Demo Data from EDBT 2026 / reviewers in the wild / expert
Davide De Martini
dblp:358/2570
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 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 · 56% Knowledge representation and reasoning · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition
knowledge base construction |
0.8 | 1 | 2024 | When Prolog Meets Generative Models: a New Approach for Managing Knowledge and Planning in Robotic Applications · ICRA 2024 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
temporal planning |
0.8 | 1 | 2024 | When Prolog Meets Generative Models: a New Approach for Managing Knowledge and Planning in Robotic Applications · ICRA 2024 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
robot task planning |
0.2 | 1 | 2024 | When Prolog Meets Generative Models: a New Approach for Managing Knowledge and Planning in Robotic Applications · ICRA 2024 |
Methods — techniques the papers use, named apart from their topics
prolog · 0.8large language model · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | When Prolog Meets Generative Models: a New Approach for Managing Knowledge and Planning in Robotic ApplicationsabstractIn this paper, we propose a robot oriented knowledge representation system based on the use of the Prolog language. Our framework hinges on a special organisation of Knowledge Base (KB) that enables: 1) its efficient population from natural language texts using semi-automated procedures based on Large Language Models (LLMs); 2) the seamless generation of temporal parallel plans for multi-robot systems through a sequence of transformations; 3) the automated translation of the plan into an executable formalism. The framework is supported by a set of open source tools and its functionality is shown with a realistic application. Enrico Saccon, Ahmet Tikna, Davide De Martini, Edoardo Lamon, Luigi Palopoli 0002, Marco Roveri |
ICRA | 3 |