Building precompiled knowledge in ODeLP

Abstract

Argumentation systems have substantially evolved in the past few years, resulting in adequate tools to model some forms of common sense reasoning. This has sprung a new set of argument-based applications in diverse areas. In previous work, we defined how to use precompiled knowledge to obtain significant speed-ups in the inference process of an argument-based system. This development is based on a logic programming system with an argumentation-driven inference engine, called Observation Based Defeasible Logic Programming (ODeLP). In this setting was first presented the concept of dialectical databases, that is, data structures for storing precompiled knowledge. These structures provide precompiled information about inferences and can be used to speed up the inference process, as TMS do in general problem solvers. In this work, we present detailed algorithms for the creation of dialectical databases in ODeLP and analyze these algorithms in terms of their computational complexity.