Probabilistic entailment in the setting of coherence: The role of quasi conjunction and inclusion relation

TL;DR

This paper explores probabilistic entailment in coherence-based default reasoning, focusing on quasi conjunction and inclusion relations for conditional events, providing new theoretical insights and algorithms for determining entailment and consistency.

Contribution

It introduces novel results on p-entailment, quasi conjunction, and inclusion relations, along with algorithms to determine entailment and consistency in probabilistic default reasoning.

Findings

p-entailment of a family of conditional events implies the entailment of their quasi conjunction.

The class of subsets implying an event has properties like additivity and a greatest element.

Algorithms can determine the greatest subset implying a conditional event.

Abstract

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. We also illustrate some alternative theorems related with p-consistency and p-entailment. Finally, we deepen the study of the connections between the notions of p-entailment and inclusion relation by introducing for a pair (F,E|H) the (possibly empty) class K of the subsets S of F such that C(S) implies E|H. We show that the class K satisfies many properties; in particular K is additive and has a greatest element which can be determined by applying a suitable algorithm.