The MatrixX Solver For Argumentation Frameworks
Maximilian Heinrich
TL;DR
MatrixX is a specialized solver for Abstract Argumentation Frameworks that uses matrix notation and hash maps to efficiently compute stable and complete semantics, optimized for competitive evaluation.
Contribution
It introduces a matrix-based representation and reduction method for argumentation frameworks, enhancing computational efficiency with hash maps.
Findings
Successfully applied to ICCMA 2021 competition
Achieves faster computation times for stable and complete semantics
Utilizes matrix notation for systematic reduction
Abstract
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.
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Logic, Reasoning, and Knowledge · Business Process Modeling and Analysis