Summary of Cobweb: An Incremental and Hierarchical Model Of Human-like Category Learning, by Xin Lian et al.
Cobweb: An Incremental and Hierarchical Model of Human-Like Category Learning
by Xin Lian, Sashank Varma, Christopher J. MacLellan
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents Cobweb, a unique human-like category learning system that constructs hierarchical tree-like structures based on the category utility measure. Unlike traditional cognitive science models, Cobweb has been shown to capture psychological effects such as basic-level, typicality, and fan effects in prior studies. The current study aims to further evaluate Cobweb’s alignment with classical human category learning effects and its flexibility to exhibit both exemplar- and prototype-like learning within a single framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cobweb is a special kind of computer program that helps us understand how people group things into categories. This program, called Cobweb, builds a special kind of tree structure based on what’s important for each category. It’s been shown to work well in certain situations, but this study looks at whether it can work well in even more situations. The goal is to see if Cobweb can help us understand how people learn and group things together. |
Keywords
* Artificial intelligence * Alignment
