0000000000190045

AUTHOR

Angelo Gilio

showing 39 related works from this author

Iterated Conditionals and Characterization of P-Entailment

2021

In this paper we deepen, in the setting of coherence, some results obtained in recent papers on the notion of p-entailment of Adams and its relationship with conjoined and iterated conditionals. We recall that conjoined and iterated conditionals are suitably defined in the framework of conditional random quantities. Given a family \(\mathcal {F}\) of n conditional events \(\{E_{1}|H_{1},\ldots , E_{n}|H_{n}\}\) we denote by \(\mathcal {C}(\mathcal {F})=(E_{1}|H_{1})\wedge \cdots \wedge (E_{n}|H_{n})\) the conjunction of the conditional events in \(\mathcal F\). We introduce the iterated conditional \(\mathcal {C}(\mathcal {F}_{2})|\mathcal {C}(\mathcal {F}_{1})\), where \(\mathcal {F}_{1}\)…

CombinatoricsPhysicsSettore MAT/06 - Probabilita' E Statistica MatematicaCoherence Conditional events Conditional random quantitiesConditional previsions Conjoined conditionals Iterated conditionalsProbabilistic entailment.Iterated functionProduct (mathematics)Characterization (mathematics)
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Probabilistic inferences from conjoined to iterated conditionals

2017

Abstract There is wide support in logic, philosophy, and psychology for the hypothesis that the probability of the indicative conditional of natural language, P ( if A then B ) , is the conditional probability of B given A, P ( B | A ) . We identify a conditional which is such that P ( if A then B ) = P ( B | A ) with de Finetti's conditional event, B | A . An objection to making this identification in the past was that it appeared unclear how to form compounds and iterations of conditional events. In this paper, we illustrate how to overcome this objection with a probabilistic analysis, based on coherence, of these compounds and iterations. We interpret the compounds and iterations as cond…

Indicative conditionalCounterfactual conditionalSettore MAT/06 - Probabilita' E Statistica MatematicaCompound conditionalInference02 engineering and technology050105 experimental psychologyTheoretical Computer ScienceArtificial Intelligence0202 electrical engineering electronic engineering information engineeringFOS: Mathematics0501 psychology and cognitive sciencesEvent (probability theory)Discrete mathematicsApplied Mathematics05 social sciencesProbability (math.PR)Probabilistic logicConditional probabilityCoherence (philosophical gambling strategy)Mathematics - Logic03b48 60A99Settore MAT/01 - Logica MatematicaLogical biconditionalCenteringp-EntailmentIterated conditional020201 artificial intelligence & image processingCounterfactualLogic (math.LO)CoherenceSoftwareMathematics - Probability
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Generalized Logical Operations among Conditional Events

2018

We generalize, by a progressive procedure, the notions of conjunction and disjunction of two conditional events to the case of n conditional events. In our coherence-based approach, conjunctions and disjunctions are suitable conditional random quantities. We define the notion of negation, by verifying De Morgan’s Laws. We also show that conjunction and disjunction satisfy the associative and commutative properties, and a monotonicity property. Then, we give some results on coherence of prevision assessments for some families of compounded conditionals; in particular we examine the Frechet-Hoeffding bounds. Moreover, we study the reverse probabilistic inference from the conjunction $\mathcal…

FOS: Computer and information sciencesSettore MAT/06 - Probabilita' E Statistica MatematicaComputer Science - Artificial IntelligenceComputer scienceMonotonic functionProbabilistic reasoning02 engineering and technologyCommutative Algebra (math.AC)Conditional random quantitieFréchet-Hoeffding boundCoherent extensionNegationArtificial IntelligenceQuasi conjunction0202 electrical engineering electronic engineering information engineeringFOS: MathematicsCoherent prevision assessmentConditional eventNon-monotonic logicRule of inferenceCommutative propertyAssociative propertyDiscrete mathematicsProbability (math.PR)Probabilistic logicOrder (ring theory)ConjunctionMathematics - LogicCoherence (philosophical gambling strategy)p-entailmentProbabilistic inferenceMathematics - Commutative AlgebraConjunction (grammar)Artificial Intelligence (cs.AI)020201 artificial intelligence & image processingInference ruleNegationLogic (math.LO)Mathematics - ProbabilityDisjunction
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On conditional probabilities and their canonical extensions to Boolean algebras of compound conditionals

2023

In this paper we investigate canonical extensions of conditional probabilities to Boolean algebras of conditionals. Before entering into the probabilistic setting, we first prove that the lattice order relation of every Boolean algebra of conditionals can be characterized in terms of the well-known order relation given by Goodman and Nguyen. Then, as an interesting methodological tool, we show that canonical extensions behave well with respect to conditional subalgebras. As a consequence, we prove that a canonical extension and its original conditional probability agree on basic conditionals. Moreover, we verify that the probability of conjunctions and disjunctions of conditionals in a rece…

Conditional subalgebraCanonical extensionSettore MAT/06 - Probabilita' E Statistica MatematicaArtificial IntelligenceApplied MathematicsConditional probabilityNonmonotonic reasoningConjunction and disjunction of conditionalBoolean algebras of conditionalSoftwareTheoretical Computer ScienceInternational Journal of Approximate Reasoning
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Probabilistic entailment and iterated conditionals

