A possibilistic handling of partially ordered information
This work addresses a limitation in possibilistic logic for AI researchers dealing with uncertain or prioritized data, but it appears incremental as it extends existing frameworks without introducing a new paradigm.
The paper tackles the problem of handling partially ordered information in possibilistic logic by extending standard methods to accommodate partial orders, showing that basic notions like subsumption and inference have natural counterparts and proposing an algorithm for computing possibilistic conclusions from such knowledge bases.
In a standard possibilistic logic, prioritized information are encoded by means of weighted knowledge base. This paper proposes an extension of possibilistic logic for dealing with partially ordered information. We Show that all basic notions of standard possibilitic logic (sumbsumption, syntactic and semantic inference, etc.) have natural couterparts when dealing with partially ordered information. We also propose an algorithm which computes possibilistic conclusions of a partial knowledge base of a partially ordered knowlege base.