ML Anthology

DEA: An Architecture for Goal Planning and Classification

François Fleuret, Eric Brunet
NeCo 2000 pp. 1987-2008
doi:10.1162/089976600300015024 /neco/2000/fleuret2000neco-dea/

Abstract

We introduce the differential efficiency algorithm, which partitions a perceptive space during unsupervised learning into categories and uses them to solve goal-planning and classification problems. This algorithm is inspired by a biological model of the cortex proposing the cortical column as an elementary unit. We validate the generality of this approach by testing it on four problems with continuous time and no reinforcement signal until the goal is reached (constrained object moves, Hanoi tower problem, animat control, and simple character recognition).

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Cite

Text

Fleuret and Brunet. "DEA: An Architecture for Goal Planning and Classification." Neural Computation, 2000. doi:10.1162/089976600300015024

Markdown

[Fleuret and Brunet. "DEA: An Architecture for Goal Planning and Classification." Neural Computation, 2000.](https://mlanthology.org/neco/2000/fleuret2000neco-dea/) doi:10.1162/089976600300015024

BibTeX

@article{fleuret2000neco-dea,
  title     = {{DEA: An Architecture for Goal Planning and Classification}},
  author    = {Fleuret, François and Brunet, Eric},
  journal   = {Neural Computation},
  year      = {2000},
  pages     = {1987-2008},
  doi       = {10.1162/089976600300015024},
  volume    = {12},
  url       = {https://mlanthology.org/neco/2000/fleuret2000neco-dea/}
}