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To cite this version:
Ben Amor, Nahla and Dubois, Didier
and Gouider, Héla and Prade, Henri Possibilistic preference
networks. (2018) Information Sciences, 460-461. 401-415.
ISSN 0020-0255
Official URL
DOI :
https://doi.org/10.1016/j.ins.2017.08.002
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Possibilistic
preference
networks
s
Nahla
Ben
Amor
a,
Didier
Dubois
b, ∗,
Héla
Gouider
a,
Henri
Prade
ba LARODEC, ISG de Tunis, 41 rue de la Liberté, Le Bardo 20 0 0, Tunisia b IRIT, UPS-CNRS, 118 route de Narbonne, Toulouse Cedex 09, 31062 France
Keywords: Preference modeling Pareto ordering Graphical models Possibility theory a b s t r a c t
This paper studies the use of product-based possibilistic networks for representing preferences in multidimensional decision problems. This approach uses symbolic possibility weights and defines a partial preference order among solutions to a set of conditional preference statements on the domains of discrete decision variables. In thecase ofBoolean decision variables, this partial ordering isshown to be consistentwith thepreference orderinginducedby theceterisparibus assumptionadopted inCP-nets. Namely,by completngthe possibilistic net ordering with suitable constraints between products of symbolicweights, all CP-net preferences can be recovered. Computing procedures for comparing solutions are provided. The flexibility and representational powerof theapproachisstressed.
1. Introduction
Modelingpreferencesisessentialinanydecisionanalysistask.However,gettingthesepreferencesbecomesnontrivialas soonasalternativesaredescribedbyaCartesianproductofmultiplefeatures.Indeed,thedirectassessmentofapreference relationbetween these alternatives isusually not feasibledueto itscombinatorial nature.Fortunately,thedecision maker can express contextual preferences that exhibit some independence relations,which allowsus to represent her/his prefer-encesinacompact manner.Moreover, graphicalrepresentationsfacilitate preferenceelicitation,aswellastheconstruction ofan orderingfrom these contextuallocal preferences.This useof graphicalpreference representations hasbeen inspired bythesuccessofBayesiannetworksasacomputationallytractableuncertaintymanagementdevice[1].
The useofpossibilisticnetworks forrepresenting conditionalpreference statementson discrete variableshasbeen pro-posedonly recently. The approach uses non-instantiatedpossibility weights to defineconditional preference tables. More-over,additionalinformationabouttherelativestrengthsofthesesymbolic weightscanbetakenintoaccount. Thefactthat atbestwehave someinformationabouttherelativevaluesoftheseweightsacknowledgesthequalitativenature of prefer-encespecification.Theseconditionalpreferencetablesgivebirthtovectorsofsymbolicweights thatreflect thepreferences thataresatisfiedandthosethatareviolatedinaconsideredsituation.Thecomparisonofsuchvectorsmayrelyondifferent orderings:the onesinducedbythe product-based,ortheminimum-based chainrule underlying thepossibilisticnetwork. Athoroughstudy oftherelationsbetweentheseorderings inpresenceofvectorcomponentsthat aresymbolicratherthan numericalis presented.In particular, weestablish that theproduct-basedorderingand the symmetricPareto ordering
co-s Dedication : This paper is dedicated to Janusz Kacprzyk on the occasion of his jubilee. The second and the fourth author, who have a long and fruitful
research companionship with him, are glad to offer him this new piece of work.
∗ Corresponding author.
E-mail addresses: nahla.benamor@gmx.com (N. Ben Amor), dubois@irit.fr (D. Dubois), hela.gouider@gmail.com, gouider.hela@gmail.com (H. Gouider),
prade@irit.fr (H. Prade).
incide in presence of constraintscomparing pairs of symbolic weights. The paper highlights the merits of product-based possibilisticnetworksforrepresentingpreferences,inwhichcasetheyarecalled
π
-prefnets.Possibilisticpreferencenetworks(
π
-prefnets)belongtothefamilyofmethodsforthemodelingofpreferenceand deci-sionthatstemfromthefuzzysetandpossibilitytheoryliterature,variantsofwhicharefuzzyMarkoviandecisionprocesses studiedvery earlybyKacprzyk[2].JustlikeCP-netsmaybeused forflexiblequerying[3],itseemsthatπ
-prefnets might alsoservethispurpose(followingideasin[4]),atopicthat hasbeenalsomuchinvestigatedbyKacprzyk(e.g.,[5]).Inthispaper,wealsodiscussexistingrelationshipsbetween
π
-prefnetsandsomepreferencemodelsthat arerelatedto theminsome sense,namely, CP-nets[6],CP-theories [7],and OCF-nets[8].Indeed,π
-prefnets sharethesamepreference specificationandgraphicalstructureasCP-nets,CP-theoriesareageneralizationofCP-nets,whileOCF-netsarebasedon an additivestructurewhichparallelstheoneofπ
-prefnets.The paper is organized as follows. Section2 presents a symbolic graphical model for preferences based on possibility theoryandpossibilisticnetworks.Section3comparesvariouswaysoforderingthesolutionstoadecisionproblemexpressed byapossibilistic preferencenetwork. Section4compares possibilisticpreference networks withother qualitative graphical representationsofconditionalpreference,especiallyCP-nets.
Thispaperhas itsroots ina conferencepaper[9] and somewhat borrowsfrom anotherconference paperon the com-parison between different orderings that can be definedbetween configurations [10], and to a lesser extent from a third conferencepaperprovidingacomparativeoverview ofgraphicalpreferencestructures[11].
2. Introducingpossibilisticpreferencenetworks
Possibilisticconditionalpreferencenetworks,
π
-prefnetsforshort, areanovelmodelforrepresentingpreferences.They arebasedonpossibilisticnetworks[12,13].Thelatter areapossibilisticcounterpartofBayesiannetworks[1]inthecontext of possibility theory [14,15], which offers a setting for preferences representation. Weuse a set of conditional preference tablesexpressingpreferencesaboutthevaluesofvariablesconditionaltothevaluesofothervariables.Hereweassumethat such conditionalpreferences are represented byconditional possibility distributions. Moreover, as itis difficultto directly quantifypreference, weshallassumethat possibilityweights remainsymbolic(i.e.,non-instantiated)andthat wemayadd appropriatepreferenceconstraintsbetweensuchweightsiftheyareavailable.Inotherwords,π
-prefnets,likeCP-nets,are intendedtobeaqualitativepreferencerepresentationframework.2.1. Conditionalpossibility andpossibilistic networks
Beforedescribing
π
-pref nets in detail, we recall basic notions of possibility theory that will be useful in the sequel. Possibilitytheoryreliesonthenotionofapossibility distributionπ
[15],which isamapping fromauniverseofdiscourseÄ
totheunitinterval [0,1], orto anybounded totally orderedscale.This possibilisticscalecanbetheunitinterval when possibility values are the result of a clear measurement procedure, or an ordinal scale when values only reflect a total preorder betweenthedifferentelements ofÄ
.Weassume thatthepossibility distributionissuchthatπ (ω)
=1forsome element ofÄ
(∃
ω
∈Ä
such thatπ (ω)
=1). The possibility distributionπ
is then said to be normalized. When used to representuncertaintyaboutsomevariablextakingvaluesonÄ
,theassignmentπ (ω)
=0meansthatω
isfullyimpossible asavalueforx,whileπ (ω)
=1meansthatω
isfullypossible, i.e.,non-surprising.The occurrence of an event F⊆
Ä
is then associated with the possibility measure5(F
)
=supω∈Fπ (ω)
estimating itsplausibility, and with the dual necessity measureN(F
)
=1−5(
¯F)
=1−supω∈/Fπ (ω)
estimating itscertainty. The degree5
(F)evaluatesto whatextentF isconsistentwith theknowledgerepresentedbyπ
,whileN(F)evaluates atwhatlevelF is certainlyimpliedbyπ
.See[14] foranintroduction topossibilitytheory.ConditioninginpossibilitytheoryisdefinedfromtheBayesian-likeequation
5(F
∩G)
=5(F
|
G)
5(G
)
,whereisassociative,monotonicallyincreasinginthewidesenseand 1representstheidentityelementsuchthat 1
α
=α
.Inthispaper,standsfortheproductinaquantitativesetting(numerical),orfortheminimuminaqualitativesetting (ordinal).Namely,• if istheproduct,wegetastraightforwardcounterpartofconditionalprobability:
5(F
|
G)
=5(F
∩G)
5(G
)
providedthat5(G
)
>0;• if istheminimum,wegetaqualitativeversionofconditioning,that makessense onafinitepossibilityscale:
5(F
|
G)
=½
5(F
∩G)
if5(G
)
>5(F
∩G)
; 1if5(G
)
=5(F
∩G)
>0.Thetwodefinitionsofpossibilisticconditioning leadto twovariants ofpossibilisticnetworks:inthenumericalcontext, wecanexpressproduct-basednetworks,whileinthequalitativecontext,weonlyhavemin-basednetworks(alsoknown as qualitativepossibilisticnetworks) [12].
