Byung Kon Kang

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EDBT 2026 Demo / reviewers in the wild / expert

dblp:67/11323 · DBLP profile ↗

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1ranked-venue papers

1as first author

0since 2021 · last 2012

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 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

Planning, search and constraint satisfaction · 56% Reinforcement learning · 44%

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 › Planning, search and constraint satisfaction › decision making under uncertainty partially observable stochastic domains	0.1	1	2012	Exploiting symmetries for single- and multi-agent Partially Observable Stochastic Domains · Artif. Intell. 2012
Machine learning › Reinforcement learning › function approximation › representation learning for reinforcement learning symmetry exploitation	0.1	1	2012	Exploiting symmetries for single- and multi-agent Partially Observable Stochastic Domains · Artif. Intell. 2012
Knowledge, reasoning and agents › Planning, search and constraint satisfaction multi-agent planning	0.0	1	2012	Exploiting symmetries for single- and multi-agent Partially Observable Stochastic Domains · Artif. Intell. 2012

Methods — techniques the papers use, named apart from their topics

symmetry reduction · 0.1

Year	Publication	Venue	Position
2012	Exploiting symmetries for single- and multi-agent Partially Observable Stochastic Domains Byung Kon Kang, Kee-Eung Kim	Artif. Intell.	1