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

2. 1999/10/1Li-we Pan2 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

3. 1999/10/1Li-we Pan3 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 =,CXT, > 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)

4. 1999/10/1Li-we Pan4 Cont. –OBS:the set of observed manifestations’ Ψ +  OBS, Ψ - = {m(a)|m(b)  OBS,b≠a} –MAN A :abnormal manifestations –MAN N :normal manifestations Ψ + =OBS A,OBS A =MAN A ∩OBS Def2: –DP =,CXT, > –A diagnosis a set E  H –  m(a)  Ψ + T  CXT  E ├ m(a); –  m(a)  Ψ - T  CXT  E ├ m(a);

5. 1999/10/1Li-we Pan5 Estimating Adaptation Stored case is represented as the tuple C= –CXT all :the set of contexts relevant to every solutions of the cases; –CXT some :the set of contexts relevant to some(but not all) solutions of the cases; –OBS:the set of manifestations observed in the case –SOL=,…, > is the list of solutions H j : a set of diagnostic hypotheses CXT j : the set of context relevant to the j-th solution EXPL() : the derivational trace form H j and CXT j observable features

6. 1999/10/1Li-we Pan6 How to estimate Input case: C I = Retrieval solution S j = (compare C I and S j ) –Compare CXT I with CXT j –Manifestations in OBS I with those in EXPL(H j,CXT j ) Context : Slightly or totally incompatible Manifestation : 1.input case m(a) & retrieval solutions m(b) has a different value 2.Only input case m(a) has value

7. 1999/10/1Li-we Pan7 Heuristic estimate Let : –ρ: the estimated cost of inconsistency removal –γ: that of explanation construction 1.α CONFLICT (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 2.α NEW (m(a)) = –γ if m(a) to be covered –0 otherwise h(Sj) =Σα CONFLICT (m(a))+Σα NEW (m(a))+δ|SI(S j )| –SI(S j ) : the set of contexts of solution S j slightly incompatible with CXT I –δ: the adaptation weight assigned to them

8. 1999/10/1Li-we Pan8 The PBR Algorithm Input : a case C1 = Output : a set of solutions S j = with minimal h(Sj) 1.Filtering. Construct a first set CC 1 of candidate cases by following indices Only cases having at least one feature in common with the input case 2.Context-Based Pruning. Restrict the set CC 1 into the set CC 2 by removing each case C such that there is a context in CXT all totally incompatible with a context in CXT I Rejecting cases having in all their solutions contextual information conflicting with the input one

9. 1999/10/1Li-we Pan9 Cont. 3.Bound Computation. For every case C  CC 2 compute a pair [h l C, h u C ],  S j  SOL h l C <= h(S j )<= h u C –Computations of bounds on the adaptation estimates of solutions of cases 4.Bound-Based Pruning. Restrict CC 2 to CC 3 by removing every case C such that h l C >a, a = min c h u C –Reject cases which have definitively no solutions with minimal estimate

10. 1999/10/1Li-we Pan10 Cont. 5.Pivoting. (…) –No deep investigations on the solutions of the case is performed

11. 1999/10/1Li-we Pan11 Comparison PBR vs. Naive Retrieval

12. 1999/10/1Li-we Pan12 PBR vs. E-MOP Retrieval

13. 1999/10/1Li-we Pan13 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

14. 1999/10/1Li-we Pan14 program Utility : the match rate(hit features/total feature) EU : ΣP x EU next 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?