Complexity results for probabilistic answer set programming

Denis Deratani Mauá, Fabio Gagliardi Cozman

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We analyze the computational complexity of probabilistic logic programming with constraints, disjunctive heads, and aggregates such as sum and max. We consider propositional programs and relational programs with bounded-arity predicates, and look at cautious reasoning (i.e., computing the smallest probability of an atom over all probability models), cautious explanation (i.e., finding an interpretation that maximizes the lower probability of evidence) and cautious maximum-a-posteriori (i.e., finding a partial interpretation for a set of atoms that maximizes their lower probability conditional on evidence) under Lukasiewicz's credal semantics.

Original languageAmerican English
Pages (from-to)133-154
Number of pages22
JournalInternational Journal of Approximate Reasoning
Volume118
DOIs
StatePublished - 1 Mar 2020
Externally publishedYes

Keywords

  • Answer set programming
  • Computational complexity
  • Probabilistic logic programming

Fingerprint

Dive into the research topics of 'Complexity results for probabilistic answer set programming'. Together they form a unique fingerprint.

Cite this