Let V=
{
A1,...,An}
be a set of n variables.Each variable Ai has a finitedomain DAi whose elements areai∈DAi. Theuniverse of discourse
Ä
={
ω
1,...,ω
|Ä|}
is the Cartesian product DA1× · · · ×DAn of domains of variables in V (so,|
Ä|
=|
DA1|
× · · · ×|
DAn|
, where|T|denotes thecardinality ofafiniteset T). Eachelementω
∈Ä
will becalledaconfiguration.ItcorrespondstoacompleteinstantiationofthevariablesinV.Apossibilisticnetwork hasadefinitionsimilartotheoneofa Bayesiannetwork.
Definition1(Possibilistic networks). [12,16] Apossibilisticnetwork over aset ofvariablesV ischaracterizedbytwo com-ponents:
(i) agraphical componentwhich isaDirectedAcyclicGraph G=(V, E)whereV isasetofnodesrepresenting variablesand
EasetofdirectededgesAi→Aj encodingconditional(in)dependenciesbetweenvariables.
(ii) avalued componentassociatingalocalnormalizedconditional possibilitydistribution
π
(Ai|p(Ai))toeachvariableAi∈V inthecontextofeachinstantiationp(Ai)ofitsparentsP(A
i)
={
Aj:Aj→Ai∈E}.Weassumethat
π
(Ai|p(Ai))>0inordertoavoidconditioningonavalueofpossibility0.Italsocomesdowntoassuming thatallconfigurationsaresomewhatpossible.Thisassumptionwillbeinnocuous inthemodelingofpreferences.Givenapossibilisticnetwork,wecancomputeajoint possibilitydistributionusingthefollowingchainrule:
π
(A
1,...,An)
=i=1.n5(A
i|
P(A
i))
. (1)When istheproduct, and no configurationis impossible,the conditionaltables canberetrievedfrom thejoint pos-sibility distribution obtained by thechain rule, usingthe same orderingof variables asin the original network.However this isno longer the case if is the minimum, as some conditional possibility values may belost when computing the minimuminthechainrule.
Originally,possibilisticnetworksweremeanttomodeluncertaintyandtocomputetheimpactofobservationsassigning values to some variables so as to predict the values of other variables of the network. In this paper, we advocate their interest in preference modeling rather than uncertainty management. Thus here
π
(ω
) should beunderstood as the level of satisfaction when choosing configurationω
. For a set of configurations F,5
(F) evaluates to what extent satisfying a constraint modeled by F is satisfactory, and N(F) evaluates to what extent this constraint is imperative. As we shall see, beyondtheirgraphicalappeal,conditionalpreferencepossibilisticnetworksprovideanaturalencodingofpreferences.Inthe following,weintroducethekindofpreferenceinformationneededtoconstructπ
-prefnets.Then,wepresentthedefinition ofπ
-prefnetsand explaintheirrepresentationalpower.2.2. Conditionalpreferencestatements
In qualitative preference models, usersare supposed to express their preferences underthe form ofcomparison state-mentsbetweeninstantiationsofavariable,conditionedbysomeotherinstantiatedvariables.Forinstance, intheparticular caseof Boolean variables, we deal with preferencesof the form:“a ispreferred to ¬a” ifthe preference isunconditional, and for conditional statements,inthe form “in thecontext where cis true, ais preferred to ¬a”, wherec correspondsto theinstantiationof(maybeseveral)othervariables.Moregenerally,
Definition 2. A preferencestatement (Ai, p(Ai), º)is a preference relationbetween values aik∈DA
i of a variableAi,
condi-tioned by the instantiationp(Ai) of a set P
(A
i)
of other variables, in the form of a complete preorder º on DAi. Namely
∀
aik,aim∈DAi,wehave
i) eitherp(Ai):aik≻aim,i.e.,inthecontextp(Ai),aikis preferredtoaim,
ii) orp(Ai):aik∼aim,i.e.,inthecontextp(Ai),oneisindifferentbetween aim andaik,
where≻isthestrictpartofº,and ∼ istheindifference partofº.If P
(A
i)
=∅, thenthepreferencestatementaboutAi isunconditional.
Note that wedonot allowincompletepreferencelocal specificationsintheformaikºaim.On eachvariabledomainDA i,
the user must choose between aik≻aim, aik≻aim and aik∼aim. It comes down to rating each possible instantiation of the variable Ai (whose domaincan benominal) on alocal totally ordered ordinal value scale,which isa usual assumption in multicriteriadecisionmaking.
The runningExample1,inspired from[6],illustratessuchpreferencestatements.
Example1. Considerapreferencespecificationabout aneveningsuit overthreedecisionvariablesV=
{
J,P,S}standingfor jacket, pants and shirt respectively, with values in DJ={
Red(
jr)
, Black(j
b)
}
, DP={
White(p
w)
, Black(p
b)
}
and DS={
Black(s
b)
, Red(s
r)
, White(s
w)
}
. Theconditionalpreferencesare giveninTable1.Preferencestatements (s1)and(s2)areunconditional. Note that theuser is indifferent between the values of thecolor of theshirt if hisjacket is blackand his pantsarewhite(inthecontext jbpw),whichisnotthecaseifhewearsaredjacketandblackpants.Indeed,heprefersred
Table 1
Conditional preference specifi- cation of Example 1 . ( s 1 ) j b ≻j r ( s 2 ) p b ≻ p w ( s 3 ) j b p b : s b ≻ s r ≻ s w ( s 4 ) j b p w : s w ≻ s b ≻ s r ( s 5 ) j r p b : s r ≻ s b ≻ s w ( s 6 ) j r p w : s b ∼ s r ∼ s w
Fig. 1. A possibilistic preference network.
