Demo Data from EDBT 2026 / reviewers in the wild / expert
Jarvis Xu
dblp:324/1799
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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% Reinforcement learning · 33% Multi-agent systems · 33% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › safe reinforcement learning
side effect avoidance |
0.6 | 1 | 2022 | Planning to Avoid Side Effects · AAAI 2022 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
symbolic planning |
0.6 | 1 | 2022 | Planning to Avoid Side Effects · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
cost-optimizing compilation · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Planning to Avoid Side EffectsabstractIn sequential decision making, objective specifications are often underspecified or incomplete, neglecting to take into account potential (negative) side effects. Executing plans without consideration of their side effects can lead to catastrophic outcomes -- a concern recently raised in relation to the safety of AI. In this paper we investigate how to avoid side effects in a symbolic planning setting. We study the notion of minimizing side effects in the context of a planning environment where multiple independent agents co-exist. We define (classes of) negative side effects in terms of their effect on the agency of those other agents. Finally, we show how plans which minimize side effects of different types can be computed via compilations to cost-optimizing symbolic planning, and investigate experimentally. Toryn Q. Klassen, Sheila A. McIlraith, Christian J. Muise, Jarvis Xu |
AAAI | 4 |