EDBT 2026 Demo / reviewers in the wild / expert
David Pierce
dblp:81/6043
· DBLP profile ↗
5ranked-venue papers
5as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-authorTheory of computation · 2 · 2 first-authorSystems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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
3 papers |
Robot navigation and mapping · 56% Motion planning and robot control · 25% Reinforcement learning · 19% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › map building
map learning |
0.0 | 1 | 1997 | Map Learning with Uninterpreted Sensors and Effectors · Artif. Intell. 1997 |
Machine learning › Reinforcement learning
exploration |
0.0 | 1 | 1994 | Learning to Explore and Build Maps · AAAI 1994 |
Robotics › Robot navigation and mapping
map building |
0.0 | 1 | 1994 | Learning to Explore and Build Maps · AAAI 1994 |
Robotics › Motion planning and robot control › robot learning
motion primitive learning |
0.0 | 1 | 1991 | Learning turn and travel actions with an uninterpreted sensorimotor apparatus · ICRA 1991 |
Robotics › Motion planning and robot control › robot learning
sensorimotor learning |
0.0 | 1 | 1991 | Learning turn and travel actions with an uninterpreted sensorimotor apparatus · ICRA 1991 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 0.0unsupervised learning · 0.0range sensing · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Fields with several Commuting DerivationsabstractAbstract For every natural numberm, the existentially closed models of the theory of fields withmcommuting derivations can be given a first-order geometric characterization in several ways. In particular, the theory of these differential fields has a model-companion. The axioms are that certain differential varieties determined by certain ordinary varieties are nonempty. There is no restriction on the characteristic of the underlying field. David Pierce |
J. Symb. Log. | 1 |
| 2003 | Differential forms in the model theory of differential fieldsabstractAbstract Fields of characteristic zero with several commuting derivations can be treated as fields equipped with aspaceof derivations that is closed under the Lie bracket. The existentially closed instances of such structures can then be given a coordinate-free characterization in terms of differential forms. The main tool for doing this is a generalization of the Frobenius Theorem of differential geometry. David Pierce |
J. Symb. Log. | 1 |
| 1997 | Map Learning with Uninterpreted Sensors and Effectors
David Pierce, Benjamin Kuipers |
Artif. Intell. | 1 |
| 1994 | Learning to Explore and Build Maps
David Pierce, Benjamin Kuipers |
AAAI | 1 |
| 1991 | Learning turn and travel actions with an uninterpreted sensorimotor apparatusabstractA learning method by which a mobile robot may analyze an initially uninterpreted sensorimotor apparatus and produce a useful characterization of its set of actions is demonstrated. By initially uninterpreted it is meant that the robot is given no knowledge of the structure of its sensory system nor of the effects of its actions. It merely sees and produces vectors of real numbers. The method is applied to the case of a simulated robot with an array of 16 range finders, and a motor apparatus with which it can make combinations of turning and advancing actions. The robot learns a set of primitive actions allowing it to make pure turns (both clockwise and counterclockwise) and pure travels. It is believed that this approach is robust and will apply to sensory systems used for motion detection, such as arrays of photoreceptors or range finders.> David Pierce |
ICRA | 1 |