2020

In this paper we exploit the notions of conjoined and iterated conditionals, which are defined in the setting of coherence by means of suitable conditional random quantities with values in the interval $[0,1]$. We examine the iterated conditional $(B|K)|(A|H)$, by showing that $A|H$ p-entails $B|K$ if and only if $(B|K)|(A|H) = 1$. Then, we show that a p-consistent family $\mathcal{F}=\{E_1|H_1,E_2|H_2\}$ p-entails a conditional event $E_3|H_3$ if and only if $E_3|H_3=1$, or $(E_3|H_3)|QC(\mathcal{S})=1$ for some nonempty subset $\mathcal{S}$ of $\mathcal{F}$, where $QC(\mathcal{S})$ is the quasi conjunction of the conditional events in $\mathcal{S}$. Then, we examine the inference rules $A…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaIterated functionInterval (graph theory)Settore MAT/01 - Logica MatematicaCoherence Conditional random quantities p-entailment Inference rules.MathematicsStrict conditional
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Assessment of qualitative judgements for conditional events in expert systems

1991

business.industryComputer scienceConditional events; qualitative probabilities.; linear and nonlinear systems; numerical probabilities; coherenceConditional eventsqualitative probabilitiesExpert elicitationConditional probability distributioncomputer.software_genreMachine learningExpert systemcoherencenumerical probabilitieslinear and nonlinear systemsArtificial intelligencebusinesscomputer
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Generalized probabilistic modus ponens

2017

Modus ponens (from A and “if A then C” infer C) is one of the most basic inference rules. The probabilistic modus ponens allows for managing uncertainty by transmitting assigned uncertainties from the premises to the conclusion (i.e., from P(A) and P(C|A) infer P(C)). In this paper, we generalize the probabilistic modus ponens by replacing A by the conditional event A|H. The resulting inference rule involves iterated conditionals (formalized by conditional random quantities) and propagates previsions from the premises to the conclusion. Interestingly, the propagation rules for the lower and the upper bounds on the conclusion of the generalized probabilistic modus ponens coincide with the re…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaProbabilistic logicConjoined conditionalPrevision0102 computer and information sciences02 engineering and technologyCoherence (philosophical gambling strategy)Settore MAT/01 - Logica MatematicaModus ponen01 natural sciencesConditional random quantitieTheoretical Computer ScienceModus ponendo tollens010201 computation theory & mathematicsIterated functionComputer Science0202 electrical engineering electronic engineering information engineeringIterated conditional020201 artificial intelligence & image processingRule of inferenceModus ponensCoherenceEvent (probability theory)Mathematics
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Conjunction of Conditional Events and t-Norms

2019

We study the relationship between a notion of conjunction among conditional events, introduced in recent papers, and the notion of Frank t-norm. By examining different cases, in the setting of coherence, we show each time that the conjunction coincides with a suitable Frank t-norm. In particular, the conjunction may coincide with the Product t-norm, the Minimum t-norm, and Lukasiewicz t-norm. We show by a counterexample, that the prevision assessments obtained by Lukasiewicz t-norm may be not coherent. Then, we give some conditions of coherence when using Lukasiewicz t-norm

Frank t-norm.Settore MAT/06 - Probabilita' E Statistica MatematicaConjunction02 engineering and technologyCoherence (statistics)01 natural sciencesConjunction (grammar)Mathematics::Logic010104 statistics & probabilitySettore SECS-S/06 -Metodi Mat. dell'Economia e d. Scienze Attuariali e Finanz.Product (mathematics)0202 electrical engineering electronic engineering information engineeringCalculus020201 artificial intelligence & image processing0101 mathematicsCoherenceConditional EventCounterexampleMathematics
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On compound and iterated conditionals

2021

We illustrate the notions of compound and iterated conditionals introduced, in recent papers, as suitable conditional random quantities, in the framework of coherence. We motivate our definitions by examining some concrete examples. Our logical operations among conditional events satisfy the basic probabilistic properties valid for unconditional events. We show that some, intuitively acceptable, compound sentences on conditionals can be analyzed in a rigorous way in terms of suitable iterated conditionals. We discuss the Import-Export principle, which is not valid in our approach, by also examining the inference from a material conditional to the associated conditional event. Then, we illus…

Settore MAT/06 - Probabilita' E Statistica MatematicaInference rulesp-validityConditional eventsIterated conditionalConjunctionSettore M-FIL/02 - Logica E Filosofia Della ScienzaConditional random quantitiesp-entailmentImport-Export principleCoherenceCoherence Conditional events Conditional random quantities Conjunction Disjunction Iterated conditional Inference rules p-validity p-entailment Import-Export principle.Disjunction
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Probabilistic entailment in the setting of coherence: The role of quasi conjunction and inclusion relation

2013

In this paper, by adopting a coherence-based probabilistic approach to default reasoning, we focus the study on the logical operation of quasi conjunction and the Goodman-Nguyen inclusion relation for conditional events. We recall that quasi conjunction is a basic notion for defining consistency of conditional knowledge bases. By deepening some results given in a previous paper we show that, given any finite family of conditional events F and any nonempty subset S of F, the family F p-entails the quasi conjunction C(S); then, given any conditional event E|H, we analyze the equivalence between p-entailment of E|H from F and p-entailment of E|H from C(S), where S is some nonempty subset of F.…

