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Katsuhiro Mayama

dblp:117/2152 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

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
Knowledge representation and reasoning · 100%
Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › commonsense reasoning
commonsense knowledge
0.112012
Evaluation of commonsense knowledge for intuitive robotic service · ICRA 2012
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge management
knowledge evaluation
0.112012
Evaluation of commonsense knowledge for intuitive robotic service · ICRA 2012

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

weighting mechanism · 0.3questionnaire · 0.3
YearPublicationVenuePosition
2012 Evaluation of commonsense knowledge for intuitive robotic service
abstract
Human commonsense is required to improve quality of robotic application. However, to acquire the necessary knowledge, robot needs to evaluate the appropriateness of the data it has collected. This paper presents an evaluation method, by combining the weighting mechanism in commonsense databases with a set of weighting factors. The method was verified on our Basic-level Knowledge Network. We conducted questionnaire to collect a commonsense data set and estimate weighting factors. Result showed that, the proposed method was able to build Robot Technology (RT) Ontology for a smart “Bring something” robotic service. More importantly, it allowed robot to learn new knowledge when necessary. An intuitive human-robot interface application was developed as an example base on our approach.
Trung Ngo Lam, Haeyeon Lee, Katsuhiro Mayama, Makoto Mizukawa
ICRA3