KSL-01-10 + redirect page
Learning Predictive Compositional Hierarchies + Has identifier
Learning Predictive Compositional Hierarchies + Ksl tr id
| Learning Predictive Compositional Hierarchies |
Bibtype
inproceedings
Has publishing details
June,2001
Has title
Learning Predictive Compositional Hierarchies
Has where published
Knowledge Systems, AI Laboratory
Has year
2001
Title
Learning Predictive Compositional Hierarchies
Year
2001
Abstract
This paper explores the vital but overlook … This paper explores the vital but overlooked problem of learning compositional hierarchies in predictive models and presents a new sequential learning paradigm in which to study such models. Hierarchical compositional structure, like taxonomic structure, is a critical representation tool for Artificial Intelligence. Prominent existing work with hand-built systems demonstrates the potential of predictive models based on compositional hierarchies for making inferences that smoothly integrate bottom-up and top-down influences and for enabling the processing of representations spanning multiple levels of spatial or temporal resolution. Additionally, like taxonomic hierarchies, compositional hierarchies can be learned purely from primitive data in a general, unsupervised fashion and subsequently used to make predictions about unseen data. However, unlike taxonomies, for which numerous foundational learning algorithms exist, there has not been analogous foundational work on learning predictive compositional hierarchies. The core aim of learning such models is to identify in a bottom-up fashion frequently occurring repeated patterns, enabling the future discovery of even larger patterns. This process holds the potential to scale up automatically from fine-grained, low-level data to coarser, high-level representations, bridging a gap that has proved to be one of the biggest stumbling blocks on the way to creating significantly more complex and intelligent autonomous agents. complex and intelligent autonomous agents.
Note
A shorter, preliminary version appeared in the proceedings of the AAAI workshop on New Research Problems for Machine Learning, August, 2000.
Author
Karl Pfleger +
Booktitle
Knowledge Systems, AI Laboratory +
Has author
Karl Pfleger +
Has identifier
Learning Predictive Compositional Hierarchies +
Ksl tr id
Learning Predictive Compositional Hierarchies +
Month
June +
Process note
NO +
Categories InProceedings +, KSL Technical Report +, Publication +
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