FOS: Computer and information sciencesClass (set theory)Goodman–Nguyen’s inclusion relationQAND ruleSettore MAT/06 - Probabilita' E Statistica MatematicaComputer Science - Artificial IntelligenceMathematics - Statistics TheoryStatistics Theory (math.ST)Logical consequencegoodman-nguyen's inclusion relationTheoretical Computer ScienceArtificial IntelligenceQuasi conjunctionFOS: MathematicsEquivalence (measure theory)MathematicsEvent (probability theory)Discrete mathematicsSettore INF/01 - InformaticaApplied MathematicsProbability (math.PR)quasi conjunction; goodman-nguyen inclusion relation; qand rule; coherence; probabilistic default reasoning; p-entailment; goodman-nguyen's inclusion relationProbabilistic logicCoherence (statistics)Conjunction (grammar)Greatest elementArtificial Intelligence (cs.AI)Probabilistic default reasoninggoodman-nguyen inclusion relationp-EntailmentCoherenceSoftwareMathematics - Probability
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Coherent Conditional Previsions and Proper Scoring Rules

2012

In this paper we study the relationship between the notion of coherence for conditional prevision assessments on a family of finite conditional random quantities and the notion of admissibility with respect to bounded strictly proper scoring rules. Our work extends recent results given by the last two authors of this paper on the equivalence between coherence and admissibility for conditional probability assessments. In order to prove that admissibility implies coherence a key role is played by the notion of Bregman divergence.

Settore MAT/06 - Probabilita' E Statistica Matematicabregman divergenceproper scor- ing rulesConditional prevision assessmentsconditional scoring rulesstrong dominanceConditional probabilityweak dominanceCoherence (statistics)Bregman divergenceConditional prevision assessments coherence proper scoring rules conditional scoring rules weak dominance strong dominance admissibility Bregman divergence.proper scoring rulescoherenceBounded functionKey (cryptography)admissibilityConditional prevision assessments; conditional scoring rules; admissibility; proper scor- ing rules; weak dominance; strong dominanceEquivalence (measure theory)Mathematical economicsconditional prevision assessments; strong dominance; admissibility; proper scoring rules; bregman divergence; weak dominance; conditional scoring rules; coherenceMathematics
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Conjunction and Disjunction Among Conditional Events

2017

We generalize, in the setting of coherence, the notions of conjunction and disjunction of two conditional events to the case of n conditional events. Given a prevision assessment on the conjunction of two conditional events, we study the set of coherent extensions for the probabilities of the two conditional events. Then, we introduce by a progressive procedure the notions of conjunction and disjunction for n conditional events. Moreover, by defining the negation of conjunction and of disjunction, we show that De Morgan’s Laws still hold. We also show that the associative and commutative properties are satisfied. Finally, we examine in detail the conjunction for a family \(\mathcal F\) of t…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaComputer scienceConditional events · Conditional random quantities · Con- junction · Disjunction · Negation · Quasi conjunction · Coherent previ- sion assessments · Coherent extensions · De Morgan’s Laws02 engineering and technologyCoherence (philosophical gambling strategy)Settore MAT/01 - Logica Matematica01 natural sciencesDe Morgan's lawsConjunction (grammar)Set (abstract data type)010104 statistics & probabilitysymbols.namesakeNegation0202 electrical engineering electronic engineering information engineeringsymbols020201 artificial intelligence & image processing0101 mathematicsAlgorithmCommutative propertyAssociative propertyEvent (probability theory)
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Coherence Checking and Propagation of Lower Probability Bounds

2003

In this paper we use imprecise probabilities, based on a concept of generalized coherence (g-coherence), for the management of uncertain knowledge and vague information. We face the problem of reducing the computational difficulties in g-coherence checking and propagation of lower conditional probability bounds. We examine a procedure, based on linear systems with a reduced number of unknowns, for the checking of g-coherence. We propose an iterative algorithm to determine the reduced linear systems. Based on the same ideas, we give an algorithm for the propagation of lower probability bounds. We also give some theoretical results that allow, by suitably modifying our algorithms, the g-coher…

Probability boxMathematical optimizationSettore MAT/06 - Probabilita' E Statistica MatematicaPosterior probabilitynon relevant gainLaw of total probabilityConditional probabilitybasic setsbasic sets; basic sets.; g-coherence checking; lower conditional probability bounds; non relevant gains; propagationCoherence (statistics)Conditional probability distributiong-coherence checking; lower conditional probability bounds; non relevant gainsImprecise probabilityTheoretical Computer Sciencelower conditional probability boundRegular conditional probabilitynon relevant gainspropagationlower conditional probability boundsGeometry and Topologyg-coherence checkingSoftwareMathematics
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Probabilities of conditionals and previsions of iterated conditionals

2019

Abstract We analyze selected iterated conditionals in the framework of conditional random quantities. We point out that it is instructive to examine Lewis's triviality result, which shows the conditions a conditional must satisfy for its probability to be the conditional probability. In our approach, however, we avoid triviality because the import-export principle is invalid. We then analyze an example of reasoning under partial knowledge where, given a conditional if A then C as information, the probability of A should intuitively increase. We explain this intuition by making some implicit background information explicit. We consider several (generalized) iterated conditionals, which allow…

Background informationSettore MAT/06 - Probabilita' E Statistica MatematicaInference02 engineering and technologyConditional probabilities and previsionTheoretical Computer ScienceConditional random quantitieAffirmation of the ConsequentArtificial Intelligence020204 information systemsFOS: Mathematics0202 electrical engineering electronic engineering information engineeringConjoined and iterated conditionalMathematicsIndependence and uncorrelation.Applied MathematicsProbability (math.PR)Conditional probabilityMathematics - LogicTrivialityIterated function020201 artificial intelligence & image processingLogic (math.LO)Mathematical economicsCoherenceSoftwareMathematics - ProbabilityIntuition
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Probabilistic Logic under Coherence: Complexity and Algorithms

