Scott B. Hunter

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Demo Data from EDBT 2026 / reviewers in the wild / expert

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2ranked-venue papers

1as first author

0since 2021 · last 1994

—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 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 2 papers		Multi-agent systems · 49% Trustworthy machine learning · 37% Reinforcement learning · 15%
Theoretical computer science 1 paper		Algorithms and data structures · 100%

Topics — the 1 heaviest of 4, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Trustworthy machine learning › interpretability explanation-based learning	0.0	1	1992	Measuring Utility and the Design of Provably Good EBL Algorithms · ML 1992

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

simulation · 0.0

Year	Publication	Venue	Position
1994	Preliminary Studies in Agent Design in Simulated Environments Scott B. Hunter	AAAI	1
1992	Measuring Utility and the Design of Provably Good EBL Algorithms Devika Subramanian, Scott B. Hunter	ML	2