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Possibility Theory and its applications: a retrospective and prospective view. D. Dubois, H. Prade IRIT-CNRS, Université Paul Sabatier 31062 TOULOUSE FRANCE. Outline. Basic definitions Pioneers Qualitative possibility theory Quantitative possibility theory.

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Possibility theory and its applications a retrospective and prospective view

Possibility Theory and its applications: a retrospective and prospective view

D. Dubois, H. Prade IRIT-CNRS, Université Paul Sabatier 31062 TOULOUSE FRANCE


  • Basic definitions

  • Pioneers

  • Qualitative possibility theory

  • Quantitative possibility theory

Possibility theory is an uncertainty theory devoted to the handling of incomplete information
Possibility theory is an uncertainty theory devoted to the handling of incomplete information.

  • similar to probability theory because it is based on set-functions.

  • differs by the use of a pair of dual set functions (possibility and necessity measures) instead of only one.

  • it is not additive and makes sense on ordinal structures.

The name "Theory of Possibility" was coined by Zadeh in 1978

The concept of possibility
The concept handling of of possibility

  • Feasibility:It is possible to do something (physical)

  • Plausibility: It is possible thatsomething occurs (epistemic)

  • Consistency : Compatible with what is known (logical)

  • Permission: It is allowed to do something (deontic)

Possibility distributions uncertainty
POSSIBILITY DISTRIBUTIONS handling of (uncertainty)

  • S: frame of discernment (set of "states of the world")

  • x : ill-known description of the current state of affairs taking its value on S

  • L: Plausibility scale: totally ordered set of plausibility levels ([0,1], finite chain, integers,...)

  • A possibility distribution πx attached to x is a mapping from S to L : s, πx(s)  L, such that s, πx(s) = 1 (normalization)

  • Conventions:

    πx(s) = 0 iff x = s is impossible, totally excluded

    πx(s) = 1 iff x = s is normal, fully plausible, unsurprizing

Example x age of president
EXAMPLE : x = AGE OF PRESIDENT handling of

  • If I do not know the age of the president, I may have statistics on presidents ages… but generally not, or they may be irrelevant.

  • partial ignorance :

    • 70 ≤ x ≤ 80 (sets,intervals)

      a uniform possibility distribution

      π(x) = 1 x  [70, 80]

      = 0 otherwise

  • partial ignorance with preferences : May have reasons to believe that72 > 71  73 > 70  74 > 75 > 76 > 77

Example x age of president1
EXAMPLE : x = AGE OF PRESIDENT handling of

  • Linguistic information described by fuzzy sets: “ he is old ” : π = µOLD

  • If I bet on president's age: I may come up with a subjective probability !

    But this result is enforced by the setting of exchangeable bets (Dutch book argument). Actual information is often poorer.

A possibility distribution is the representation of a state of knowledge: a description of how we think the state of affairs is.

  • π' more specific than π in the wide sense if and only if π' ≤ π

    In other words: any value possible for π' should be at least as possible for π that is, π' is more informative than π

  • COMPLETE KNOWLEDGE : The most specific ones

  • π(s0) = 1 ; π(s) = 0 otherwise

  • IGNORANCE : π(s) = 1,  s  S

Possibility and necessity of an event

  • A possibility distribution on S (the normal values of x)

  • an event A

    How confident are we that x  A  S ?

  • (A) = maxuAπ(s); The degree of possibility that x  A

  • N(A) = 1 – (Ac)=min uA 1 – π(s) The degree of certainty (necessity) that x  A

Comparing the value of a quantity x to a threshold of knowledge: when the value of x is only known to belong to an interval [a, b].

  • In this example, the available knowledge is modeled by p(x) = 1 if  x  [a, b], 0 otherwise.

  • Proposition p = "x > " to be checked

  • i) a > : then x >  is certainly true : N(x >  ) = P(x >  ) = 1.

  • ii) b < : then x >  is certainly false ; N(x >  ) = P(x >  ) = 0.

  • iii) a ≤  ≤ b: then x >  is possibly true or false; N(x >  ) = 0; P(x >  ) = 1.

Basic properties
Basic properties of knowledge:

(A) = to what extent at least one element in A is consistent with π (= possible)

N(A) = 1 – (Ac) = to what extent no element outside A is possible = to what extent π implies A

(A  B) = max((A), (B)); N(A  B) = min(N(A), N(B)).

