Abstract
A coherence-based probability semantics for categorical syllogisms of Figure I, which have transitive structures, has been proposed recently (Gilio, Pfeifer, & Sanfilippo [15]). We extend this work by studying Figure II under coherence. Camestres is an example of a Figure II syllogism: from Every P is M and No S is M infer No S is P. We interpret these sentences by suitable conditional probability assessments. Since the probabilistic inference of \(\bar{P}|S\) from the premise set \(\{M|P,\bar{M}|S\}\) is not informative, we add \(p(S|(S \vee P))>0\) as a probabilistic constraint (i.e., an “existential import assumption”) to obtain probabilistic informativeness. We show how to propagate the assigned (precise or interval-valued) probabilities to the sequence of conditional events \((M|P,\bar{M}|S, S|(S \vee P))\) to the conclusion \(\bar{P}|S\). Thereby, we give a probabilistic meaning to the other syllogisms of Figure II. Moreover, our semantics also allows for generalizing the traditional syllogisms to new ones involving generalized quantifiers (like Most S are P) and syllogisms in terms of defaults and negated defaults.
N. Pfeifer and G. Sanfilippo—Shared first authorship (both authors contributed equally to this work).
N. Pfeifer—Supported by his DFG grant PF 740/2-2 (within the SPP1516).
G. Sanfilippo—Partially supported by the “National Group for Mathematical Analysis, Probability and their Applications” (GNAMPA – INdAM).
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We thank three anonymous reviewers for their useful comments and suggestions.
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Pfeifer, N., Sanfilippo, G. (2018). Probabilistic Semantics for Categorical Syllogisms of Figure II. In: Ciucci, D., Pasi, G., Vantaggi, B. (eds) Scalable Uncertainty Management. SUM 2018. Lecture Notes in Computer Science(), vol 11142. Springer, Cham. https://doi.org/10.1007/978-3-030-00461-3_14
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