Possibilistic decreasing persistence
This work addresses temporal reasoning for AI systems, but it appears incremental as it builds on existing nonmonotonic reasoning frameworks.
The paper tackles the problem of modeling persistence in temporal data by proposing a gradual approach where certainty decreases over time, based on possibility theory, and compares it with probabilistic projection.
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.