Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Mateus Mendes 0001

dblp:119/8553-1 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning › partially observable reinforcement learning
memory-based navigation
0.112008
Robot navigation using a sparse distributed memory · ICRA 2008
Robotics › Robot navigation and mapping
mobile robot navigation
0.112008
Robot navigation using a sparse distributed memory · ICRA 2008
Machine learning › Representation and self-supervised learning › associative memory
sparse distributed memory
0.112008
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
YearPublicationVenuePosition
2008 Robot navigation using a sparse distributed memory
abstract
Despite 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
ICRA1