2005

In previous work [V. Biazzo, A. Gilio, T. Lukasiewicz and G. Sanfilippo, Probabilistic logic under coherence, model-theoretic probabilistic logic, and default reasoning in System P, Journal of Applied Non-Classical Logics 12(2) (2002) 189---213.], we have explored the relationship between probabilistic reasoning under coherence and model-theoretic probabilistic reasoning. In particular, we have shown that the notions of g-coherence and of g-coherent entailment in probabilistic reasoning under coherence can be expressed by combining notions in model-theoretic probabilistic reasoning with concepts from default reasoning. In this paper, we continue this line of research. Based on the above sem…

conditional probability assessmentSettore MAT/06 - Probabilita' E Statistica MatematicaDivergence-from-randomness modelalgorithmsprobabilistic logicConditional probability assessments; probabilistic logic; g-coherence; g-coherent entailment; complexity and algorithms.Artificial IntelligenceProbabilistic logic networkprobabilistic logic under coherenceConditional probability assessmentsProbabilistic analysis of algorithmsNon-monotonic logicconditional constraintMathematicsg-coherent entailmentConditional probability assessments probabilistic logic g-coherence g-coherent entailment complexity and algorithms.Reasoning systemcomputational complexitymodel-theoretic probabilistic logicApplied Mathematicscomplexity and algorithmsProbabilistic logiclogical constraintProbabilistic argumentationg-coherenceconditional probability assessment logical constraint conditional constraint probabilistic logic under coherence model-theoretic probabilistic logic g-coherence g-coherent entailment computational complexity algorithmsProbabilistic CTLalgorithms; computational complexity; conditional constraint; conditional probability assessment; g-coherence; g-coherent entailment; logical constraint; model-theoretic probabilistic logic; probabilistic logic under coherenceAlgorithmAnnals of Mathematics and Artificial Intelligence
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On the checking of g-coherence of conditional probability bounds

2003

We illustrate an approach to uncertain knowledge based on lower conditional probability bounds. We exploit the coherence principle of de Finetti and a related notion of generalized coherence (g-coherence), which is equivalent to the "avoiding uniform loss" property introduced by Walley for lower and upper probabilities. Based on the additive structure of random gains, we define suitable notions of non relevant gains and of basic sets of variables. Exploiting them, the linear systems in our algorithms can work with reduced sets of variables and/or constraints. In this paper, we illustrate the notions of non relevant gain and of basic set by examining several cases of imprecise assessments d…

Mathematical optimizationSettore MAT/06 - Probabilita' E Statistica MatematicaPosterior probabilityConditional probability tablealgorithmslower conditional probability boundRegular conditional probabilityalgorithms; generalized coherence; linear systems; lower conditional probability bounds; probabilistic reasoning; reduced sets of variables and constraints.Artificial Intelligencelinear systemprobabilistic reasoninggeneralized coherenceMathematicsDiscrete mathematicsreduced sets of variables and constraintsalgorithmlinear systemsProbabilistic logicLaw of total probabilityConditional probabilityCoherence (philosophical gambling strategy)Conditional probability distributionControl and Systems Engineeringlower conditional probability boundsSoftwareInformation Systems
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Quasi conjunction and p-entailment in nonmonotonic reasoning

2010

We study, in the setting of coherence, the extension of a probability assessment defined on n conditional events to their quasi conjunction. We consider, in particular, two special cases of logical dependencies; moreover, we examine the relationship between the notion of p-entailment of Adams and the inclusion relation of Goodman and Nguyen. We also study the probabilistic semantics of the QAND rule of Dubois and Prade; then, we give a theoretical result on p-entailment.

Settore MAT/06 - Probabilita' E Statistica MatematicaProbability assessmentProbabilistic semanticsInclusion relationExtension (predicate logic)Coherence (statistics)Logical consequenceConjunction (grammar)Coherence lower/upper probability bounds quasi conjunction QAND rule p-entailmentCalculusp-entailment.; quasi conjunction; lower/upper probability bounds; qand rule; coherence; p-entailmentNon-monotonic logicAlgorithmMathematics
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Compound conditionals, Fr\'echet-Hoeffding bounds, and Frank t-norms

2021

Abstract In this paper we consider compound conditionals, Frechet-Hoeffding bounds and the probabilistic interpretation of Frank t-norms. By studying the solvability of suitable linear systems, we show under logical independence the sharpness of the Frechet-Hoeffding bounds for the prevision of conjunctions and disjunctions of n conditional events. In addition, we illustrate some details in the case of three conditional events. We study the set of all coherent prevision assessments on a family containing n conditional events and their conjunction, by verifying that it is convex. We discuss the case where the prevision of conjunctions is assessed by Lukasiewicz t-norms and we give explicit s…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaLogical independenceFrank t-normsApplied MathematicsLinear systemProbabilistic logicRegular polygon02 engineering and technologyConjunction and disjunctionConditional previsionTheoretical Computer ScienceConvexityFréchet-Hoeffding boundArtificial Intelligence020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingPairwise comparisonCoherenceSoftwareMathematics - ProbabilityCounterexampleMathematicsCorresponding conditional
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Quasi conjunction, quasi disjunction, t-norms and t-conorms: Probabilistic aspects

2013

We make a probabilistic analysis related to some inference rules which play an important role in nonmonotonic reasoning. In a coherence-based setting, we study the extensions of a probability assessment defined on $n$ conditional events to their quasi conjunction, and by exploiting duality, to their quasi disjunction. The lower and upper bounds coincide with some well known t-norms and t-conorms: minimum, product, Lukasiewicz, and Hamacher t-norms and their dual t-conorms. On this basis we obtain Quasi And and Quasi Or rules. These are rules for which any finite family of conditional events p-entails the associated quasi conjunction and quasi disjunction. We examine some cases of logical de…