Mind that most of the time : (A  B) < min((A), (B)); N(A B) > max(N(A), N(B)

Corollary N(A) > 0 (A) = 1

Pioneers of possibility theory
Pioneers of possibility theory of knowledge:

  • In the 1950’s, G.L.S. Shackle called "degree of potential surprize" of an event its degree of impossibility.

  • Potential surprize is valued on a disbelief scale, namely a positive interval of the form [0, y*], where y* denotes the absolute rejection of the event to which it is assigned.

  • The degree of surprize of an event is the degree of surprize of its least surprizing realization.

  • He introduces a notion of conditional possibility

Pioneers of possibility theory1
Pioneers of possibility theory of knowledge:

  • In his 1973 book, the philosopherDavid Lewisconsiders a relation between possible worlds he calls "comparative possibility".

  • He relates this concept of possibility to a notion of similarity between possible worlds for defining the truth conditions of counterfactual statements.

  • for events A, B, C, A B C  A  C  B.

  • The ones and only ordinal counterparts to possibility measures

Pioneers of possibility theory2
Pioneers of possibility theory of knowledge:

  • The philosopherL. J. Cohenconsidered the problem of legal reasoning (1977).

  • "Baconian probabilities" understood as degrees of provability.

  • It is hard to prove someone guilty at the court of law by means of pure statistical arguments.

  • A hypothesis and its negation cannot both have positive "provability"

  • Such degrees of provability coincide with necessity measures.

Pioneers of possibility theory3
Pioneers of possibility theory of knowledge:

  • Zadeh(1978) proposed an interpretation of membership functions of fuzzy sets as possibility distributions encoding flexible constraints induced by natural language statements.

  • relationship between possibility and probability: what is probable must preliminarily be possible.

  • refers to the idea of graded feasibility ("degrees of ease") rather than to the epistemic notion of plausibility.

  • the key axiom of "maxitivity" for possibility measures is highlighted (also for fuzzy events).

Qualitative vs quantitative possibility theories
Qualitative vs. quantitative possibility theories of knowledge:

  • Qualitative:

    • comparative: A complete pre-ordering ≥πon U A well-ordered partition of U: E1 > E2 > … > En

    • absolute: πx(s)  L = finite chain, complete lattice...

  • Quantitative: πx(s)  [0, 1], integers...

    One must indicate where the numbers come from.

    All theories agree on the fundamental maxitivity axiom(A  B) = max((A), (B))

    Theories diverge on the conditioning operation

Ordinal possibilistic conditioning
Ordinal possibilistic conditioning of knowledge:

  • A Bayesian-like equation: A) = min(A), ) A) is the maximal solution to this equation.

    (B | A) = 1 if A, B ≠ Ø, (A) = (A  B) > 0 = (A  B) if (A) > (A  B)

    N(B | A) = 1 – (Bc| A)

    • Independence (B | A) = (B) impliesA) = min(), )

    Not the converse!!!!

Qualitative possibilistic reasoning

  • The set of states of affairs is partitioned via π into a totally ordered set of clusters of equally plausible states

    E1 (normal worlds) > E2 >... En+1 (impossible worlds)

  • ASSUMPTION: the current situation is normal.

    By default the state of affairs is in E1

  • N(A) > 0 iff P(A) > P(Ac)

    iff A is true in all the normal situations

    Then, A is accepted as an expected truth

  • Accepted events are closed under deduction

A calculus of plausible inference

(B) ≥(C) means « Comparing propositions on the basis of their most normal models »

  • ASSUMPTION for computing (B): the current situation is the most normal where B is true.

  • PLAUSIBLE REASONING = “ reasoning as if the current situation were normal” and jumping to accepted conclusions obtained from the normality assumption.


