Aktuelles
Einladung zum Vortrag von Herrn Dr. Fabrizio Russo (Imperial College London) am 23.09.2026, 10:00 Uhr, im Rahmen des Oberseminars des Lehrstuhls „Künstliche Intelligenz“
[23.09.2026]Im Rahmen des Oberseminars des Lehrstuhls „Künstliche Intelligenz“ (Prof. Dr. Matthias Thimm) hält am Mittwoch, den 23.09.2026, um 10:00 Uhr, Herr Dr. Fabrizio Russo (Imperial College London)
einen Vortrag zum Thema „Optimal Correction Sets for Argumentative Causal Discovery“
Abstract:
Causal Discovery amounts to inferring causal-effect relations from data, representing them as causal graphs. Causal Assumption-based Argumentation (ABA) has been proposed as a causal discovery method with increased guarantees on the correspondence of the discovered causal graphs to a subset of the input constraints that drive the search for the causal relations. Heuristics are currently used to identify the optimal subset of constraints and infer the most likely corresponding graphs. Minimal Unsatisfiable Sets (MUSes) and Minimal Correction Sets (MCSes) have been explored in the Answer Set Programming (ASP) literature to create explanations and repair logic programs (LPs). We leverage the correspondence of stable semantics between ABA and LPs to investigate the benefits and drawbacks of MUSes and MCSes when applied to the causal discovery task carried out by Causal ABA. We define the notion of optimal MCSes and show how they can be computed by leveraging standard optimisation constructs such as weak constraints. We then relate our formulation of optimal MCSes to conflict-based justifications and the notion of provable redress under contestation. An empirical evaluation supports the formal account: our proposed OptABAPC retains more correct facts, reduces ambiguity in the compatible outputs, and improves graph reconstruction over Causal ABA on common benchmarks.
Interessierte sind herzlich eingeladen, im Rahmen eines Zoom-Meetings teilzunehmen:
https://fernuni-hagen.zoom.us/j/62388524887?pwd=WmJRRUpDZWZ4WlJoeFlGQTBESWplQT09
