The MatrixX Solver For Argumentation Frameworks
This work addresses the need for faster solvers in computational argumentation, but it appears incremental as it builds on existing matrix-based methods with implementation optimizations.
The paper introduces MatrixX, a solver for Abstract Argumentation Frameworks that tackles the problem of efficiently computing stable and complete semantics by using matrix notation and hash maps for acceleration, achieving unspecified performance gains as designed for the ICCMA 2021 competition.
MatrixX is a solver for Abstract Argumentation Frameworks. Offensive and defensive properties of an Argumentation Framework are notated in a matrix style. Rows and columns of this matrix are systematically reduced by the solver. This procedure is implemented through the use of hash maps in order to accelerate calculation time. MatrixX works for stable and complete semantics and was designed for the ICCMA 2021 competition.