Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation
This work addresses a computational bottleneck in argumentation theory for researchers and practitioners, but it is incremental as it extends existing tools from flat to non-flat frameworks.
The paper tackles the problem of reasoning in non-flat assumption-based argumentation (ABA), which is more general than the commonly studied flat case, by developing an instantiation-based approach that translates ABA to bipolar argumentation frameworks (BAFs) and identifies redundant arguments to reduce computational cost. It shows that the BAF-based approach outperforms direct methods on many instances, contrasting with flat ABA where direct methods dominate.
Most existing computational tools for assumption-based argumentation (ABA) focus on so-called flat frameworks, disregarding the more general case. In this paper, we study an instantiation-based approach for reasoning in possibly non-flat ABA. We make use of a semantics-preserving translation between ABA and bipolar argumentation frameworks (BAFs). By utilizing compilability theory, we establish that the constructed BAFs will in general be of exponential size. In order to keep the number of arguments and computational cost low, we present three ways of identifying redundant arguments. Moreover, we identify fragments of ABA which admit a poly-sized instantiation. We propose two algorithmic approaches for reasoning in possibly non-flat ABA. The first approach utilizes the BAF instantiation while the second works directly without constructing arguments. An empirical evaluation shows that the former outperforms the latter on many instances, reflecting the lower complexity of BAF reasoning. This result is in contrast to flat ABA, where direct approaches dominate instantiation-based approaches.