2.3. Introducing
π
-prefnetsRepresenting the preference statements in a graphical way means that each node in the graph represents a decision variableAiwhichisassociated toasetoflocalconditionalpreferencestatements,conditionaltothevaluesofvariablesthat areitsparent nodesinthegraph.A(conditional)preferencenetworkcanbedefinedasfollows:
Definition 3. A preference network is a Directed Acyclic Network (DAG) (E, V) with nodes Ai, Aj∈V, s.t. each arc from Aj→Ai∈Eexpresses that thepreference about Ai depends on Aj. Each nodeAi is associated witha ConditionalPreference TableCPTi thatassociates preferencestatements (Ai,p(Ai),º)betweenthevaluesofAi, conditionaltoeachpossible instan-tiationp(Ai)oftheparents P
(A
i)
ofAi (ifany).Ina possibilisticpreference network,for each particularinstantiation p(Ai)of P
(A
i)
, thepreference orderbetween the valuesofAistatedbytheuserwillbeencodedbyalocalconditionalpossibilitydistributionexpressedbymeansofsymbolicweights. By a symbolic weight, we mean a symbol representing a strictly positive real number in (0, 1] whose value is unspecified.Werelyonsymbolicweights intheabsenceofavailablenumericalvalues.Moreformally, wehave:
Definition4. (ConditionalPreferencePossibilisticnetwork(
π
-prefnet))Apossibilisticpreferencenetworkbasedonoperation(−
π
-prefnet)5G
over aset V={
A1,...,An}
of decision variablesis a preference network where each localprefer-encerelation atnodeAi isassociated witha symbolicconditionalpossibility distribution (
π
i-table forshort), encoding theorderingbetweenthevaluesofAisuchthat: (i) If p(Ai
)
:ai≺a′i then
π (a
i|
p(Ai))
=α
,π (a
′i|
p(Ai))
=β
whereα
andβ
aresymbolicweights,and 0<α
<β
≤1;(ii) If p(Ai
)
:ai∼ai′ thenπ (a
i|
p(Ai))
=π (a
′i|
p(Ai))
=α
>0whereα
isasymbolicweightsuchthatα
≤1;(iii) For eachinstantiation p(Ai)ofP
(A
i)
,∃
ai∈DAi suchthatπ (a
i|
p(Ai))
=1.(iv) Asymbolicdegreeofpossibilityisassignedtoeachconfiguration
ω
usingthechainrule(1)basedon.LetC0bethesetstoringtheconstraintsbetweenthesymbolicpossibilityweightspertainingtoeachpreferencestatement (Ai,p(Ai),º),encodingthecompletepreorderingº.Inadditiontothesepreferencesencodedbya
π
-prefnet,additional con-straintscanbetakenintoaccount.Suchconstraints,formingasetdenotedbyC1,mayexpressthatsomeweightspertaining toonepreferencestatementareequalto,orgreaterthan,weightspertainingtoanotherpreferencestatement.LetC=C0∪C1 bethesetofallconstraints.Incaseoneneedstocomparetwoweightsα
andβ
,onecheckifthereisanyrelationbetween themstoredinC,orifonecaninferitbytransitivityfromC;otherwisetheyareconsideredasincomparable.Example2. Considerthepreferencespecificationabout anevening suitofExample1.Itscorresponding
π
-prefnetand the conditional possibility weights are given by Fig.1. The graph is built based on Definition3. In fact, since P(J)
=P(P
)
=∅ the two variables J and P are independent, while S depends on J and P since the preference statements associated to Sareconditioned by P
(S)
={
J,P}
.The constraints betweensymbolic weights inherent from thepreference specification are representedbythesetC0 suchthat C0={
(δ
1>δ
2)
,(θ
1>θ
2)
,(λ
1>λ
2)
}
.Table 2
Vectors associated to each configura- tion of Example 2 .
configurations Symbolic vectors
J P S jb p b s b (1, 1, 1) jb p b s r (1, 1, δ1 ) jb p b s w (1, 1, δ2 ) jb p w s b (1, β, θ1 ) jb p w s r (1, β, θ2 ) jb p w s w (1, β, 1) jr p b s b (α, 1, λ1 ) jr p b s r (α 1, 1) jr p b s w (α, 1, λ2 ) jr p w s b (α, β, 1) jr p w s r (α, β, 1) jr p w s w ( α, β, 1)
Aset ofconditionalpreferencetablesencodedasa
π
-prefnetdeterminesapartial orderamongconfigurations.Indeed, eachconfigurationhasasatisfactionlevelencodedbyapossibilitydegreecomputedbymeansofthepossibilisticchainrule(1).Thisleadsustothefollowingdefinitionoftheinducedpreferenceorderingon configurations.
Definition5(Preferenceordering). Consider asymbolicpossibilistic preferencenetwork
5G
and aset C ofconstraints be-tweenthesymbolic weights.Letω
andω
′ betwo configurationsinÄ
, andπ
5G(ω
)(resp.π
5G(ω
′))bethesymbolicpos-sibility degree of
ω
(resp.ω
′) computed by(1). Then, configurationω
is weakly preferred toω
′, denoted byω
ºω
′, iffπ
5G(ω
)≥π
5G(ω
′).Inthedefinition,
π
5G(ω
)isacombinationofsymbolicweightsusing.So,π
5G(ω
)≥π
5G(ω
′)(resp.π
5G(ω
)>π
5G(ω
′),π
5G(ω)
=π
5G(ω
′)
) should beunderstood asfollows: this inequality(resp. strict inequality, equality)holds whatever thenumerical instantiations of the weights involved in the possibility values, in agreement with constraints in C. This is re-spectively denoted by
ω
ºω
′,ω
≻ω
′ andω
∼ω
′. When it isnot possible to prove aninequality betweenπ
5G(ω
) andπ
5G(ω
′), because it ispossible to have strict inequalities in bothdirections by substituting distinct numerical values, weinterpretthissituationintermsofincomparability asalready said,andthisisdenotedby
ω
±ω
′.Since we use symbolic weights, adefinite preference between all configurationscannot be established (as long aswe do notinstantiate allsymbolic weights). Each configuration
ω
=a1...an can alsobeassociated with avector(α
1,...,α
n)
,where
α
i=π (a
i|
p(Ai))
and p(Ai)
=ω
[P(A
i)
], whereω
[P(A
i)
]istherestriction oftheconfigurationω
totheparentsofAi.For instance, vectors associated to thepreference possibilistic network of Example2 are given in Table2. Thus, comparing configurationsamountstocomparingvectorsofsymbolicweights attachedto configurations,and theuseofthechainrule isjustonewayofcomparingsuchvectors, amongotheronesasdiscussedinthenextsection.However,notethatsymbolic weightsattachedtoavariabledependontheinstantiationsofitsparents.
3. Onvarious waysoforderingconfigurationsinduced byconditionalpreferencenetworks
Intheprevioussection,wehaveshownhowtoencodethepreferencespecificationsinapossibilisticnetworkformatand wehavedefinedapartialorderingonconfigurationsbasedoncomparingexpressions involvingsymbolicweightscombined with operation. In contrast wemay alsocompare vectors ofsymbolic weights representing the local satisfactionof the conditionaltablesfor configurations.Inthissectionwewill firstcompare thepartial orderrelationsbased onproduct and minimum definedabove, and thenclassical vector comparisontechniques such asPareto and symmetric Pareto orderings withthepurposetousethemtogenerateameaningfulorderingoverconfigurations.
3.1. Orderingrelationsforsymbolicvectors
A symbolicvectoron [0, 1] isof theform
α
=(α
1,. . .,α
n)
. Viewed asvectors ofratingsofconfigurations a1...an, therangeof each component
α
i=π (a
i|
p(Ai)
is either the open interval (0, 1), orreduces to {1} (ifπ (a
i|
p(Ai)
=1). In otherwords,thevectorcontains1’sand/orsymbolsstandingforunknownpositivevaluesstrictlylessthan 1.
