Advanced search
Start date
Betweenand


The Joy of Probabilistic Answer Set Programming

Full text
Author(s):
Cozman, Fabio Gagliardi ; DeBock, J ; DeCampos, CP ; DeCooman, G ; Quaeghebeur, E ; Wheeler, G
Total Authors: 6
Document type: Journal article
Source: INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162; v. 103, p. 11-pg., 2019-01-01.
Abstract

Probabilistic answer set programming (PASP) combines rules, facts, and independent probabilistic facts. Often one restricts such programs so that every query yields a sharp probability value. The purpose of this paper is to argue that a very useful modeling language is obtained by adopting a particular credal semantics for PASP, where one associates with each consistent program a credal set. We examine the basic properties of PASP and present an algorithm to compute (upper) probabilities given a program. (AU)

FAPESP's process: 16/18841-0 - Inference and learning algorithms for probabilistic logic programming
Grantee:Fabio Gagliardi Cozman
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE