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Florian Pham

dblp:354/0185 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 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
Planning, search and constraint satisfaction · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › domain model learning
macro-operator learning
0.712023
Can I Really Do That? Verification of Meta-Operators via Stackelberg Planning · IJCAI 2023

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

stackelberg planning · 0.7adversarial planning · 0.7
YearPublicationVenuePosition
2023 Can I Really Do That? Verification of Meta-Operators via Stackelberg Planning
abstract
Macro-operators are a common reformulation method in planning that adds high-level operators corresponding to a fixed sequence of primitive operators. We introduce meta-operators, which allow using different sequences of actions in each state. We show how to automatically verify whether a meta-operator is valid, i.e., the represented behavior is always doable. This can be checked at once for all instantiations of the meta-operator and all reachable states via a compilation into Stackelberg planning, a form of adversarial planning. Our results show that meta-operators learned for multiple domains can often express useful high-level behaviors very compactly, improving planners' performance.
Florian Pham, Álvaro Torralba
IJCAI1