Semantics for possibilistic answer set programs: Uncertain rules versus rules with uncertain conclusions
| Autor: | Kim Bauters, Martine De Cock, Dirk Vermeir, Steven Schockaert |
|---|---|
| Přispěvatelé: | Theoretical Computer Science, Logic Engineering, Informatics and Applied Informatics |
| Rok vydání: | 2014 |
| Předmět: |
Semantics (computer science)
media_common.quotation_subject Logic programming LOGIC PROGRAMS Theoretical Computer Science Answer set programming Artificial Intelligence SEARCH KNOWLEDGE Set (psychology) Mathematics Possibility theory media_common business.industry Applied Mathematics Uncertainty CONSTRAINTS Extension (predicate logic) Certainty Science General MODEL Non-monotonic reasoning semantic Stable model semantics Artificial intelligence business Mathematical economics Software |
| Zdroj: | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING |
| ISSN: | 0888-613X |
| DOI: | 10.1016/j.ijar.2013.09.006 |
| Popis: | Although Answer Set Programming (ASP) is a powerful framework for declarative problem solving, it cannot in an intuitive way handle situations in which some rules are uncertain, or in which it is more important to satisfy some constraints than others. Possibilistic ASP (PASP) is a natural extension of ASP in which certainty weights are associated with each rule. In this paper we contrast two different views on interpreting the weights attached to rules. Under the first view, weights reflect the certainty with which we can conclude the head of a rule when its body is satisfied. Under the second view, weights reflect the certainty that a given rule restricts the considered epistemic states of an agent in a valid way, i.e. it is the certainty that the rule itself is correct. The first view gives rise to a set of weighted answer sets, whereas the second view gives rise to a weighted set of classical answer sets. |
| Databáze: | OpenAIRE |
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