Resumen

HARRISON, Michael  y  MEYER, Thomas. DDLV: A system for rational preferential reasoning for Datalog. SACJ [online]. 2020, vol.32, n.2, pp.184-217. ISSN 2313-7835.  https://doi.org/10.18489/sacj.v32i2.850.

Datalog is a powerful language that can be used to represent explicit knowledge and compute inferences in knowledge bases. Datalog cannot, however, represent or reason about contradictory rules. This is a limitation as contradictions are often present in domains that contain exceptions. In this paper, we extend Datalog to represent contradictory and defeasible information. We define an approach to efficiently reason about contradictory information in Datalog and show that it satisfies the KLM requirements for a rational consequence relation. We introduce DDLV, a defeasible Datalog reasoning system that implements this approach. Finally, we evaluate the performance of DDLV.CATEGORIES: • Computing methodologies ~ Artificial intelligence • Theory of computation ~ Logic

Palabras clave : datalog; non-monotonic reasoning; preferential reasoning; knowledge representation.

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