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Rajkumar Doshi

dblp:22/3692 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1986
—ORCID · none

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

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 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
2 papers
Planning, search and constraint satisfaction · 64% Knowledge representation and reasoning · 28% Motion planning and robot control · 8%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
plan execution		 0.011986
Generating Perception Requests and Expectations to Verify the Execution of Plans · AAAI 1986
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning › planning evaluation
plan verification		 0.011986
Generating Perception Requests and Expectations to Verify the Execution of Plans · AAAI 1986
Robotics › Motion planning and robot control
robot control architecture		 0.011985
The role of knowledge in the architecture of a robust robot control · ICRA 1985

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

expectation generation · 0.0knowledge base · 0.0
YearPublicationVenuePosition
1986 Generating Perception Requests and Expectations to Verify the Execution of Plans
Richard J. Doyle, David Atkinson, Rajkumar Doshi
 AAAI3
1985 The role of knowledge in the architecture of a robust robot control
abstract
We would like robots to recognize and handle situations that do not conform with normal operating conditions. We want to be able to do this without having to consider explicitly errors caused by missing or defective parts, or by malfunctioning. To this end we present the detailed design of a system in which the controller of the robot takes advantage of large knowledge bases to ensure proper execution of the robot task. Real time considerations played a large role in our design.
Maria L. Gini, Rajkumar Doshi, Marc Gluch, Richard E. Smith, Imran A. Zualkernan
 ICRA2