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David Pierce

dblp:81/6043 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › map building
map learning
0.011997
Map Learning with Uninterpreted Sensors and Effectors · Artif. Intell. 1997
Machine learning › Reinforcement learning
exploration
0.011994
Learning to Explore and Build Maps · AAAI 1994
Robotics › Robot navigation and mapping
map building
0.011994
Learning to Explore and Build Maps · AAAI 1994
Robotics › Motion planning and robot control › robot learning
motion primitive learning
0.011991
Learning turn and travel actions with an uninterpreted sensorimotor apparatus · ICRA 1991
Robotics › Motion planning and robot control › robot learning
sensorimotor learning
0.011991
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
YearPublicationVenuePosition
2014 Fields with several Commuting Derivations
abstract
Abstract 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 fields
abstract
Abstract 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
AAAI1
1991 Learning turn and travel actions with an uninterpreted sensorimotor apparatus
abstract
A 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
ICRA1