Probabilistic entailment in the setting of coherence: The role of quasi conjunction and inclusion relation
This work provides incremental theoretical insights into coherence-based probabilistic reasoning, primarily relevant for researchers in logic and AI.
The paper tackles the problem of probabilistic entailment in default reasoning by analyzing quasi conjunction and inclusion relations for conditional events, showing that any finite family of conditional events p-entails its quasi conjunction and establishing equivalences for p-entailment with subsets.
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.