The phrase "argumentation based" is not correct in standard written English.
It is typically used in contexts discussing methods or approaches that rely on argumentation, but it should be part of a larger phrase, such as "argumentation-based reasoning."
Example: "The research focuses on argumentation-based reasoning to enhance critical thinking skills in students."
Alternatives: "argument-driven" or "argument-focused".
Hence, in this paper we extend Dung's theory to accommodate arguments that claim preferences between other arguments, thus incorporating meta-level argumentation based reasoning about preferences in the object level.
We explore a framework for argumentation (based on classical logic) in which an argument is a pair where the first item in the pair is a minimal consistent set of formulae that proves the second item (which is a formula).
No algorithm can be provided, only argumentation based on the preceding steps.
We present a novel approach to machine learning, called ABML (argumentation based ML).
We estimate the maximum value of parameters a 1– a 3 through empirical argumentation based upon European data.
Humanists regarded it as a practical way to investigate questions on which dialectical argumentation based on logic had proved unable to produce certitude.
For content selection and organization, we devised an argumentation strategy based on guidelines from argumentation theory.
In this context, we propose a general recursive schema for argumentation semantics, based on decomposition along the strongly connected components of the argumentation framework.
The argumentation is based on the assumption that 'knowing about' context and 'knowing about' lexis is contained and maintained within a networked cognitive system.
This argumentation is based on the assumption that the PISA assessments in math, science, and problem solving are able to measure students' competence as outcomes, which are directly linked to their education (Bulle [2011]; Kind [2013]).
The teaching and learning of argumentation are based on premises that are consistent with the wider literature in science education, namely in framing science learning in terms of the appropriation of community practices that provide the structure, motivation and modes of communication required to sustain scientific discourse (Kelly & Chen 1999; Lemke 1990).
