Synthesizing Argumentation Frameworks from Examples

Argumentation is nowadays a core topic in AI research. Understanding computational and representational aspects of abstract argumentation frameworks (AFs) is a central topic in the study of argumentation. The study of realizability of AFs aims at understanding the expressive power of AFs under different semantics. We propose and study the AF synthesis problem as a natural extension of realizability, addressing some of the shortcomings arising from the relatively stringent definition of realizability. Specifically, AF synthesis seeks to construct, or synthesize, AFs that are semantically closest to the knowledge at hand even when no AFs exactly representing the knowledge exist. Going beyond defining the AF synthesis problem, we (i) prove NP-completeness of AF synthesis under several semantics, (ii) study basic properties of the problem in relation to realizability, (iii) develop algorithmic solutions to AF synthesis using constrained optimization, (iv) empirically evaluate our algorithms on different forms of AF synthesis instances, as well as (v) discuss variants and generalization of AF synthesis.