Acceptance is defeasible

• If B is learned to be true, then the normal situations become the most plausible ones in B, and the accepted beliefs are revised accordingly

  • Accepting A in the context where B is true:

  • P(AB) > P(Ac B) iff N(A | B) > 0(conditioning)

    • One may have N(A) > 0 , N(Ac | B) > 0 :


Plausible inference with a possibility distribution

Given a non-dogmatic possibility distribution π on S (π(s) > 0, s)

Propositions A, and B

  • A |=πB iff (A  B) > (A Bc)

    It means that B is true in the most plausible worlds where A is true

  • This is a form of inference first proposed by Shoham in nonmonotonic reasoning

Plausible inference with a possibility distribution1

(in A)

Exa mple continued
Exa mple (continued) of knowledge:

  • Pieces of knowledge like ∆ = {b f, p  b, p  ¬f}

    can be expressed by constraints

    (b  f) > ( b ¬f)

    (p  b) > (p  ¬b)

    (p  ¬f) > (p  f)

    • the minimally specific π* ranks normal situations first:

      ¬p  b  f, ¬p ¬b

    • then abnormal situations: ¬f  b

    • Last, totally absurd situations f  p , ¬b p

Example back to possibilistic logic
Example of knowledge: (back to possibilistic logic)

 = material implication

  • Ranking of rules: b f has less priority that others according to p*: N*(b f ) = N*(p  b) > N*(b f)

  • Possibilistic base :

    K = {(b f ), (p  b), (p  ¬f)}, with  < 

Applications of qualitative possibility theory
Applications of qualitative possibility theory of knowledge:

  • Exception-tolerant Reasoning in rule bases

  • Belief revision and inconsistency handling in deductive knowledge bases

  • Handling priority in constraint-based reasoning

  • Decision-making under uncertainty with qualitative criteria (scheduling)

  • Abductive reasoning for diagnosis under poor causal knowledge (satellite faults, car engine test-benches)

Absolute approach to qualitative decision

  • A set of states S;

  • A set of consequences X.

  • A decision = a mapping f from S to X

  • f(s) is the consequence of decision f when the state is known to be s.

  • Problem : rank-order the set of decisions in XS when the state is ill-known and there is a utility function on X.

  • This is SAVAGE framework.

Absolute approach to qualitative decision1

  • Uncertainty on states is possibilistic a function π: S  L

    L is a totally ordered plausibility scale

  • Preference on consequences:

    a qualitative utility function µ: X  U

    • µ(x) = 0 totally rejected consequence

    • µ(y) > µ(x) y preferred to x

    • µ(x) = 1 preferred consequence

Possibilistic decision criteria
Possibilistic decision criteria of knowledge:

  • Qualitative pessimistic utility (Whalen):

    UPES(f) = minsS max(n(π(s)), µ(f(s)))

    where n is the order-reversing map of V

    • Low utility : plausible state with bad consequences

  • Qualitative optimistic utility (Yager):

    UOPT(f) = maxsS min(π(s), µ(f(s)))

    • High utility: plausible states with good consequences

The pessimistic and optimistic utilities are well known fuzzy pattern matching indices
The pessimistic and optimistic utilities are well-known fuzzy pattern-matching indices

  • in fuzzy expert systems:

    • µ = membership function of rule condition

    • π = imprecision of input fact

  • in fuzzy databases

    • µ = membership function of query

    • π = distribution of stored imprecise data

  • in pattern recognition

    • µ = membership function of attribute template

    • π = distribution of an ill-known object attribute

Assumption plausibility and preference scales l and u are commensurate
Assumption fuzzy pattern-matching indices : plausibility and preference scales L and U are commensurate

  • There exists a common scale V that contains both L and U, so that confidence and uncertainty levels can be compared.

    • (certainty equivalent of a lottery)

  • If only a subset E of plausible states is known

    • π = E

    • UPES(f) = minsE µ(f(s)) (utility of the worst consequence in E)

      criterion of Wald under ignorance

    • UOPT(f)= maxsE µ(f(s))

On a linear state space
On a linear state space fuzzy pattern-matching indices

Pessimistic qualitative utility of binary acts xay with x y
Pessimistic qualitative utility of binary acts fuzzy pattern-matching indices xAy, with µ(x) > µ(y):

  • xAy (s) = x if A occurs = y if its complement Ac occurs

    UPES(xAy) = median {µ(x), N(A), µ(y)}

  • Interpretation: If the agent is sure enough of A, it is as if the consequence is x: UPES(f) = µF(x)

    If he is not sure about A it is as if the consequence is y: UPES(f) = µF(y)

    Otherwise, utility reflects certainty: UPES(f) = N(A)

  • WITH UOPT(f) : replace N(A) by (A)

Representation theorem for pessimistic possibilistic criteria
Representation theorem fuzzy pattern-matching indices for pessimistic possibilistic criteria

  • Suppose the preference relation a on acts obeys the following properties:

    • (XS, a) is a complete preorder.

