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Davide De Martini

dblp:358/2570 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition
knowledge base construction
0.812024
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
0.812024
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
0.212024
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
YearPublicationVenuePosition
2024 When Prolog Meets Generative Models: a New Approach for Managing Knowledge and Planning in Robotic Applications
abstract
In 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
ICRA3