English

Possibilistic decreasing persistence

Artificial Intelligence 2013-03-08 v1

Abstract

A key issue in the handling of temporal data is the treatment of persistence; in most approaches it consists in inferring defeasible confusions by extrapolating from the actual knowledge of the history of the world; we propose here a gradual modelling of persistence, following the idea that persistence is decreasing (the further we are from the last time point where a fluent is known to be true, the less certainly true the fluent is); it is based on possibility theory, which has strong relations with other well-known ordering-based approaches to nonmonotonic reasoning. We compare our approach with Dean and Kanazawa's probabilistic projection. We give a formal modelling of the decreasing persistence problem. Lastly, we show how to infer nonmonotonic conclusions using the principle of decreasing persistence.

Keywords

Cite

@article{arxiv.1303.1510,
  title  = {Possibilistic decreasing persistence},
  author = {Dimiter Driankov and Jerome Lang},
  journal= {arXiv preprint arXiv:1303.1510},
  year   = {2013}
}

Comments

Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)

R2 v1 2026-06-21T23:37:51.437Z