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Jarvis Xu

dblp:324/1799 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning › safe reinforcement learning
side effect avoidance
0.612022
Planning to Avoid Side Effects · AAAI 2022
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
symbolic planning
0.612022
Planning to Avoid Side Effects · AAAI 2022

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

cost-optimizing compilation · 0.6
YearPublicationVenuePosition
2022 Planning to Avoid Side Effects
abstract
In 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
AAAI4