Definition6. Orderingrelationsbetween symbolicvectors
α
=(α
1,...,α
n)
andβ
=(β
1,...,β
n)
based onanoperation , thatcanbetheminimumortheproduct,aredefinedasfollows:•
α
≻β
iff ni=1α
i>ni=1β
i•
α
ºβ
iff ni=1α
i≥ ni=1β
i•
α
∼β
iffα
ºβ
andβ
ºα
When=min(resp.product),wewrite
α
≻minβ
(resp.α
≻prodβ
),andso onfortheother relations.In thisdefinition,
α
≻β
reallymeans that the inequality ni=1ai>nj=1bj holds for any choiceof numerical values aiand bj1 in therespective ranges of
α
i andβ
i, and likewiseforα
ºβ
usinga weak inequality.Actually we can drop the termsα
i andβ
j whose ranges are {1}. Note that the relationα
≻β
is then more demanding than the one defined by“
α
ºβ
and notβ
ºα
”,since inthe former we requesta strictinequality for allinstantiationsby numericalvalues. Thelatteronlyrequires
α
ºβ
and∃
ai,bj:i:αi6=1ai>j:βi6=1bj.Finally,α
±β
standsfor∃
ai,bj,a′ i,b′ii:αi6=1ai>j:βi6=1bjand i:α i6=1a ′ i<j:βi6=1b ′ j.
Wealsoconsider thefollowingclassicalrelations,expressedinthesymboliccase: Definition7(Pareto).
•
α
ºPβ
iff∀k
,α
k≥β
k.•
α
≻Pβ
iffα
ºPβ
and∃
k,α
k>β
k.•
α
∼Pβ
iffα
=β
.Note that with the type of symbolic vectors that we use,
α
k≥β
k may hold only if eitherα
k=1, orα
k=β
k, or1>
α
k>β
k inthesameconditionalpreferencetable,andα
k>β
k mayholdonlyifeitherα
k=1,β
k6=1,or1>α
k>β
k.Definition8(SymmetricPareto).
α
ºSPβ
iff thereexistsapermutationσ
ofthecomponentsofα
,yieldingavectorα
σ,such thatα
σºPβ
.Similardefinitionscanbewrittenfor
α
≻SPβ
,α
∼SPβ
.Inthenumericalsettingitiseasytoseethatα
≻Pβ
impliesα
≻SPβ
,which inturn implies
α
≻prodβ
. Besides,α
ºPβ
impliesα
ºSPβ
which implies bothα
ºprodβ
andα
ºminβ
. Butα
≻SPβ
onlyimplies
α
ºminβ
:for instance, (0.3, 0.8)≻SP(0.7,0.3), whilemin(
0.3,0.8)
=min(
0.7,0.3)
.Things changewhen weconsidervectorsofsymbolicweights.
3.2. Comparisonofproduct-basedandmin-basedorderingsinthesymbolicsetting
Inthefollowing,wepresentthepossiblerelationsbetweentheproduct-basedand theminimum-basedorderings inthe particular case where theconstraints known between the symbolic weights only pertainto the expressionof conditional preferences, asallowed byDefinition4. Thus, a constraint ofthis kindmay only compare weights pertainingto the same componentin thevectors, and we have C1=∅. Namely, weights located in differentcomponents ofavector areassumed tobeincomparable.Underthisassumption,Paretoorderingandsymmetric Paretoyieldthesameordering.Indeed, fortwo vectors
α
=(α
1,. . .,α
n)
andβ
=(β
1,. . .,β
n)
encodingthesymbolingratingsofconfigurations,eachsymbolicweightα
i6=1of
α
canonlybecomparedtothesymbolicweightβ
i6=1ofβ
.Thus,thereisnoneedtopermutecomponentsastheresultwoulddefinitelybeincomparable withanothercomponentweightsinceC1=∅. Inthefollowing, wedenote byprod(
α
)the product Qni=1
α
i.Then, itcan beshown that inthissituation, the followingequivalenceshold:
Proposition1. The followingequivalencesholdwhenC1=∅:
•
α
≻prodβ
⇔α
≻Pβ.
•
α
∼prodβ
⇔α
=β
•
α
ºprodβ
⇔α
ºPβ
Proof:
α
≻prodβ
requires that each instantiation of prod(α
) be at least as great as each instantiation of prod(α
). As byassumption, symbolsoftheform
α
i andβ
j arenot comparablefori6=j,unless oneof themisequalto 1, theonly waytohave
α
≻prodβ
istohaveaconstraintoftheformα
i>β
i(possiblyα
i=1)forsomei’sandα
j=β
j fortheothercomponents.Hence
α
≻Pβ
.Theothercasesfollowusingthesameapproach. ¤Wenowcomparethedifferentorderingsinducedbytheuseofproductorminimum,dependingonthechainruleapplied tothepossibilistic network.Theproduct ofsymbolic vectorsoften hasadiscriminatingpower greaterthan theone ofthe minimum operator, in the sense that
αβ
<α
, while we only have min(α
,β
)≤α
. However, with instantiated numerical values,bothproductandminimumleadtototalordersthatmayalsocontradicteachother:forinstance0.1×0.9>0.2×0.2, whilewiththeminwegetmin(0.1,0.9)<min(0.2,0.2).Thefollowingexampleillustratesthedifferencebetweenthepartial ordersobtainedwithproductand minimumincaseofsymbolicweights.Example 3. Let us consider the possibilistic preference network of Example2. Using the chain rule, we obtain the sym-bolic vectors presentedin Table2, and thefollowing symbolic joint possibilitydistribution:
π (
jbpbsb)
=1,π (
jbpbsr)
=δ
1,π (
jbpbsw)
=δ
2,π (
jbpwsb)
=β
θ
1,π (
jbpwsr)
=β
θ
2,π (
jbpwsw)
=β
,π (
jrpbsb)
=α
λ
1,π (
jrpbsr)
=α
,π (
jrpbsw)
=α
λ
2,π (
jrpwsb)
=π (
jrpwsr)
=π (
jrpwsw)
=α
β
. The product-basedinducedordering basedon inequalityconstraints in C0 is represented by Fig.2. For instance, jbpbsr≻prod jbpbsw becauseδ
1>δ
2 is in C0 and jbpwsw≻prod jrpwsw becauseβ
>α
β
. Now, if we use the min-based chain rule, we will not be able to getas many strict preferences as long as no1 Not to be confused with instantiations a
Fig. 2. Possibilistic product-based order relative to Example 3 .
Fig. 3. Possibilistic minimum-based order relative to Example 3 .
otherconstraintisadded.Infact,theonlystrictorderinginformationwecangetatthatstageisthat jbpbsb≻min jbpbsr≻min jbpbsw; jbpbsb≻min jbpwsw;andjbpbsb≻minjrpbsb.But,fortherest,weonlygetweakinequalitiessuchas jrpwsb¹min jbpwsw,
since
π
min(
jrpwsb)
=min(α
,β )
≤π
min(j
bpwsw)
=β
(dottedarrowsonFig.3depictsthismin-based ordering).The followingresults canbeobserved[10]:
Proposition2. WhenC1=∅,
α
∼prodβ
⇔α
∼minβ.
Indeed,
α
∼prodβ
ifandonlyifα
=β
duetoitscoincidencewithParetoorderingand thenα
∼minβ
.Conversely,supposeα
∼minβ
,i.e.,minα
i=minβ
i.Supposeα
i6=β
iforsomei.Theneitherα
i=1anditiseasytoletminβ
iassmallaspossible,fixing thevalues ofother
α
j’sand settingβ
i to avery smallvalue bi, i.e.,minai>minbi. Wecan dosomething similarifα
i>β
i. As the weights arenot comparableacross components of vectors (C1=∅),except for weights 1that do not affectminimumnorproduct, weconclude that
α
andβ
containthesameweightsineachcomponent. Proposition3. WhenC1=∅:α
±prodβ
⇔α
±minβ.
