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=== Answer set programs ===
The [[stable model semantics]] underlying [[answer set programming]] gives meaning to unstratified programs by allocating potentially more than one answer set to every truth value assignment of the probabilistic facts. This raises the question of how to distribute the probability mass across the answer sets.<ref name=":1">{{Citation |last=Riguzzi |first=Fabrizio |title=Probabilistic Answer Set Programming |date=2023-05-22 |work=Foundations of Probabilistic Logic Programming |pages=165–173 |url=http://dx.doi.org/10.1201/9781003427421-6 |access-date=2024-02-03 |place=New York |publisher=River Publishers |doi=10.1201/9781003427421-6 |isbn=978-1-003-42742-1|url-access=subscription }}</ref><ref name=":2">{{Cite journal |last1=Cozman |first1=Fabio Gagliardi |last2=Mauá |first2=Denis Deratani |date=2020 |title=The joy of Probabilistic Answer Set Programming: Semantics, complexity, expressivity, inference |url=https://linkinghub.elsevier.com/retrieve/pii/S0888613X20302012 |journal=International Journal of Approximate Reasoning |language=en |volume=125 |pages=218–239 |doi=10.1016/j.ijar.2020.07.004|s2cid=222233309 |doi-access=free |url-access=subscription }}</ref>
The probabilistic logic programming language P-Log resolves this by dividing the probability mass equally between the answer sets, following the [[principle of indifference]].<ref name=":1" /><ref>{{Cite journal |last1=Baral |first1=Chitta |last2=Gelfond |first2=Michael |last3=Rushton |first3=Nelson |date=2009 |title=Probabilistic reasoning with answer sets |url=https://www.cambridge.org/core/product/identifier/S1471068408003645/type/journal_article |journal=Theory and Practice of Logic Programming |language=en |volume=9 |issue=1 |pages=57–144 |doi=10.1017/S1471068408003645 |issn=1471-0684|url-access=subscription }}</ref>
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