Chris van Merwijk

dblp:242/7978 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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
Probabilistic and Bayesian machine learning · 61% Planning, search and constraint satisfaction · 30% Trustworthy machine learning · 9%
Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models
0.612022
A Complete Criterion for Value of Information in Soluble Influence Diagrams · AAAI 2022
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › directed graphical model
influence diagrams
0.612022
A Complete Criterion for Value of Information in Soluble Influence Diagrams · AAAI 2022
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › decision making under uncertainty
value of information
0.612022
A Complete Criterion for Value of Information in Soluble Influence Diagrams · AAAI 2022

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

graphical criteria · 0.6ID homomorphism · 0.6
YearPublicationVenuePosition
2025 Incentives for responsiveness, instrumental control and impact
Ryan Carey, Eric D. Langlois 0002, Chris van Merwijk, Shane Legg, Tom Everitt
Artif. Intell.3
2022 A Complete Criterion for Value of Information in Soluble Influence Diagrams
abstract
Influence diagrams have recently been used to analyse the safety and fairness properties of AI systems. A key building block for this analysis is a graphical criterion for value of information (VoI). This paper establishes the first complete graphical criterion for VoI in influence diagrams with multiple decisions. Along the way, we establish two techniques for proving properties of multi-decision influence diagrams: ID homomorphisms are structure-preserving transformations of influence diagrams, while a Tree of Systems is a collection of paths that captures how information and control can flow in an influence diagram.
Chris van Merwijk, Ryan Carey, Tom Everitt
AAAI1