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Sean Zammit

dblp:355/6469 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › representation language › knowledge representation formalisms › knowledge representation language
game description language
0.712023
General Game Playing With State-Independent Communication · KR 2023
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › game playing
general game playing
0.712023
General Game Playing With State-Independent Communication · KR 2023
Machine learning › Reinforcement learning › multi-agent reinforcement learning
multi-agent communication
0.712023
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
YearPublicationVenuePosition
2023 General Game Playing With State-Independent Communication
abstract
Communication 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
KR1