Demo Data from SIGIR 2026 / reviewers in the wild / expert
Deqian Chen
dblp:29/5232
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1994
—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 |
Legged, aerial and field robots · 62% Motion planning and robot control · 19% Planning, search and constraint satisfaction · 19% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Legged, aerial and field robots › construction robotics
excavation |
0.0 | 1 | 1994 | A Fuzzy Control System for an Automated Mining Excavator · ICRA 1994 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › intelligent control
fuzzy control |
0.0 | 1 | 1994 | A Fuzzy Control System for an Automated Mining Excavator · ICRA 1994 |
Robotics › Motion planning and robot control
robot control |
0.0 | 1 | 1994 | A Fuzzy Control System for an Automated Mining Excavator · ICRA 1994 |
Methods — techniques the papers use, named apart from their topics
fuzzy logic · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1994 | A Fuzzy Control System for an Automated Mining ExcavatorabstractTo automate a mining excavator the control issues resulting from environmental uncertainties must be solved. In particular the interactions between the excavation tool and the excavation environment are dynamic, unstructured and complex. Thus, generating rigorous mathematical models as required by conventional control techniques is extremely difficult if possible. This paper presents an excavator controller that uses fuzzy logic to interpret forces and torques gathered from a wrist mounted force/torque sensor during the excavation process. This approach enables the transfer of human heuristics and expert knowledge to the controller. Experimental results from several excavation tests using the fuzzy logic controller are given. These results represent the first step toward an integrated approach that uses force/torque, vision, and other sensor data to perform generic tasks in uncertain mining environments.> Paul J. A. Lever, Fei-Yue Wang 0001, Deqian Chen |
ICRA | 3 |