    • there are two acts such that f a g.

    •  A, f, x, y constant,x  a y  xAfyAf

    • if f >a h and g >a h imply f g >a h

    • if x is constant, h >a x and h >a g imply h >a xg

      then there exists a finite chain L, an L-valued necessity measure on S and an L-valued utility function u, such thata is representable by the pessimistic possibilistic criterion UPES(f).

Merits and limitations of qualitative decision theory
Merits and limitations fuzzy pattern-matching indices of qualitative decision theory

  • Provides a foundation for possibility theory

  • Possibility theory is justified by observing how a decision-maker ranks acts

  • Applies to one-shot decisions (no compensations/ accumulation effects in repeated decision steps)

  • Presupposes that consecutive qualitative value levels are distant from each other (negligibility effects)

Quantitative possibility theory
Quantitative possibility theory fuzzy pattern-matching indices

  • Membership functions of fuzzy sets

    • Natural language descriptions pertaining to numerical universes (fuzzy numbers)

    • Results of fuzzy clustering

      Semantics: metrics, proximity to prototypes

  • Upper probability bound

    • Random experiments with imprecise outcomes

    • Consonant approximations of convex probability sets

      Semantics: frequentist, subjectivist (gambles)...

Quantitative possibility theory1
Quantitative possibility theory fuzzy pattern-matching indices

  • Orders of magnitude of very small probabilities

    degrees of impossibility k(A) ranging on integers k(A) = n iff P(A) = en

  • Likelihood functions (P(A| x), where x varies) behave like possibility distributions

    P(A| B) ≤ maxx B P(A| x)

Possibility as upper probability
POSSIBILITY AS fuzzy pattern-matching indices UPPER PROBABILITY

  • Given a numerical possibility distribution p, define P(p) = {Probabilities P | P(A) ≤ (A) for all A}

  • Then, generally it holds that (A) = sup {P(A) | P P(p)} N(A) = inf {P(A) | P P(p)}

  • So p is a faithful representation of a family of probability measures.

From confidence sets to possibility distributions
From confidence sets to possibility distributions fuzzy pattern-matching indices

Consider a nested family of sets E1E2 …  En

a set of positive numbers a1 …an in [0, 1]

and the family of probability functions

P = {P | P(Ei) ≥ ai for all i}.

Pis always representable by means of a possibility measure. Its possibility distribution is precisely

πx = mini max(µEi, 1 – ai)

Random set view
Random set view fuzzy pattern-matching indices

  • Let mi = i – i+1 then m1 +… + mn = 1

    A basic probability assignment (SHAFER)

  • π(s) = ∑i: sAi mi (one point-coverage function)

  • Only in the consonant case can m be recalculated from π

Conditional possibility measures
CONDITIONAL POSSIBILITY MEASURES fuzzy pattern-matching indices

  • A Coxian axiom(A C) = (A |C)*(C), with * = product

    Then: (A |C) = (A C)/ (C)

    N(A|C) = 1 – (Ac | C)

    Dempster rule of conditioning (preserves s-maxitivity)

    For the revision of possibility distributions: minimal change of  when N(C) = 1.

    It improves the state of information (reduction of focal elements)

Bayesian possibilistic conditioning
Bayesian possibilistic conditioning fuzzy pattern-matching indices

(A |b C) = sup{P(A|C), P ≤ , P(C) > 0}

N(A |b C) = inf{P(A|C), P ≤ , P(C) > 0}

It is still a possibility measure π(s |b C) = π(s)max(1, 1/( π(s) + N(C)))

It can be shownthat:

(A |b C) = (A C)/ ((A C) + N(AcC))

N(A|bC) = N(A C) / (N(A C) + P(AcC))

= 1 – (Ac |b C)

For inference from generic knowledge based on observations

Possibility probability transformations
Possibility-Probability transformations fuzzy pattern-matching indices

  • Why ?

