Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Max Kramer 0001

dblp:195/8147 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

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
Trustworthy machine learning · 50% Knowledge representation and reasoning · 50%
Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%

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 › decision theory
decision-theoretic reasoning
0.312017
Moral Decision Making Frameworks for Artificial Intelligence · AAAI 2017
Machine learning › Trustworthy machine learning › ethical AI
moral decision making
0.312017
Moral Decision Making Frameworks for Artificial Intelligence · AAAI 2017
Algorithmic game theory and mechanism design
decision theory
0.112017
Moral Decision Making Frameworks for Artificial Intelligence · AAAI 2017
YearPublicationVenuePosition
2017 Moral Decision Making Frameworks for Artificial Intelligence
abstract
The generality of decision and game theory has enabled domain-independent progress in AI research. For example, a better algorithm for finding good policies in (PO)MDPs can be instantly used in a variety of applications. But such a general theory is lacking when it comes to moral decision making. For AI applications with a moral component, are we then forced to build systems based on many ad-hoc rules? In this paper we discuss possible ways to avoid this conclusion.
Vincent Conitzer, Walter Sinnott-Armstrong, Jana Schaich Borg, Max Kramer 0001
AAAI5