FOS: Computer and information sciencesSettore MAT/06 - Probabilita' E Statistica MatematicaInformation Systems and ManagementComputer Science - Artificial Intelligencet-Norms/conormDuality (mathematics)goodman-nguyen inclusion relation; lower/upper probability bounds; t-norms/conorms; generalized loop rule; coherence; quasi conjunction/disjunctionComputer Science::Artificial IntelligenceTheoretical Computer ScienceArtificial IntelligenceFOS: MathematicsProbabilistic analysis of algorithmsNon-monotonic logicRule of inferenceLower/upper probability boundGoodman–Nguyen inclusion relationMathematicsEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniDiscrete mathematicsInterpretation (logic)Probability (math.PR)Probabilistic logicCoherence (philosophical gambling strategy)Generalized Loop ruleComputer Science ApplicationsAlgebraArtificial Intelligence (cs.AI)Control and Systems EngineeringQuasi conjunction/disjunctionCoherenceMathematics - ProbabilitySoftwareInformation Sciences
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Probabilistic Logic under Coherence‚ Model−Theoretic Probabilistic Logic‚ and Default Reasoning in System P

2016

We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail, we explore how probabilistic reasoning under coherence is related to model-theoretic probabilistic reasoning and to default reasoning in System P. In particular, we show that the notions of g-coherence and of g-coherent entailment can be expressed by combining notions in model-theoretic probabilistic logic with concepts from default reasoning. Moreover, we show that probabilistic reasoning under coherence is a generalization of default reasoning in System P. That is, we provide a new probabilistic semantics for System P, which neither uses infinitesimal probabilities nor atomic bound (or bi…

Deductive reasoningSettore MAT/06 - Probabilita' E Statistica MatematicaConditional probability assessments conditional constraints probabilistic logic under coherence model-theoretic probabilistic logic g-coherence g-coherent entailment defaultreasoning from conditional knowledge bases System P conditional objects.conditional constraintsLogicDefault logicStatistics::Other StatisticsProbabilistic logic networkConditional probability assessmentsprobabilistic logic under coherenceNon-monotonic logicSystem PMathematicsg-coherent entailmentHardware_MEMORYSTRUCTURESmodel-theoretic probabilistic logicbusiness.industryProbabilistic logicSystem P; g-coherence; conditional objectsCoherence (statistics)default reasoning from conditional knowledge basesProbabilistic argumentationConditional probability assessments; conditional constraints; probabilistic logic under coherence; model-theoretic probabilistic logic; g-coherence; g-coherent entailment; default reasoning from conditional knowledge bases; System P; conditional objects.Philosophyg-coherenceProbabilistic CTLArtificial intelligencebusinessAlgorithmconditional objectsJournal of Applied Non−Classical Logics
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Algebraic aspects and coherence conditions for conjoined and disjoined conditionals

2019

We deepen the study of conjoined and disjoined conditional events in the setting of coherence. These objects, differently from other approaches, are defined in the framework of conditional random quantities. We show that some well known properties, valid in the case of unconditional events, still hold in our approach to logical operations among conditional events. In particular we prove a decomposition formula and a related additive property. Then, we introduce the set of conditional constituents generated by $n$ conditional events and we show that they satisfy the basic properties valid in the case of unconditional events. We obtain a generalized inclusion-exclusion formula and we prove a …

Pure mathematicsProperty (philosophy)Settore MAT/06 - Probabilita' E Statistica MatematicaDistributivityApplied MathematicsProbability (math.PR)02 engineering and technologyCoherence (statistics)Characterization (mathematics)Settore MAT/01 - Logica Matematica60Axx 03B48Theoretical Computer ScienceCoherenceConditional random quantities Conjunction and disjunction of conditionals Decomposition formula Conditional constituents Inclusion-exclusion formulaSet (abstract data type)Artificial Intelligence020204 information systemsFOS: Mathematics0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingInclusion–exclusion principleAlgebraic numberMathematics - ProbabilitySoftwareCounterexampleMathematics
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Transitive Reasoning with Imprecise Probabilities

2015

We study probabilistically informative (weak) versions of transitivity by using suitable definitions of defaults and negated defaults in the setting of coherence and imprecise probabilities. We represent \(\text{ p-consistent }\) sequences of defaults and/or negated defaults by g-coherent imprecise probability assessments on the respective sequences of conditional events. Finally, we present the coherent probability propagation rules for Weak Transitivity and the validity of selected inference patterns by proving p-entailment of the associated knowledge bases.

Discrete mathematicsTransitive relationSettore MAT/06 - Probabilita' E Statistica MatematicaSettore INF/01 - Informaticabusiness.industryProbabilistic logicSyllogismInferenceCoherence (philosophical gambling strategy)Settore M-FIL/02 - Logica E Filosofia Della ScienzaComputer Science::Artificial IntelligenceImprecise probabilityCoherence default imprecise probability knowledge base p-consistency p-entailment reasoning syllogism weak transitivityProbability propagationKnowledge basebusinessMathematics
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Conditional Random Quantities and Iterated Conditioning in the Setting of Coherence

