Satisfiability and Model Counting in Open Universes

Research output: Chapter in Book/Report/Conference proceedingConference contribution


SAT and #SAT are at the heart of many important problem formulations in AI, the most prominent being reasoning and learning in first-order and probabilistic knowledge bases. In practice, all contemporary systems resort to domain closure: objects in the universe are all and only the ones mentioned in the knowledge base. This is in stark contrast to the natural ability of human beings to infer things about sensory inputs and unforeseen data: they infer the existence of objects from their observations; no predefined list of objects is given or known in advance. In this paper, we introduce the formal foundations for reasoning in open universes in a general way, purely based on SAT and #SAT technology.
Original languageEnglish
Title of host publicationThe Workshops of the Thirtieth AAAI Conference on Artificial Intelligence Beyond NP: Technical Report WS-16-05
PublisherAAAI Press
Publication statusPublished - Feb 2016
EventThirtieth AAAI Conference on Artificial Intelligence Beyond NP - Phoenix, United States
Duration: 12 Feb 201613 Feb 2016


ConferenceThirtieth AAAI Conference on Artificial Intelligence Beyond NP
Country/TerritoryUnited States
Internet address

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