Labeled Bipolar Argumentation Frameworks

MELISA G. ESCAÑUELA GONZALEZ; MAXIMILANO C. D. BUDAN; SIMARI, GERARDO; GUILLERMO R. SIMARI

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, THE

AI ACCESS FOUNDATION

Lugar: El Segundo; Año: 2021

1076-9757

An essential part of argumentation-based reasoning is to identify arguments in favor and against a statement or query, select the acceptable ones, and then determine whether or not the original statement should be accepted. We present here an abstract framework that considers two independent forms of argument interaction?support and conflict?and is able to represent distinctive information associated with these arguments. This information can enable additional actions such as: (i) a more in-depth analysis of the relations between the arguments; (ii) a representation of the user?s posture to help in focusing the argumentative process, optimizing the values of attributes associated with certain arguments; and (iii) an enhancement of the semantics taking advantage of the availability of richer information about argument acceptability. Thus, the classical semantic definitions are enhanced by analyzing a set of postulates they satisfy. Finally, a polynomial-time algorithm to perform the labeling process is introduced, in which the argument interactions are considered.