A Probabilistic Abduction Engine for Media Interpretation Based on Ontologies

摘要

We propose an abduction-based formalism that uses description logics for the ontology and Horn rules for defining the space of hypotheses for explanations, and we use Markov logic to define the motivation for the agent to generate explanations on the one hand, and for ranking different explanations on the other. The formalism is applied to media interpretation problems in a agent-oriented scenario.1