A Polynomial-time Fragment of Epistemic Probabilistic Argumentation (Technical Report)
This work addresses computational bottlenecks in AI reasoning systems, offering a polynomial-time solution for a specific fragment, which is incremental but impactful for practical applications.
The paper tackles the exponential computational complexity in probabilistic argumentation by identifying a fragment where problems become tractable, showing that some previously intractable problems can be solved in polynomial time with efficient convex programming formulations.
Probabilistic argumentation allows reasoning about argumentation problems in a way that is well-founded by probability theory. However, in practice, this approach can be severely limited by the fact that probabilities are defined by adding an exponential number of terms. We show that this exponential blowup can be avoided in an interesting fragment of epistemic probabilistic argumentation and that some computational problems that have been considered intractable can be solved in polynomial time. We give efficient convex programming formulations for these problems and explore how far our fragment can be extended without loosing tractability.