Proof. Indeed,
α
±prodβ
indicates that the vectorα
contains symbolic values that are not comparable to symbolic valuesin
β
. In that case,α
±minβ
as well.Conversely, note that no symbolic weight inα
will be absorbed with theminimumoperation since weights appearing in
α
(resp.β
) are pairwise incomparable.So, in our situation where C1=∅,α
±minβ
holdsinthesamecasesaswhen
α
±prodβ
holds. ¤Example4. ManycasesofincomparabilitycanbeidentifiedonExample2andthemin-based(resp.product-based)ordering presentedbyFig.3(resp.Fig.2).Forinstance,wehave jrpbsr±prod jbpwsw (resp. jrpbsr±min jbpwsw).
Moreover, usingsymbolic weights, product and minimumprovide consistent orderings, in contrast with thenumerical setting,inthesensethat:
Proposition4. WhenC1=∅,
α
≻prodβ
⇔α
ºminβ
andα
6=β.
Proof. It is obviousthat
α
≻prodβ
impliesα
6=β
.That it impliesα
ºminβ
comes from theequivalence between Pareto andproduct orderings when C1=∅. Conversely since C1=∅, the only possibility to get
α
ºminβ
is that∀
i,α
i≥β
i since theweights are not comparableacross components ofvectors (e.g.
α
i is not comparabletoβ
j for i6=j). Asα
6=β
, there mustexistisuchthat
α
i>β
i,and fortheother components,eitherα
k>β
korα
k=β
k.Hence,α
≻prodβ
. ¤As aconsequence,thestrictpreferencegraphof≻prod willbethesameastheweakpreferencegraphof≻min,aspatent
whencomparingFigs.2and 3forExample2.Andwecanseethat
α
ºminβ
⇒α
ºprodβ
.Proposition5. WhenC1=∅,
α
≻minβ
⇒α
≻prodβ.
Proof. This is because
α
≻minβ
if and only if∀
i suchthatβ
i6=1,α
i>β
i. Indeed supposeα
i≤β
i for some i. Then we canalwayssetboth
α
i andβ
i tothesamevalueascloseto0and possiblesoastomakeα
≻minβ
fail. ¤For instance, all solid arrows in Fig.3 also appear in Fig.2 for Example2. This indicates that ≻min isa strong form of
3.3. Constraintsacrosscomponentsofsymbolicvectors
As already mentioned, constraints between symbolic weights, beside those induced from the preference specification, can beadded whenavailable. In thefollowing wewill study therelations betweenthe different orderingrelationsin the presenceofsuch constraints.Ithasbeen shown abovethat,when thereisno constraintbetween symbolicweights in the vectors,theorderinginducedbytheproduct-basedchainrulecorrespondsexactlytotheParetoordering.Thisresultactually holdsreplacingParetobySymmetricParetointhe presenceofinequalityconstraintsbetween symbolicweights.
Proposition6. Givenanyset ofconstraints C oftheform
α
i≥β
j orα
i>β
j between symbolicweights, it holdsthatα
≻SPβ
iffα
≻prodβ
andα
ºSPβ
iffα
ºprodβ.
The proof of this result is not trivial and can be found in [10]. It appears in the appendix for the sake of self-containedness.
Letuscomparetheminimumbased-orderingand theproduct-basedordering(equivalently,SP).Itisclear thatwehave asacorollary ofthepreviousresult:
Proposition7.
α
∼prodβ
⇒α
∼minβ.
Proof.
α
∼prodβ
iffα
∼SPβ
, i.e.,α
∼Pα
σ′ for some permutationσ
. Thus,∀
i,α
i=β
iσ, where i∈{
1,...,n}
. Therefore, min(β
1,...,β
n)
=min(α
1,...,α
n)
. ¤Thelast propositionshows that thestrict min-based orderingcan solvesome incomparability cases forthe Symmetric Paretoordering.Indeed:
Proposition8. If
α
≻minβ
wemayeitherhaveα
±SPβ
orα
≻SPβ.
Proof. From Proposition7, if
α
∼SPβ
thenα
∼minβ
. Moreover, ifα
≺SPβ
then by definition,∀
i,α
i≤β
σ(i), thus itfollows that min
(α
1,...,α
n,β
i,...,β
n)
=min(α
1,...,α
n)
, this proves that we cannot haveα
≻minβ
in this case.Proposition8follows. ¤
Itis importantto notice that,ingeneral, symmetric Paretodominance (hencethe product ordering) inthe widesense doesnotrefinetheminimumordering,sincetheformermayyieldincomparabilityinsomecaseswhenminimumsucceeds incomparing.
Theextreme caseis whenassuming a total preorder between allsymbolic weights.In that case, minimumordering is total. However, in the presence of such rich constraints, symmetric Pareto ordering (i.e., product) with symbolic weights maystillleadtoincomparability.Indeed, theonlycase,wheresymmetric Paretoleadstoatotal orderingiswhenthereare enoughconstraintsbetweensubsets ofsymbolicweights(correspondingtothecomparisonofsubproducts).
3.4. Applicationto bestsolutionanddominancequeries in
π
-prefnetsIna preference model, two typesof queries are commonly used: namely,optimization queries forfinding the optimal configuration(s)(i.e.,thosewhicharenotdominatedbyothers)and dominancequeriesforcomparingconfigurations.
Optimization. Since
π
-pref nets allow theuser toexpress indifference, the optimization querymay return morethan one configuration.Clearly, thebest configurationsare thosehaving ajoint possibility degreeequal to1. Indeed, such a config-urationalwaysexists sincethejointpossibility distributionassociated tothepossibilisticnetwork isnormalized,thanksto the normalization of each conditional possibility table (indeed, for each variable Ai, for each instantiation p(Ai) of P(A
i)
,wehave:max
(π (a
i|
p(Ai))
,π (
¬ai|
p(Ai)))
=1where{
¬ai}
=DAi/{
ai}
withai∈DAi). Thus,wecanalwaysfind anoptimalconfiguration,startingfromtherootnodeswherewechooseeachtimethemostorone ofthemostpreferredvalue(s)(i.e., with possibility equalto 1). Then, depending on theparents instantiation, eachtime weagain choose analternativewith aconditional possibility equal to1. Atthe endof theprocedure, we getone orseveral completely instantiated configura-tions having apossibility equal to 1. Consequently, partial preference orders with incomparable maximalelements cannot berepresentedbya
π
-prefnet.Example5. Letusreconsider Example2anditsproduct-basedjointpossibilitydegreedepicted byFig.2.Then,jbpbsb isthe preferredconfigurationsinceitsjointpossibilityisequalto1,and thisistheonly one.
