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Shengying Pan

dblp:97/9514 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems › task allocation
dynamic task allocation
0.212016
Dynamic Task Allocation Algorithm for Hiring Workers that Learn · IJCAI 2016
Knowledge, reasoning and agents › Multi-agent systems
task allocation
0.212016
Dynamic Task Allocation Algorithm for Hiring Workers that Learn · IJCAI 2016
YearPublicationVenuePosition
2016 Dynamic Task Allocation Algorithm for Hiring Workers that Learn
Shengying Pan, Kate Larson, Josh Bradshaw, Edith Law
IJCAI1
2010 Argumentation Mechanism Design for Preferred Semantics
abstract
Recently 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
COMMA1