    • fusion of heterogeneous data

    • decision-making : betting according to a possibility distribution leads to probability.

    • Extraction of a representative value

    • Simplified non-parametric imprecise probabilistic models

Elementary forms of probability-possibility transformations exist for a long time

  • POSS PROB: Laplace indifference principle  “ All that is equipossible is equiprobable ” = changing a uniform possibility distribution into a uniform probability distribution

  • PROB POSS: Confidence intervals Replacing a probability distribution by an interval A with a confidence level c.

    • It defines a possibility distribution

    • π(x) = 1 if x  A,

      = 1 – c if x  A

Possibility probability transformations basic principles
Possibility-Probability transformations : exist for a long timeBASIC PRINCIPLES

  • Possibility probability consistency: P ≤ 

  • Preserving the ordering of events : P(A) ≥ P(B) (A) ≥ (B) or elementary events only(x) > (x') if and only if p(x) > p(x')(orderpreservation)

  • Informational criteria:

    from  to P: Preservation of symmetries

    (Shapley value rather than maximal entropy)

    from P to : optimize information content

    (Maximization or minimisation of specificity

From objective probability to possibility
From OBJECTIVE probability to possibility : exist for a long time

  • Rationale : given a probability p, try and preserve as much information as possible

  • Select a most specific element of the set PI(P) = {:  ≥ P} of possibility measures dominating P such that  (x) >  (x') iff p(x) > p(x')

  • may be weakened into : p(x) > p(x')implies (x) >  (x')

  • The result is i = j=i,…n pi

    (case of no ties)

From probability to possibility continuous case
From probability to possibility : Continuous case exist for a long time

  • The possibility distribution  obtained by transforming p encodes then family of confidence intervals around the mode of p.

  • The a-cut of  is the (1- a)-confidence interval of p

  • The optimal symmetric transform of the uniform probability distribution is the triangular fuzzy number

  • The symmetric triangular fuzzy number (STFN) is a covering approximation of any probability with unimodal symmetric density p with the same mode.

  • In other words the a-cut of a STFN contains the (1- a)-confidence interval of any such p.

From probability to possibility continuous case1
From probability to possibility : Continuous case exist for a long time

  • IL = {x, p(x) ≥ } =[aL, aL+ L] is the interval of length L with maximal probability

  • The most specific possibility distribution dominating p is π such that L > 0, π(aL) = π(aL+ L) = 1 – P(IL).


Possibilistic view of probabilistic inequalities
Possibilistic view of probabilistic inequalities exist for a long time

  • Chebyshev inequality defines a possibility distribution that dominates any density with given mean and variance.

  • The symmetric triangular fuzzy number (STFN) defines a possibility distribution that optimally dominates anysymmetric density with given mode and bounded support.

From possibility to probability
From possibility to probability exist for a long time

  • Idea (Kaufmann, Yager, Chanas):

    • Pick a number  in [0, 1] at random

    • Pick an element at random in the -cut of π.

      a generalized Laplacean indifference principle : change alpha-cuts into uniform probability distributions.

  • Rationale : minimise arbitrariness by preserving the symmetry properties of the representation.

  • The resulting probability distribution is:

    • The centre of gravity of the polyhedron P(p)

    • The pignistic transformation of belief functions (Smets)

    • The Shapley value of the unanimity game N in game theory.

  • Subjective possibility distributions

    • Starting point : exploit the betting approach to subjective probability

    • A critique: The agent is forced to be additive by the rules of exchangeable bets.

      • For instance, the agent provides a uniform probability distribution on a finite set whether (s)he knows nothing about the concerned phenomenon, or if (s)he knows the concerned phenomenon is purely random.

    • Idea : It is assumed that a subjective probability supplied by an agent is only a trace of the agent's belief.

    Subjective possibility distributions1

    • Assumption 1: Beliefs can be modelled by belief functions

      • (masses m(A) summing to 1 assigned to subsets A).

    • Assumption 2: The agent uses a probability function induced by his or her beliefs, using the pignistic transformation (Smets, 1990) or Shapley value.

    • Method : reconstruct the underlying belief function from the probability provided by the agent by choosing among the isopignistic ones.

    Subjective possibility distributions2

    • There are clearly several belief functions with a prescribed Shapley value.

