Research map / Learning theory and algorithms / Prediction and decision losses
A Comparative Study of Ranking-Based Semantics for Abstract Argumentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use path-count lexicographic ranking rather than attacker-strength aggregation | Dbs · Running example argumentation framework | relative ranking of c and e | Mixed |
| Use burden numbers to compare arguments lexicographically | Bbs · Argumentation frameworks with distributed defenses | Distributed-Defense Precedence (DDP) | Helped |
| Use tupled ancestor-branch values | Tuples* · Acyclic argumentation frameworks | Totality (Tot) | Hurt |
| Compare whole attack and defense branches | Tuples* and M&T · Argumentation frameworks satisfying Attack vs Full Defense | Attack vs Full Defense (AvsFD) | Helped |
| Treat attackers' strength as more important than their number | Categoriser and SAF · Running example argumentation framework | relative ranking of e and c | Helped |
| Make self-attack intrinsically lower-ranked | M&T · Abstract argumentation frameworks | Self-Contradiction (SC) | Helped |
| Require Void Precedence while requiring every added defense branch to improve ranking | Ranking-based semantics · Abstract argumentation frameworks | compatibility of VP and strict addition of defense branch | Hurt |
| Require Cardinality Precedence together with Quality Precedence | Ranking-based semantics · Abstract argumentation frameworks | compatibility of CP and QP | Hurt |
| Use the same ranking axioms for Categoriser and SAF | Categoriser and SAF · Abstract argumentation frameworks | axiom-based discrimination | No clear effect |
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