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

1999/10/1 Li-we Pan 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

1999/10/1 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)

1999/10/1 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 EH m(a)Ψ+ T CXT E ├ m(a); m(a)Ψ- T CXT E ├ m(a);

1999/10/1 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

1999/10/1 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 :

1. input case m(a) & retrieval solutions m(b) has a different value

2. Only input case m(a) has value

1999/10/1 Li-we Pan 7

Heuristic estimate• Let :

– ρ: the estimated cost of inconsistency removal– γ: that of explanation construction

1. 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

2. 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

1999/10/1 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)

1. 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

2. 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

1999/10/1 Li-we Pan 9

Cont.

3. Bound Computation. For every case C CC2 compute

a pair [hlC, hu

C], SjSOL hlC <= h(Sj)<= hu

C

– Computations of bounds on the adaptation estimates of solutions of cases

4. Bound-Based Pruning. Restrict CC2 to CC3 by

removing every case C such that hlC>a, a = minchu

C

– Reject cases which have definitively no solutions with minimal estimate

1999/10/1 Li-we Pan 10

Cont.5. Pivoting. (…)

– No deep investigations on the solutions of the case is performed

1999/10/1 Li-we Pan 11

Comparison PBR vs. Naive Retrieval

1999/10/1 Li-we Pan 12

PBR vs. E-MOP Retrieval

1999/10/1 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

1999/10/1 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?