if m(a) not to be covered and m(b) supported • 0 otherwise • αNEW(m(a)) = • γ if m(a) to be covered • 0 otherwise • h(Sj) =ΣαCONFLICT(m(a))+ΣαNEW(m(a))+δ|SI(Sj)| • SI(Sj) : the set of contexts of solution Sj slightly incompatible with CXTI • δ: the adaptation weight assigned to them Li-we Pan

  8. The PBR Algorithm • Input : a case C1 = <CXTI,OBSI> • Output : a set of solutions Sj = <Hn,EXPL(Hn,CXTn)> with minimal h(Sj) • Filtering. Construct a first set CC1 of candidate cases by following indices • Only cases having at least one feature in common with the input case • Context-Based Pruning. Restrict the set CC1 into the set CC2 by removing each case C such that there is a context in CXTall totally incompatible with a context in CXTI • Rejecting cases having in all their solutions contextual information conflicting with the input one Li-we Pan

  9. Cont. • Bound Computation. For every case C  CC2 compute a pair [hlC, huC], SjSOL hlC <= h(Sj)<= huC • Computations of bounds on the adaptation estimates of solutions of cases • Bound-Based Pruning. Restrict CC2 to CC3 by removing every case C such that hlC>a, a = minchuC • Reject cases which have definitively no solutions with minimal estimate Li-we Pan

  10. Cont. • Pivoting. (…) • No deep investigations on the solutions of the case is performed Li-we Pan

  11. Comparison PBR vs. Naive Retrieval Li-we Pan

  12. PBR vs. E-MOP Retrieval Li-we Pan

  13. Conclusion • Simple memory organization avoiding the space problems of more complex organizations like E-MOP • Allow one to obtain the best possible accuracy in terms of adaptation effort estimate • Retrieval time is considerably reduced by the combination of pivoting and pruning techniques Li-we Pan

  14. program • Utility : the match rate(hit features/total feature) • EU : ΣP x EUnext • P : ? (domain similarity) • Adaptation knowledge : • If (query value –case’s value) / case’s value >= 95% • Then can adaptability • Else cannot adaptability • Adapt method : replace • Question : each input case(query) need rebuild the tree? Li-we Pan

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