  • Consider the least informative of those, in the sense of a non-specificity index (expected cardinality of the random set)

    I(m) = ∑m(A)card(A).

  • RESULT : The least informative belief function whose Shapley value is p is unique and consonant.

  • Subjective possibility distributions3

    • The least specific belief function in the sense of maximizing I(m) is characterized by

    • i = j=1,n min(pj, pi).

    • It is a probability-possibility transformation, previously suggested in 1983: This is the unique possibility distribution whose Shapley value is p.

    • It gives results that are less specific than the confidence interval approach to objective probability.

    Applications of quantitative possibility
    Applications of quantitative possibility exist for a long time

    • Representing incomplete probabilistic data for uncertainty propagation in computations

    • (but fuzzy interval analysis based on the extension principle differs from conservative probabilistic risk analysis)

    • Systematizing some statistical methods (confidence intervals, likelihood functions, probabilistic inequalities)

    • Defuzzification based on Choquet integral (linear with fuzzy number addition)

    Applications of quantitative possibility1
    Applications of quantitative possibility exist for a long time

    • Uncertain reasoning : Possibilistic nets are a counterpart to Bayesian nets that copes with incomplete data. Similar algorithmic properties under Dempster conditioning (Kruse team)

    • Data fusion : well suited for mergingheterogeneous information on numerical data (linguistic, statistics, confidence intervals) (Bloch)

    • Risk analysis : uncertainty propagation using fuzzy arithmetics, and random interval arithmetics when statistical data is incomplete (Lodwick, Ferson)

    • Non-parametric conservative modelling of imprecision in measurements (Mauris)

    Perspectives exist for a long time

    Quantitative possibility is not as well understood as probability theory.

    • Objective vs. subjective possibility (a la De Finetti)

    • How to use possibilistic conditioning in inference tasks ?

    • Bridge the gap with statistics and the confidence interval literature (Fisher, likelihood reasoning)

    • Higher-order modes of fuzzy intervals (variance, …) and links with fuzzy random variables

    • Quantitative possibilistic expectations : decision-theoretic characterisation ?

    Conclusion exist for a long time

    • Possibility theory is a simple and versatile tool for modeling uncertainty

    • A unifying framework for modeling and merging linguistic knowledge and statistical data

    • Useful to account for missing information in reasoning tasks and risk analysis

    • A bridge between logic-based AI and probabilistic reasoning

    Properties of inference
    Properties of inference exist for a long time|=

    • A |=π A if A ≠ Ø (restricted reflexivity)

    • if A ≠ Ø, then A |=πØ never holds (consistency preservation)

    • The set {B: A |=π B} is deductively closed

      -If A  B and C |=π A then C |=π B

      (right weakening rule RW)

      -If A |=π B and A |=π C then A |=π B C

      (Right AND)

    Properties of inference1
    Properties of inference exist for a long time|=

    • If A |=π C ; B |=π C then A  B |=π C (Left OR)

    • If A |=π B and A  B |=π C then A |=π C

      (cut, weak transitivity )

      (But if A normally implies B which normally implies C, then A may not imply C)

    • If A |=π B and if A |=π Cc is false, then A  C |=π B (rational monotony RM)

      If B is normally expected when A holds,then B is expected to hold when both A and C hold, unless it is that A normally implies not C

    Representation theorem for possibilistic entailment

    • Let |= be a consequence relation on 2S x 2S

    • Define an induced partial relation on subsets as

      A > B iff A  B |= Bc for A ≠ 

    • Theorem: If |= satisfies restricted reflexivity, right weakening, rational monotony, Right AND and Left OR, then A > B is the strict part of a possibility relation on events.

      So a consequence relation satisfying the above properties is representable by possibilistic inference, and induces a complete plausibility preordering on the states.

    A possibilistic approach to modeling rules

    • A generic rule « if A then B » is modelled by P(AB) > P(Ac B).

      • This is a constraint that delimits a set of possibility

      • distributions on the set of interpretations of the language

  • Applying the minimal specificity principle: P(AB) = P(ABc ) = P(Ac Bc ) > P(Ac B).

