Explaining Tournament Solutions with Minimal Supports
This addresses the need for explainable AI in tournament-based decision-making, offering compact and intuitive explanations, though it is incremental as it builds on existing tournament solution concepts.
The paper tackles the problem of providing certified explanations for why a candidate wins under various tournament rules by identifying minimal supports, which are minimal sub-tournaments where the candidate is a necessary winner. It determines the size of the smallest minimal supports for common rules, presents polynomial-time algorithms for most, and shows NP-completeness for the weighted uncovered set.
Tournaments are widely used models to represent pairwise dominance between candidates, alternatives, or teams. We study the problem of providing certified explanations for why a candidate appears among the winners under various tournament rules. To this end, we identify minimal supports, minimal sub-tournaments in which the candidate is guaranteed to win regardless of how the rest of the tournament is completed (that is, the candidate is a necessary winner of the sub-tournament). This notion corresponds to an abductive explanation for the question,"Why does the winner win the tournament", a central concept in formal explainable AI. We focus on common tournament solutions: the top cycle, the uncovered set, the Copeland rule, the Borda rule, the maximin rule, and the weighted uncovered set. For each rule we determine the size of the smallest minimal supports, and we present polynomial-time algorithms to compute them for all but the weighted uncovered set, for which the problem is NP-complete. Finally, we show how minimal supports can serve to produce compact, certified, and intuitive explanations.