Demo Data from EDBT 2026 / reviewers in the wild / expert
Mateus Mendes 0001
dblp:119/8553-1
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2008
0000-0003-4313-7966ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 |
Robot navigation and mapping · 33% Reinforcement learning · 33% Representation and self-supervised learning · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › partially observable reinforcement learning
memory-based navigation |
0.1 | 1 | 2008 | Robot navigation using a sparse distributed memory · ICRA 2008 |
Robotics › Robot navigation and mapping
mobile robot navigation |
0.1 | 1 | 2008 | Robot navigation using a sparse distributed memory · ICRA 2008 |
Machine learning › Representation and self-supervised learning › associative memory
sparse distributed memory |
0.1 | 1 | 2008 | Robot navigation using a sparse distributed memory · ICRA 2008 |
Methods — techniques the papers use, named apart from their topics
image sequence learning · 0.1associative memory · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Robot navigation using a sparse distributed memoryabstractDespite all the progress that has been made in Robotics and Artificial Intelligence, traditional approaches seem unsuitable to build truly intelligent robots, exhibiting human-like behaviours. Many authors agree that the source of intelligence is, to a great extent, the use of a huge memory, where sequences of events that guide our later behaviour are stored. Inspired by that idea, our approach is to navigate a robot using sequences of images stored in a Sparse Distributed Memory-a kind of associative memory based on the properties of high dimensional binary spaces, which, in theory, exhibits some human-like behaviours. The robot showed good ability to correctly follow most of the sequences learnt, with small errors and good immunity to the kidnapped robot problem. Mateus Mendes 0001, Manuel M. Crisóstomo, A. Paulo Coimbra |
ICRA | 1 |