Demo Data from EDBT 2026 / reviewers in the wild / expert
Shengying Pan
dblp:97/9514
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2016
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Multi-agent systems · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems › task allocation
dynamic task allocation |
0.2 | 1 | 2016 | Dynamic Task Allocation Algorithm for Hiring Workers that Learn · IJCAI 2016 |
Knowledge, reasoning and agents › Multi-agent systems
task allocation |
0.2 | 1 | 2016 | Dynamic Task Allocation Algorithm for Hiring Workers that Learn · IJCAI 2016 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Dynamic Task Allocation Algorithm for Hiring Workers that Learn
Shengying Pan, Kate Larson, Josh Bradshaw, Edith Law |
IJCAI | 1 |
| 2010 | Argumentation Mechanism Design for Preferred SemanticsabstractRecently Argumentation Mechanism Design (ArgMD) was introduced as a paradigm for studying argumentation using game-theoretic techniques. To date, this framework has been used to study under what conditions a direct mechanism based on Dung's grounded semantics is strategy-proof (i.e. truth-enforcing) when knowledge of arguments is private to self-interested agents. In this paper, we study Dung's preferred semantics in order to understand under what conditions it is possible to design strategy-proof mechanisms. This is challenging since, unlike with the grounded semantics, there may be multiple preferred extensions, forcing a mechanism to select one. We show that this gives rise to interesting strategic behaviour, and we show that in general it is not possible to have a strategy-proof mechanism that selects amongst the preferred extensions in a non-biased manner. We also investigaet refinements of preferred semantics which induce unique outcomes, namely the skeptical-preferred and ideal semantics. Shengying Pan, Kate Larson, Iyad Rahwan |
COMMA | 1 |