Thisprocedureislinearinthesizeofthenetwork(usingaforwardsweepalgorithm).Apossiblevariantofthe optimiza-tionproblemistocomputetheMmostpossibleconfigurationsusingavariant oftheMostProbableExplanationalgorithm in [17]. This query can be interesting in
π
-pref nets even if the answer is not always obvious to obtain in presence of incomparableconfigurations.Algorithm1:Comparisonbetweentwojoint symbolicpossibilityvectors. Data:
α
,β
,CResult:R
1 begin
2 equality
(α
,β
,C)
;3 if(empty
(α)
andempty(β)
)thenR←α
=β
;4 else s←sort(α,
β
, C)
; 5 ifs=truethenR←α
≻β
; 6 else s←sort(β,α
, C)
; 7 ifs=truethenR←β
≻α
; 8 else R←α
±β
; 9 returnRDominance. The comparison between symbolic possibility degrees can befound usingAlgorithm1 that takes asinput a set of constraintsC between symbolic weights and two ratingvectors. Let usconsider two configurations
ω
andω
′ with simplifiedrespectivesimplifiedvectorsα
∗=(α
1,...,
α
k)
andβ
∗=(β
1,...,β
m)
wherethecomponentsequalto1havebeendeleted,withk≤m≤n.Then,thealgorithmproceedsbyfirstdeletingallpairsofequalcomponents,oneineachvector,soto gettotallydifferentsetsofcomponentsineachvector.Second,itchecksifthereexistsaninjectivefunction
ϕ
:{
1,...,k}
→{
1,...,m}
suchthat∀
i=1,...,k,α
i≥β
ϕ(i) and∃
ℓ∈{
1,...,k}
,α
ℓ>β
ϕ(ℓ) (otherwisetheyremainincomparable). Thusthealgorithmisbasedonthesequentialapplicationof:(1) Thefunctionequalitythat deletesthecommonvaluesbetween
α
andβ
.(2) Thefunctionsortthatreturnstrueifaninjectionisfoundbetween
α
∗ andβ
∗ ensuringtheabovedominancecondition.(3) Thefunctionemptythattestsifavectorofweights
α
isemptyornot.Example6. Letusconsiderthe
π
-prefnet5G
ofExample2.UsingAlgorithm1,theorderingbetweentheconfigurationsis shownin Fig.2suchthat alink fromω
toω
′ meansthatω
ispreferred toω
′. For instance, considerconfiguration jbpwsr
suchthat
π (
jbpwsr)
=β
·θ
2 and configuration jrpwsr suchthatπ (
jrpwsr)
=α
·β
.First, we shoulddelete commonvalues,namely the symbolic weight
β
. Then, we should check if C entailsα
<θ
2 or the converse. But here,α
andθ
2 are notcomparable.Thus, jbpwsr±prod jrpwsr.
Clearly, for
π
-pref nets, the complexity is due to the comparison step in Algorithm1 (since the computation of the possibility degrees is a simple matter using the chain rule), and in particular to the sort function where the matching between thetwo vectors needs the definitionof different possible arrangements, i.e., thealgorithm is oftime complexity O(n!).4. Comparisonof
π
-pref netswithother graphicalpreferencestructuresWe now compare
π
-prefnets with ConditionalPreference networks (CP-nets) which deal with thesame kind of con-ditional preference statements. Moreover we also discuss the OCF networks that are “semi-qualitative”. For the sake of simplicity,werestricttothecasewherethedecisionvariablesareBoolean.4.1. CP-nets
CP-nets, initially introduced in[6,18], areconsidered as anefficient model to manage qualitative preferences.They are basedonapreferentialindependencepropertyoftenreferredtoasaceteris paribusassumptionsuchthatapartial configu-rationispreferredtoanotherone,everythingelsebeingequal.Formally,itisdefinedasfollows:
Definition9(Preferentialindependence). LetV beasetofvariablesand W beasubsetofV.W issaidtobepreferentially independentfrom itscomplementZ=V
\
W iff foranyinstantiations,z,z′ ofvariablesinZ,and w, w′ ofvariablesinW, itholdsthat(w,
z)≻(w
′,z)⇔(w,
z′)
≻(w
′,z′)
.Preferential independence is asymmetric. Indeed, it might happen, e.g., for disjoint sets X, Y and Z of variables that X is preferentially independent (Definition9) from Y given Z without having Y preferentially independentfrom X. This independenceisataworkinthegraphicalstructureunderlying CP-nets.
Definition 10 (CP-nets). A CP-net is a preference network in the sense of Definition3 where preference statements are interpreted by meansof the ceteris paribus assumption, namely, the preference pertaining to each decision variable Ai at
eachnodeonly dependson theparent(s)contextp(Ai),andispreferentiallyindependentfromtherestofvariables.
Using theinformation in the CP-Tables and applying the ceteris paribus principle, we only obtain preferences between configurationsdifferingbyoneflip,i.e.,obtainedbychangingthevalueofasinglevariable.Indeed,whenflippingavalueof
Fig. 4. Preference network for Example 7 .
Fig. 5. Worsening flip graph for the CP-net of Fig. 4 up to transitive closure.
one variablein aconfiguration, one obtains eitheranimproved configuration(improving flip),or aworse one (worsening flip).Thesepairsofconfigurationsdifferingbyoneflipcanbeorganizedintoacollectionofworsening(directed)pathswith auniquerootcorrespondingtothebestconfigurationandwheretheother pathextremitiesaretheworstones.ACP-netis saidtobesatisfiableifthereexistsatleastonepartialorderofconfigurationsthatsatisfiesit.NotethateveryacyclicCP-net issatisfiable.
Example 7. Consider a preference specification about a holiday house in terms of four decision variables V=
{
T,S,P,C}standing for type, size, place and car park respectively, with values T∈{flat(t1),house(t2)}, S∈{big(s1),small(s2)}, P∈
{
downtown(p
1)
, outskirt(p
2)
}
and C∈{car(c1), nocar(c2)}. Preference on T is unconditional, while all the other prefer-encesareconditional asfollows:t1≻t2, t1:p1≻p2, t2:p2≻p1, p1:c1≻c2,p2:c2≻c1,t1:s2≻s1,t2:s1≻s2.Fig.4represents the correspondingCP-net,anditsinducedworseningflipgraphison Fig.5.Acyclic CP-nets have a unique optimal configuration. Finding it amounts to looking for a configuration where all the conditional preferences are best satisfied. It can be done by asimple forward sweeping procedure where, for eachnode, weassign themost preferredvalue accordingtothe parentscontext. Foracyclic CP-nets,thisprocedure islinearw.r.t. the numberofvariables[6].Incontrast,forcycliconesansweringthisqueryneedsanNP-hardalgorithmand mayleadtomore than oneoptimalconfiguration[19].Dominancequeriesaremorecomplex. Aconfigurationispreferredto anotherif there exists achain(directed path)of worseningflipsbetween them[18].Note that if forany variableAi∈V,Ai ispreferentially independentfromVAi,thentheCP-netgraphisdisconnectedandmanyconfigurationscannotbecompared.Thecomplexity ofdominancetestingdependsontheCP-netstructure.IndeedforthecaseofacyclicCP-nets,Boutilieretal.[6] showthat(i) indirectedtreeCP-nets,thecomplexity isquadraticinthenumberofvariables,(ii)inpolytreeCP-nets,itispolynomial in thesizeoftheCP-netdescription(variablesand preferencetablesizes),(iii) insinglyconnectedCP-nets,itisNP-complete. InmultiplyconnectedCP-nets,theproblemisinNPorharder(itremainsanopenproblemuntilnow).Forgeneral CP-nets (allowingcycles)theproblemisPSPACE-complete [19].
Ingeneral,theorderinginducedbyaCP-netisstrictand partial,sinceseveral configurationsmayremainincomparable (i.e.,no worsening flipschainexists betweenthem).Clearly, acyclic CP-netscannotexhibit anyties. Theceteris paribus
as-Fig. 6. Configuration graph of Ex. 7. Thin arrows reflect ≻prod , dotted arrows compare sets S(ω) , and bold arrows reflect additional ceteris paribus compar- isons, also in bold on Fig. 5 .
sumptionsimplifiespreferenceelicitationforCP-nets;theelicitationcomplexityisequaltoO(nk)suchthatnisthenumber
ofnodesand kisthemaximalnumberofparents[20].
However,inCP-nets,preferenceexpressedinaparentnodetendstobemoreimportantthantheoneexpressedinachild one[21].Inotherwords,violatingapreferenceassociatedwithafathernodeismoreimportantthanviolatingapreference associatedwithachildone;thispriorityimplicitlygiven bytheapplicationofceterisparibus assumptionmaybedebatable. Forinstance,intheCP-netofFig.4,configurationt1p1c2s1ispreferredtoconfigurationt2p2c2s2becausethereisasequence
ofworseningflipsfromtheformertothelatter,asseeninthegraphofFig.5.Moreover,thiskindofpriorityisnottransitive inthesensethatCP-netscannotalwaysdecidewhetherviolatingpreferencesoftwochildrennodesispreferredtoviolating preferencesassociated withone childand one grandsonnoderespectively (whichmight havebeen expected asbeingless damagingthanviolatingtwochildrenpreferences)[22].Thislimitationisproblematic.Generally,therearepartialpreference orderingsthat CP-netscannotexpress,see[9]foracounterexample.