  • Modelling a set of default rules as a possibility distribution

    • ∆ = {Ai Bi, i = 1,n}

    • ∆ defines a set of constraints on possibility distributions (Ai Bi) > (Ai ¬Bi), i = 1,…n

    • • (∆) = set of feasible π's with respect to ∆

      • One may compute * : the least specific possibility distribution in (∆)

    Plausible inference from a set of default rules
    Plausible inference from a set of default rules DISTRIBUTION

    What « ∆ implies A  B » means

    • Cautious inference

      ∆ = A  B iff

      For all P  (∆), P(AB) > P(Ac B).

    • Possibilistic inference

      ∆ =* A  B iff *(AB) > *(Ac B) for the least specific possibility measure in (∆).

      Leads to a stratification of ∆ according to N*(Ac B)

    Possibilistic logic
    Possibilistic logic DISTRIBUTION

    • A possibilistic knowledge base is an ordered set of propositional or 1st order formulas pi

    • K = {(pii), i = 1,n} where i > 0 is the level of priority or validity of pi

      i = 1 means certainty.

      i = 0 means ignorance

    • Captures the idea of uncertain knowledge in an ordinal setting

    Possibilistic logic1
    Possibilistic logic DISTRIBUTION

    • Axiomatization:

      All axioms of classical logic with weight 1

      Weighted modus ponens{(p ), (¬p  q )} |- (q min(,))

      OLD! Goes back to Aristotle school

      Idea: the validity of a chain of uncertain deductions is the validity of its weakest link

      Syntactic inference K |-(p ) is well-defined

    Possibilistic logic2
    Possibilistic logic DISTRIBUTION

    • Inconsistency becomes a graded notion inc(K) = sup{, K |- (,)}

    • Refutation and resolution methods extend K |- (p ) iffK {(p 1)} |- (,)

    • Inference with a partially inconsistent knowledge base becomes non-trivial and nonmonotonic K |-nt p iff K |- (p ) and  > inc(K)

    Semantics of possibilistic logic
    Semantics of possibilistic logic DISTRIBUTION

    • A weighted formula has a fuzzy set of models .

    • If A = [p] is the set of models of p (subset of S),

    • |-(p a) means N(A) ≥ 

      The least specific possibility distribution induced by |-(p a) is:

      π(p a)(s) = max(µA(s), 1 – )

      = 1 if p is true in state s

      = 1 –  if p is false in state s

    Semantics of possibilistic logic1
    Semantics of possibilistic logic DISTRIBUTION

    • The fuzzy set of models of K is the intersection of the fuzzy sets of models of {(pii), i = 1,n}

    • πK(s)= mini=1,n {1 – i | s [pi]}

      determined by the highest priority formula violated by s

    • The p. d. πK is the least informed state of partial knowledge compatible with K

    Soundness and completeness
    Soundness and completeness DISTRIBUTION

    • Monotonic semantic entailment follows Zadeh’s entailment principle K |= (p, ) stands for πK ≤ π(p a)

      Theorem: K |- (p, ) iff K |= (p )

    • For the non-trivial inference under inconsistency: {(p 1)}  K |-nt q iff (q  p) > (¬q  p)

    Possibilistic vs fuzzy logics

    Possibilistic logic DISTRIBUTION

    Formulas are Boolean

    Truth is 2-valued

    Weighted formulas have fuzzy sets of models

    Validity is many-valued

    degrees of validity are not compositional except for conjunctions

    Represents uncertainty

    Fuzzy logic (Pavelka)

    Formulas are non-Boolean

    Truth is many-valued

    Weighted formulas have crisp sets of models (cuts)

    Validity is Boolean

    degrees of truth are compositional

    represents real functions by means of logical formulas

    Possibilistic vs. fuzzy logics

    Example if bird then fly if penguin then bird if penguin then not fly

    • K = {b  f, p  b, p  ¬f}

     = material implication

    • K  {b} |- f; K  {p} |- contradiction

  • using possibilistic logic:  < min(,)

    K = {(b  f ), (p  b ), (p  ¬f )}

    then K  {(b, 1)} |- (f ) and K  {(b, 1)} |-nt f

  • Inc(K{(p, 1), (b, 1)} = 

  • K  {(p, 1), (b, 1)} |- (¬f, min(,))

  • Hence K  {(p, 1), (b, 1)} |-nt ¬f

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