An implementation of statistical default logic

In Jose Alferes & Joao Leite (eds.), Logics in Artificial Intelligence (JELIA 2004). Springer (2004)
Statistical Default Logic (SDL) is an expansion of classical (i.e., Reiter) default logic that allows us to model common inference patterns found in standard inferential statistics, e.g., hypothesis testing and the estimation of a population‘s mean, variance and proportions. This paper presents an embedding of an important subset of SDL theories, called literal statistical default theories, into stable model semantics. The embedding is designed to compute the signature set of literals that uniquely distinguishes each extension on a statistical default theory at a pre-assigned error-bound probability.
Keywords Statistical Default Logic  Literal statistical default theories  Non-monotonic reasoning
Categories (categorize this paper)
 Save to my reading list
Follow the author(s)
My bibliography
Export citation
Find it on Scholar
Edit this record
Mark as duplicate
Revision history Request removal from index Translate to english
Download options
PhilPapers Archive

Upload a copy of this paper     Check publisher's policy on self-archival     Papers currently archived: 24,488
External links
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library
References found in this work BETA

No references found.

Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles
Zhou Beihai & Mao Yi (2006). A Base Logic for Default Reasoning. Frontiers of Philosophy in China 1 (4):688-709.

Monthly downloads

Added to index


Total downloads

37 ( #130,274 of 1,925,795 )

Recent downloads (6 months)

6 ( #140,562 of 1,925,795 )

How can I increase my downloads?

My notes
Sign in to use this feature

Start a new thread
There  are no threads in this forum
Nothing in this forum yet.