2013

We consider conditional random quantities (c.r.q.’s) in the setting of coherence. Given a numerical r.q. X and a non impossible event H, based on betting scheme we represent the c.r.q. X|H as the unconditional r.q. XH + μH c , where μ is the prevision assessed for X|H. We develop some elements for an algebra of c.r.q.’s, by giving a condition under which two c.r.q.’s X|H and Y|K coincide. We show that X|HK coincides with a suitable c.r.q. Y|K and we apply this representation to Bayesian updating of probabilities, by also deepening some aspects of Bayes’ formula. Then, we introduce a notion of iterated c.r.q. (X|H)|K, by analyzing its relationship with X|HK. Our notion of iterated conditiona…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaSettore INF/01 - Informaticaconditional random quantitiesCoherence (statistics)Bayesian inferencebayesian updatingcoherenceCombinatoricsconditional previsionsBayes' theoremIterated functionbayesian updating; conditional random quantities; betting scheme; conditional previsions; coherence; iterated conditioning; iterated conditioning.Coherence betting scheme conditional random quantities conditional previsions Bayesian updating iterated conditioning.Scheme (mathematics)iterated conditioningConditioningRepresentation (mathematics)betting schemeEvent (probability theory)Mathematics
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Conjunction, Disjunction and Iterated Conditioning of Conditional Events

2013

Starting from a recent paper by S. Kaufmann, we introduce a notion of conjunction of two conditional events and then we analyze it in the setting of coherence. We give a representation of the conjoined conditional and we show that this new object is a conditional random quantity, whose set of possible values normally contains the probabilities assessed for the two conditional events. We examine some cases of logical dependencies, where the conjunction is a conditional event; moreover, we give the lower and upper bounds on the conjunction. We also examine an apparent paradox concerning stochastic independence which can actually be explained in terms of uncorrelation. We briefly introduce the…

Theoretical computer scienceSettore MAT/06 - Probabilita' E Statistica MatematicaComputer scienceProbabilistic logicCoherence (philosophical gambling strategy)Conditional events conditional random quantities conjunction disjunction iterated conditionalsConjunction (grammar)Set (abstract data type)Regular conditional probabilitydisjunction; conditional events; conjunction; conditional random quantities; iterated conditionals.Iterated functionRepresentation (mathematics)Settore SECS-S/01 - StatisticaMathematical economicsEvent (probability theory)
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Transitivity in coherence-based probability logic

2016

We study probabilistically informative (weak) versions of transitivity by using suitable definitions of defaults and negated defaults in the setting of coherence and imprecise probabilities. We represent p-consistent sequences of defaults and/or negated defaults by g-coherent imprecise probability assessments on the respective sequences of conditional events. Moreover, we prove the coherent probability propagation rules for Weak Transitivity and the validity of selected inference patterns by proving p-entailment of the associated knowledge bases. Finally, we apply our results to study selected probabilistic versions of classical categorical syllogisms and construct a new version of the squa…

Square of oppositionSettore MAT/06 - Probabilita' E Statistica MatematicaTheoretical computer scienceLogicInferenceSquare of oppositionProbability logicSettore M-FIL/02 - Logica E Filosofia Della Scienza02 engineering and technologyComputer Science::Artificial Intelligence0603 philosophy ethics and religion0202 electrical engineering electronic engineering information engineeringGeneralized coherenceCategorical variableMathematicsTransitivityTransitive relationApplied MathematicsDefaultProbabilistic logicSyllogism06 humanities and the artsCoherence (statistics)Settore MAT/01 - Logica MatematicaImprecise probabilityp-EntailmentSyllogism060302 philosophyImprecise probabilityp-Consistency020201 artificial intelligence & image processingCoherenceAlgorithmJournal of Applied Logic
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Quasi Conjunction and Inclusion Relation in Probabilistic Default Reasoning

2011

We study the quasi conjunction and the Goodman & Nguyen inclusion relation for conditional events, in the setting of probabilistic default reasoning under coherence. We deepen two recent results given in (Gilio and Sanfilippo, 2010): the first result concerns p-entailment from a family F of conditional events to the quasi conjunction C(S) associated with each nonempty subset S of F; the second result, among other aspects, analyzes the equivalence between p-entailment from F and p-entailment from C(S), where S is some nonempty subset of F. We also characterize p-entailment by some alternative theorems. Finally, we deepen the connections between p-entailment and the Goodman & Nguyen inclusion…

Discrete mathematicsClass (set theory)goodman & nguyen inclusion relationSettore MAT/06 - Probabilita' E Statistica MatematicaSettore INF/01 - Informaticap-entailment.; quasi conjunction; goodman & nguyen inclusion relation; qand rule; coherence; probabilistic default reasoning; p-entailmentProbabilistic logicqand ruleprobabilistic default reasoningConsistency (knowledge bases)Coherence (philosophical gambling strategy)p-entailmentCoherence probabilistic default reasoning quasi conjunction Goodman & Nguyen inclusion relation QAND rule p-entailment.coherenceConjunction (grammar)Default reasoningquasi conjunctionGreatest elementAlgorithmEquivalence (measure theory)Mathematics
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Centering and Compound Conditionals under Coherence

2016

There is wide support in logic , philosophy , and psychology for the hypothesis that the probability of the indicative conditional of natural language, \(P(\textit{if } A \textit{ then } B)\), is the conditional probability of B given A, P(B|A). We identify a conditional which is such that \(P(\textit{if } A \textit{ then } B)= P(B|A)\) with de Finetti’s conditional event, B|A. An objection to making this identification in the past was that it appeared unclear how to form compounds and iterations of conditional events. In this paper, we illustrate how to overcome this objection with a probabilistic analysis, based on coherence, of these compounds and iterations. We interpret the compounds a…

