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A. Harry Klopf

dblp:58/702 · DBLP profile ↗
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2ranked-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 · 2

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 · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › optimization-based control
differential game
0.011994
Advantage Updating Applied to a Differrential Game · NIPS 1994

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

advantage updating · 0.0
YearPublicationVenuePosition
1994 Advantage Updating Applied to a Differrential Game
Mance E. Harmon, Leemon Baird, A. Harry Klopf
NIPS3
1990 A drive-reinforcement neural network model of simple instrumental conditioning
abstract
A network of classically conditionable drive-reinforcement neurons learned to choose an appropriate instrumental response to cues in a T maze when the cues could be utilized to anticipate the presentation of a positive or negative reinforcer or the absence of a reinforcer. To prove generality, it was shown that a network trained to respond to one configuration of reinforcers in the maze could learn to respond appropriately when the configuration is reversed. When the learning system turned toward the one arm of the maze it was negatively reinforced. When this happened the opposing intentional neuron corresponding to the active effector became active. The opposing intentional neuron corresponding to the inactive effector remained inactive because its high threshold precluded activity unless both the negative reinforcement center and its corresponding effector were active. When either opposing intentional neuron became active, it inhibited the currently active effector neuron and excited the other. A negatively reinforcing event therefore had the effect of inhibiting the responses that led to it. causing the connection strength from the T sensor to that effector neuron to decrease and also excited alternative actions
James S. Morgan, E. C. Patterson, A. Harry Klopf
IJCNN3