Chronological ignorance: experiments in nonmonotonic temporal reasoning
Artificial Intelligence
A model for reasoning about persistence and causation
Computational Intelligence
On the semantics of fuzzy logic
International Journal of Approximate Reasoning
Fundamenta Informaticae - Special issue: logics for artificial intelligence
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Possibilistic logic, preferential models, non-monotonicity and related issues
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
A logic and time nets for probabilistic inference
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
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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.