Discrete mathematicsIndicative conditionalcenteringSettore MAT/06 - Probabilita' E Statistica Matematica05 social sciencesClassical logicConditional probabilityInference02 engineering and technologyCoherence (philosophical gambling strategy)p-entailmentn-conditional event050105 experimental psychologycoherenceLogical biconditionalp-validity0202 electrical engineering electronic engineering information engineeringbiconditional event020201 artificial intelligence & image processing0501 psychology and cognitive sciencesProbabilistic analysis of algorithmsArithmeticMathematicsEvent (probability theory)Conditional
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Conditional Random Quantities and Compounds of Conditionals

2013

In this paper we consider finite conditional random quantities and conditional previsions assessments in the setting of coherence. We use a suitable representation for conditional random quantities; in particular the indicator of a conditional event $E|H$ is looked at as a three-valued quantity with values 1, or 0, or $p$, where $p$ is the probability of $E|H$. We introduce a notion of iterated conditional random quantity of the form $(X|H)|K$ defined as a suitable conditional random quantity, which coincides with $X|HK$ when $H \subseteq K$. Based on a recent paper by S. Kaufmann, we introduce a notion of conjunction of two conditional events and then we analyze it in the setting of cohere…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaLogicImport–Export principleProbability (math.PR)Probabilistic logicConjunctionOf the formSettore M-FIL/02 - Logica E Filosofia Della ScienzaCoherence (philosophical gambling strategy)Conditional random quantitieConjunction (grammar)Lower/upper prevision boundsHistory and Philosophy of ScienceNegationIterated functionIterated conditioningFOS: MathematicsConditional eventRepresentation (mathematics)CoherenceDisjunctionMathematics - ProbabilityMathematicsEvent (probability theory)
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Probabilistic Logic under Coherence, Model-Theoretic Probabilistic Logic, and Default Reasoning

2001

We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail, we explore the relationship between coherence-based and model-theoretic probabilistic logic. Interestingly, we show that the notions of g-coherence and of g-coherent entailment can be expressed by combining notions in model-theoretic probabilistic logic with concepts from default reasoning. Crucially, we even show that probabilistic reasoning under coherence is a probabilistic generalization of default reasoning in system P. That is, we provide a new probabilistic semantics for system P, which is neither based on infinitesimal probabilities nor on atomic-bound (or also big-stepped) probabil…

Deductive reasoningSettore MAT/06 - Probabilita' E Statistica MatematicaKnowledge representation and reasoningComputer scienceDefault logicDivergence-from-randomness modelLogic modelcomputer.software_genreLogical consequenceProbabilistic logic networkConditional probability assessments conditional constraints probabilistic logic under coherence model-theoretic probabilistic logic g-coherence g-coherent entailment default reasoning from conditional knowledge bases System P conditional objectsprobabilistic logic under coherenceNon-monotonic logicProbabilistic relevance modeldefault reasoningmodel-theoretic probabilistic logicbusiness.industryProbabilistic logicProbabilistic argumentationExpert systemg-coherencesystem pProbabilistic CTLArtificial intelligencebusinesscomputerdefault reasoning; g-coherence; model-theoretic probabilistic logic; probabilistic logic under coherence; system p
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On Trivalent Logics, Compound Conditionals, and Probabilistic Deduction Theorems

2023

In this paper we recall some results for conditional events, compound conditionals, conditional random quantities, p-consistency, and p-entailment. Then, we show the equivalence between bets on conditionals and conditional bets, by reviewing de Finetti's trivalent analysis of conditionals. But our approach goes beyond de Finetti's early trivalent logical analysis and is based on his later ideas, aiming to take his proposals to a higher level. We examine two recent articles that explore trivalent logics for conditionals and their definitions of logical validity and compare them with our approach to compound conditionals. We prove a Probabilistic Deduction Theorem for conditional events. Afte…

FOS: Computer and information sciencesArtificial Intelligence (cs.AI)Computer Science - Artificial Intelligence
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Compound conditionals as random quantities and Boolean algebras

2022

Conditionals play a key role in different areas of logic and probabilistic reasoning, and they have been studied and formalised from different angles. In this paper we focus on the de Finetti's notion of conditional as a three-valued object, with betting-based semantics, and its related approach as random quantity as mainly developed by two of the authors. Compound conditionals have been studied in the literature, but not in full generality. In this paper we provide a natural procedure to explicitly attach conditional random quantities to arbitrary compound conditionals that also allows us to compute their previsions. By studying the properties of these random quantities, we show that, in f…

03B48Settore MAT/06 - Probabilita' E Statistica MatematicaFOS: MathematicsMathematics - LogicLogic (math.LO)Compound conditionals Conditional Boolean algebra conjunction and disjunction canonical extension
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A Generalized Probabilistic Version of Modus Ponens

2017

Modus ponens (\emph{from $A$ and "if $A$ then $C$" infer $C$}, short: MP) is one of the most basic inference rules. The probabilistic MP allows for managing uncertainty by transmitting assigned uncertainties from the premises to the conclusion (i.e., from $P(A)$ and $P(C|A)$ infer $P(C)$). In this paper, we generalize the probabilistic MP by replacing $A$ by the conditional event $A|H$. The resulting inference rule involves iterated conditionals (formalized by conditional random quantities) and propagates previsions from the premises to the conclusion. Interestingly, the propagation rules for the lower and the upper bounds on the conclusion of the generalized probabilistic MP coincide with …

Probability (math.PR)FOS: MathematicsMathematics - Probability
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Probabilistic entailment and iterated conditionals

