Demo Data from EDBT 2026 / reviewers in the wild / expert
Sean Zammit
dblp:355/6469
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Theory of computation · 1 · 1 first-author · 1 since 2021
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 |
Planning, search and constraint satisfaction · 33% Knowledge representation and reasoning · 33% Reinforcement learning · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › representation language › knowledge representation formalisms › knowledge representation language
game description language |
0.7 | 1 | 2023 | General Game Playing With State-Independent Communication · KR 2023 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › game playing
general game playing |
0.7 | 1 | 2023 | General Game Playing With State-Independent Communication · KR 2023 |
Machine learning › Reinforcement learning › multi-agent reinforcement learning
multi-agent communication |
0.7 | 1 | 2023 | General Game Playing With State-Independent Communication · KR 2023 |
Methods — techniques the papers use, named apart from their topics
strategy evaluation · 0.7game description language · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | General Game Playing With State-Independent CommunicationabstractCommunication actions in games are usually given meaning by either the effect they have on the game state or a possible reduction in the size of the information set of an agent. However, this precludes analysis of games involving state-independent communication, in which players are given the ability to communicate with each other but are not required to be truthful. As such, these communication actions cannot be used to reduce the size of the information set or update beliefs in states without considering the intent of the communicating agent. In this paper, we introduce a language to describe the rules of such games as an extension of the Game Description Language (GDL). We also identify a set of scenarios involving state-independent communication actions in which an effective agent should be able to derive information, and propose and evaluate strategies for reasoning about such actions in these scenarios. Sean Zammit, Michael Thielscher |
KR | 1 |