TheexpressivepowerofCP-netsislimited.Inparticular,weareunabletospecifyimportancerelationsbetweenvariables, besidethoseimplicitlyimposedbetweenparentsand children.Tradeoffs-enhancedCP-nets(TCP-nets)[23] areanextension ofCP-netsthatadds anotionofimportancebetween thevariablesbyenrichingthenetwork withnewarcs.TCP-netsobey thepreferencestatements inducedbyceterisparibusassumption,sincetheorderingobtainedisarefinementoftheCP-nets ordering.Infact, therefinementbroughtbyTCP-netscannotoverridetheimplicitpriorityinfavorofparentsnodes.
4.2.
π
-prefnetsvs.CP-netsIn this section, we show that the configuration graph of any CP-net can be refined using a
π
-pref net without local indifference,basedon thesamepreferencenetwork,providedsomeconstraintson productsofsymbolicweightsareadded totheπ
-prefnet,inordertorestoresomeceterisparibusassumption-basedpriorities.Precisely,theaddedconstraintsreflect thehigher importanceoffathernodeswithrespecttotheirchildren.The preferencesexpressed bytheCP-netscan berepresentedbya
π
-prefnetsharingthesamegraphicalstructureand wheretheconditionalpossibilitydistributionsencodethelocalpreferences.Example 8. Consider the preference network of Fig.4. Encoded as a possibilistic network it reads:
π (t
1)
=1,π (t
2)
=α
,π (p
1|
t1)
=π (p
2|
t2)
=1,π (p
2|
t1)
=β
1,π (p
1|
t2)
=β
2,π (s
1|
t1)
=γ
1,π (s
2|
t2)
=γ
2,π (s
2|
t1)
=π (s
1|
t2)
=1,π (c
1|
p1)
=π (c
2|
p2)
=1,π (c
2|
p1)
=δ
1 andπ (c
1|
p2)
=δ
2. Applying the product-basedchain rule, we can compute the jointpossi-bility distribution relative to T,P, Cand S.Thin arrows in Fig.6represent the configurationgraph inducedfrom thisjoint possibilitydistribution,andboldarrowsreflectadditionalceterisparibuscomparisonsforthecorrespondingCP-nets.Clearly, theconfigurationt1p1c1s2istheroot(sinceitistheuniqueonewithdegree
π (
t1p1c1s2)
=1).Giventheordinalnature ofpreferencetablesofCP-nets,andthefactthatwerestricttoBooleanvariables,italsomakes sensetocharacterizethequalityof
ω
usingthesetS(ω)
={
Ai:α
i=1}
ofsatisfiedpreferencestatements(onepervariable),where
α
=(α
1,. . .,α
n)
.Itisthenclearthat theParetoorderingbetweenconfigurationsinducedbythepreferencetablesisrefinedbycomparingthesesatisfactionsets:
α
≻prodβ
⇒S(
ω
′)
⊂ S(
ω
)
(2)since the only case when
α
i>β
i is whenα
i=1 if variables are Boolean and ≻prod precisely coincides with the ParetoExample 9. To see that this inclusion-based ordering is stronger than the
π
-pref net ordering, consider Fig.6 whereπ (t
1p2c1s2)
=β
1δ
2 with S(t
1p2c1s2)
={
T,S} andπ (t
2p1c2s1)
=αβ
2δ
1 with S(t
2p1c2s1)
={
S}. We do have thatS
(t
1p2c1s2)
⊃ S(t
2p1c2s1)
, butβ
1δ
2 is not comparable withαβ
2δ
1. Dotted and thin arrows of Fig.6 represent thecon-figurationgraphinducedbycomparingsetsS
(ω)
Theinclusion-basedorderingS
(ω
′)
⊂ S(ω)
doesnot dependontheparent variablescontexts,but onlyon thefactthat inthe context ofaconfiguration ofits parents,a variable hasapreferred value (we call“good”)or aless preferred value (wecall“bad”).Inthefollowing,weassumethatthecomponentsofvector
α
arelinearlyorderedinagreementwiththepartialordering ofvariablesinthesymbolicpreference network,namely,if i<jthenAiisnotadescendentofAjinthepreferencenet(i.e., topologicalordering). ForinstanceinthepreferencenetofFig.4,wecanusetheordering(T,P,C,S).Letusfirstprove that,intheconfigurationgraphsinducedbyaCP-net andthecorresponding
π
-prefnet,therecannot beany preferencereversalsbetweenconfigurations.LetC(A
)
denotethechildrenset ofA∈V.Lemma 1. If
ω
≻CPω
′ and these configurations differ by oneflip of some variable Ai, thenthe inclusion S(ω)
⊂ S(ω
′)
is not possible.Proof. Compare S
(ω)
and S(ω
′)
. It is clear that Ai6∈S
(ω
′)
(otherwise the flip would not be improving) and S(ω)
=(
S(ω
′)
∪{
Ai
}
∪C−+(A
i))
\
C−+(A
i)
,whereC−+(A
i)
isthesetofchildrenvariablesthatswitchfromabadtoagood valuewhengoingfrom
ω
′ toω
, andC−+
(A
i)
istheset ofchildrenvariablesthatswitchfromagood toabadvaluewhengoingfromω
′to
ω
.Itisclearthatitcanneverbethecasethat S(ω)
⊂ S(ω
′)
,indeedAiisinS
(ω)
andnotinS(ω
′)
byconstruction.ButS
(ω
′)
maycontainvariablesnotinS(ω)
(thoseinC+−(A
i)
ifnotempty). ¤Inthefollowing,given twoconfigurations
ω
andω
′,letDω,ω′ theset ofvariableswhichbeardifferent valuesinω
andω
′.Proposition9. If
ω
≻CPω
′ thenS(ω
′)
⊂ S(ω)
isnotpossible.Proof. If
ω
≻CPω
′,thenthereisachainofimprovingflipsω
0=ω
′≺CPω
1≺CP· · · ≺CPω
k=ω
.ApplyingtheaboveLemma1,S
(ω
i)
=(
S(ω
i−1)
∪{
Vi−1}
∪C−+(V
i−1))
\
(
C+−(V
i−1)
forsomevariableVi−1=Aj.BytheaboveLemma1,wecannothaveω
i ≺πω
i−1.SupposewechoosethechainofimprovingflipsbyflippingateachstepatopvariableAjinthepreferencenet,amongtheones tobeflipped, i.e., j=min
{
ℓ:Aℓ∈Dωi−1,ω}
. Itmeansthat whenfollowing thechainofimproving flips,thestatus ofeachflippedvariablewillnotbequestionedbylaterflips,asnoflipped variablewillbeachildofvariablesflipped later on.So S(ω
′)
willcontainsomevariablesnotinS(ω)
, soω
≻prod
ω
′ isnotpossible. ¤Thepreviousresultsshowthat itisimpossibletohaveapreference reversalbetweenCP-netorderingand theinclusion ordering,whichimpliesthatno preferencereversalispossiblebetweentheCP-netorderingand the
π
-prefnetordering.It suggeststhatwecantrytoaddceterisparibusconstraintstoaπ
-prefnetandsoastocapturethepreferencesexpressedby aCP-net.Define apreferencerelation≻+prod betweenconfigurationsasfollowsω
≻+prodω
′ ⇐⇒ω
≻prodω
′ orω
≻CPω
′.Asmentionedearlier,inCP-nets,parentspreferencesaremoreimportantthatchildrenones.Thispropertyisnotensuredby
π
-prefnetswhereallviolationsareconsideredhavingthesameimportance. Inthefollowing,welaybarelocal constraints between productsofsymbolic weights, pertainingto eachnode and itschildren,that enable ceteris paribusassumption to besimulated.Let
ω
,ω
′differbyoneflip,andsuchthatnoneofω
≻prodω
′,ω
′≻prodω
holds,andmoreover,ω
≻CPω
′.Wemustenforcethecondition
π
(ω
)>π
(ω
′).Suppose theflippingvariableisA.Clearly, A∈S(ω)
,but A6∈S(ω
′)
.Letα
