Non-monotonic Negation in Probabilistic Deductive Databases

03/20/2013
by   Raymond T. Ng, et al.
0

In this paper we study the uses and the semantics of non-monotonic negation in probabilistic deductive data bases. Based on the stable semantics for classical logic programming, we introduce the notion of stable formula, functions. We show that stable formula, functions are minimal fixpoints of operators associated with probabilistic deductive databases with negation. Furthermore, since a. probabilistic deductive database may not necessarily have a stable formula function, we provide a stable class semantics for such databases. Finally, we demonstrate that the proposed semantics can handle default reasoning naturally in the context of probabilistic deduction.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/03/2020

A lemma on closures and its application to modularity in logic programming semantics

This note points out a lemma on closures of monotonic increasing functio...
research
05/13/2003

Computing only minimal answers in disjunctive deductive databases

A method is presented for computing minimal answers in disjunctive deduc...
research
04/11/2023

Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue

The role of uncertainty in data management has become more prominent tha...
research
02/27/2019

On Constrained Open-World Probabilistic Databases

Increasing amounts of available data have led to a heightened need for r...
research
07/21/2023

Eliminating Unintended Stable Fixpoints for Hybrid Reasoning Systems

A wide variety of nonmonotonic semantics can be expressed as approximato...
research
10/19/2012

Upgrading Ambiguous Signs in QPNs

WA qualitative probabilistic network models the probabilistic relationsh...
research
11/27/2017

Measurable Cones and Stable, Measurable Functions

We define a notion of stable and measurable map between cones endowed wi...

Please sign up or login with your details

Forgot password? Click here to reset