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Jun'ichiro Otsubo

dblp:89/6253 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2002
—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
Motion planning and robot control · 50% Planning, search and constraint satisfaction · 50%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
behavior control
0.012002
Emotional Intelligence for Linking Symbolic Behaviors · ICRA 2002
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › intelligent control
behavior selection
0.012002
Emotional Intelligence for Linking Symbolic Behaviors · ICRA 2002

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

q-learning · 0.0consciousness-based architecture · 0.0
YearPublicationVenuePosition
2002 Emotional Intelligence for Linking Symbolic Behaviors
abstract
A six-layered architecture for behavior control, consciousness-based architecture (CBA), is developed to link symbolic behaviors to low-level behaviors such as reflex action, detour and pursuit. This architecture makes possible behaviors categorized in "use of media" in parallel with running low-level behaviors. Two emotion-valued criteria are given for vertical and horizontal behavior selection. While a level of behavior is chosen to maximize the consciousness intensity, a behavior at the level chosen is selected to increase the pleasure. In CBA, the obstruction of behavior triggers the elevation of the level of consciousness. Computer simulation successfully shows symbolic detour of an obstacle and enclosure of a prey by three creatures predicting the positions of mates, a prey and an obstacle. CBA also shows its advantage over a Q-learning technique for the obstacle detouring problem.
Tadashi Kitamura, Jun'ichiro Otsubo, Makoto Abe
ICRA2