bethepossibilitydegree of Awhen it takes the bad value in context p(A) (itis 1when it takes the good value). When flipping A from agood to abad value, only thequality ofthechildren variablesC(A
)
of Amay change.C(A
)
can bepartitioned intoatmost 4sets, C−−(A
)
(resp. C−+(A
)
,C−+(A
)
,C++(A
)
), which represents theset ofchildren ofA whose values remain bad (resp.change from goodtobad, frombadtogood,and staygood)whenflippingA.Thenitcanbeeasilycheckedthat:π
(
ω
)
=1· Y Ci∈C+−(A)γ
i· Y Cj∈C−−(A)γ
j·β
π
(
ω
′)
=α
· Y Ck∈C+−(A)γ
k· Y Cj∈C−−(A)γ
j·β
where
β
isaproductofsymbols,pertainingtonodesotherthanAanditschildren,that remainunchangedbytheflipofA. Thentheconstraintπ
(ω
)>π
(ω
′)comes downtotheinequality:Y Ci∈C−+(A)
γ
i>α
· Y Ck∈C+−(A)γ
k (3)wheresymbolsappearing on oneside donot appearon theother side.Suchconstraintsaresufficientto retrievethe pref-erencesoftheCP-net.Notethat thepreferences
ω
≻prodω
′ andω
≻CPω
′ conjointlyholdinbothapproacheswheneverAhasno childnode,and moregenerally wheneverthe downflip onA correspondsto nochildvariable moving fromabad to a good state, i.e.C−+
(A
)
=∅.Thus, adding constraints(3) to theπ
-pref netconstraints,we geta refinedconfiguration graph thatincludes preferencesinducedbytheCP-net.Example 10. The
π
-pref net configuration graph of Fig.6 must be completed using the constraints (3) for the five bold arrows, corresponding to the ceteris paribus preferences not captured by theπ
-pref net, which come down to enforcingα
<β
1γ
1,β
1<δ
1 andβ
2<δ
2. Itturnsoutthat werecovertheCP-netorderingexactly.Especially, thedottedarrows(cor-respondingtotheinclusionrelationsS
(ω
′)
⊃ S(ω)
)areobtainedfromtheCP-netorderingbychainsofworseningflips. In the example, we exactlycapture thepreference graph of aCP-net usingadditional constraints between productsof symbolicweights.This observationand theabove considerationsthus encourageus tostudy whetherπ
-prefnets without constraintsarerefinedbyCP-nets,namelythattheconfigurationgraphoftheformercontainslessstrictpreferencesbetween configurationsthan theone of thelatter, so that adding the constraints(3) are enough to exactlysimulate a CP-netby aπ
-pref netwith constraints. A sufficient condition would be that S(ω)
⊂ S(ω
′)
impliesω
′≻CPω
. Note that if it were notthecase, itwould mean that CP-netsdo not respect Pareto-ordering,which would casta doubt on the rationalnature of CP-nets.However, providingaformalprooflookstricky.Thisisatopicforfurtherresearch.
4.3.
π
-prefnetsvs.OCF-netsOrdinal ConditionalFunctions (OCF) [24] are an uncertainty representation framework very close to possibility theory
[25]that havebeen recentlyused for preferencemodeling [8].They arefunctions
κ
:Ä
→N suchthatκ (ω)
=0forsome configurationω
,whichmeans,inthepreferencesettingthatω
isfullyacceptable,whilethegreaterκ
(ω
),theless satisfac-toryitis.Besidesitisassumed thatκ (U
∪V)
=min(κ (U
)
,κ (V
))
.OCF-netsarelikenumericalpossibilisticnetworks, exceptthattheyobeyanadditivechainruleoftheform:
κ
(
ω
)
=n
X
i=1
κ
(A
i|
p(Ai))
(4)In [26], itwas pointedout that the set-function
π
κ(A
i)
=2−κ (Ai) is apossibility measure. The converse holdsto someextent insofar as if
π (A
i)
=α
, the valuesκ (A
i)
=−log2(α)
are integer rank weights. In fact in the symbolic setting, wecanindifferently use
π
-prefnets and OCF-nets,since theproductof variableson [0, 1] where1 indicatesfull satisfaction, behavesexactlylikethesumofvariablesonthepositive integers,where0indicatesfullsatisfaction.Eichhorn etal. [8] proved that numerical OCF-nets can refine CP-net orderings. Precisely, OCF-nets will lead always to total orderings that are compatible withCP-nets. Todo so they useaset of particular constraintsto beimposedon their integerweights, which basicallycorrespond toour constraints(3), albeitbetween numericalvalues. In contrasttheuse of symbolicweights inourapproach preservesthepartialityofthe ordering,thus enabling theCP-netconfigurationgraphto beexactlyrecovered.Moreovertheuseofsymbolicweightsdoesnotcommitustothechoiceofparticularnumericalvalues.
4.4.
π
-prefnetsvs.CP-theoriesπ
-prefnetscanalsobecomparedwithso-calledCP-theories[27].Thelatterinterpret conditionalpreferencestatements assuming theyhold irrespectively ofthe valuesof other variables.It meansthat any configurationω
where A takes a pre-ferredvaluea+,accordingtoapreferencestatementinthecontextp(A),ispreferredtoanyω
′whereAtakesalesspreferred valuea−, inthesamecontextp(A).Thisconstraintreadsmin
ω:ω[P(A)∪{A}]=p(A)a+
π
(
ω
)
>ω:ω[P(Amax)∪{A}]=p(A)a−π
(
ω
)
.In terms of possibility functions, it reads
1(p(A
)
∧a+)
>5(
p(A)
∧a−)
, where1(F
)
=minω∈F
π (ω)
[28]. In [27] arestudied hybrid nets where some variables are handled using the ceteris paribus assumption, while the preference holds irrespectively of other variables. In
π
-pref nets preference statements are interpreted byπ (a
+|
p(A))
>π (a
−|
p(A))
whichisprovablyequivalentto
5(p(A
)
∧a+)
>5(
p(A)
∧a−)
,i.e.,comparingbest configurations.Itisclear thatifω
≻CP
ω
′ holds,then
ω
≻ω
′ holdsinaCP-theory,whereconditionalpreferenceholdsirrespectivelyofothervariables,becausetheCP-theory generatesmorepreferenceconstraintsbetweenconfigurations,includingtheonesinducedbytheceterisparibusassumption. Constraints inducedby CP theories can thus be captured inπ
-prefnets by adding moreconstraints between products of symbolicweights.5. Conclusion
This paperpresents anapproach to preference modeling based on joint possibility distributions obtained from a con-ditional preference network, albeit without usingnumerical possibility values to represent preference intensity. We have used uninstantiated symbols taking values on the unit interval and constraints between them to describe local relative preferences. Then wecompute the product thereof to assign symbolic possibility values to complete configurations (solu-tions)usingtheproduct chainrule ofpossibilistic networks.Thepreference graphbetweenconfigurationsisthenobtained