2018

In this paper we exploit the notions of conjoined and iterated conditionals, which are defined in the setting of coherence by means of suitable conditional random quantities with values in the interval $[0,1]$. We examine the iterated conditional $(B|K)|(A|H)$, by showing that $A|H$ p-entails $B|K$ if and only if $(B|K)|(A|H) = 1$. Then, we show that a p-consistent family $\mathcal{F}=\{E_1|H_1,E_2|H_2\}$ p-entails a conditional event $E_3|H_3$ if and only if $E_3|H_3=1$, or $(E_3|H_3)|QC(\mathcal{S})=1$ for some nonempty subset $\mathcal{S}$ of $\mathcal{F}$, where $QC(\mathcal{S})$ is the quasi conjunction of the conditional events in $\mathcal{S}$. Then, we examine the inference rules $A…

Probability (math.PR)FOS: MathematicsMathematics - LogicLogic (math.LO)Mathematics - Probability
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Canonical Extensions of Conditional Probabilities and Compound Conditionals

2022

In this paper we show that the probability of conjunctions and disjunctions of conditionals in a recently introduced framework of Boolean algebras of conditionals are in full agreement with the corresponding operations of conditionals as defined in the approach developed by two of the authors to conditionals as three-valued objects, with betting-based semantics, and specified as suitable random quantities. We do this by first proving that the canonical extension of a full conditional probability on a finite algebra of events to the corresponding algebra of conditionals is compatible with taking subalgebras of events.

Settore MAT/06 - Probabilita' E Statistica MatematicaBoolean algebras of conditionals Conditional probability Conjunction and disjunction of conditionals
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Transitive reasoning with imprecise probabilities

2015

We study probabilistically informative (weak) versions of transitivity, by using suitable definitions of defaults and negated defaults, in the setting of coherence and imprecise probabilities. We represent p-consistent sequences of defaults and/or negated defaults by g-coherent imprecise probability assessments on the respective sequences of conditional events. Finally, we prove the coherent probability propagation rules for Weak Transitivity and the validity of selected inference patterns by proving the p-entailment for the associated knowledge bases.

FOS: Computer and information sciencesComputer Science - Logic in Computer ScienceArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceProbability (math.PR)FOS: MathematicsComputer Science::Artificial IntelligenceMathematics - ProbabilityLogic in Computer Science (cs.LO)
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Conjunction of Conditional Events and T-norms

2019

We study the relationship between a notion of conjunction among conditional events, introduced in recent papers, and the notion of Frank t-norm. By examining different cases, in the setting of coherence, we show each time that the conjunction coincides with a suitable Frank t-norm. In particular, the conjunction may coincide with the Product t-norm, the Minimum t-norm, and Lukasiewicz t-norm. We show by a counterexample, that the prevision assessments obtained by Lukasiewicz t-norm may be not coherent. Then, we give some conditions of coherence when using Lukasiewicz t-norm.

Mathematics::LogicProbability (math.PR)FOS: MathematicsMathematics - Probability
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Computational aspects in checking of coherence and propagation of conditional probability bounds

2000

In this paper we consider the problem of reducing the computational difficulties in g-coherence checking and propagation of imprecise conditional probability assessments. We review some theoretical results related with the linear structure of the random gain in the betting criterion. Then, we propose a modi ed version of two existing algorithms, used for g-coherence checking and propagation, which are based on linear systems with a reduced number of unknowns. The reduction in the number of unknowns is obtained by an iterative algorithm. Finally, to illustrate our procedure we give some applications.

reduced sets of variables and constrainsCoherent probability assessments propagation random gain computation algorithmsSettore MAT/06 - Probabilita' E Statistica MatematicaChecking of coherencerandom gainpropagationChecking of coherence; computational aspects; propagation; linear systems; random gain; reduced sets of variables and constrainslinear systemscomputational aspects
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Efficient checking of coherence and propagation of imprecise probability assessments

2000

We consider the computational difficulties in the checking of coherence and propagation of imprecise probability assessments. We examine the linear structure of the random gain in betting criterion and we propose a general methodology which exploits suitable subsets of the set of values of the random gain. In this way the checking of coherence and propagation amount to examining linear systems with a reduced number of unknowns. We also illustrate an example.

computationCoherent probability assessments propagation random gain computation algorithmsSettore MAT/06 - Probabilita' E Statistica MatematicaCoherent probability assessmentsrandom gainCoherent probability assessments; propagation; random gain; computation; algorithms.Coherent probability assessments; Propagation; AlgorithmsPropagationAlgorithms
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On general conditional prevision assessments

2009

In this paper we consider general conditional random quantities of the kind $X|Y$, where $X$ and $Y$ are finite discrete random quantities. Then, we introduce the notion of coherence for conditional prevision assessments on finite families of general conditional random quantities. Moreover, we give a compound prevision theorem and we examine the relation between the previsions of $X|Y$ and $Y|X$. Then, we give some results on random gains and, by a suitable alternative theorem, we obtain a characterization of coherence. We also propose an algorithm for the checking of coherence. Finally, we briefly examine the case of imprecise conditional prevision assessments by introducing the notions of…

Conditional random quantities; coherence; conditional prevision assessments; random gain; alternative theorems; algorithms; imprecise assessments; generalized and total coherence.Settore MAT/06 - Probabilita' E Statistica Matematicarandom gainConditional events general conditional random quantitiesgeneral conditional prevision assessments generalized compound prevision theorem generalized Bayes TheoremConditional random quantitiesalgorithmsimprecise assessmentsalternative theoremsgeneralized and total coherencecoherenceconditional prevision assessments
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