This research focused on learning ‘natural concepts’: creating an agent capable of recognising noisy categories such as ‘chair’ and ‘bird’, which have no crisply defined boundaries but which humans can easily identify without explicit, formal definitions.

Early machine learning models attempted to employ GOFAI (good old-fashioned AI) techniques, which represent concepts in symbolic logic and use formal reasoning to learn natural concepts, without much success. Instead, we believe, an effective agent may may build upon lessons learned from the human ability to perform classification tasks quickly and effectively despite moderate noise. A more successful learning agent must therefore borrow from the interdisciplinary literature on concept formation and models of representation from cognitive psychology and the philosophy of mind corpora.

This paper begins with a comparative study of of existing supervised learning methods, such as decision trees, neural networks, bayesian methods, and semantic networks, and their effectiveness of learning natural concepts. We examine concept acquisition and concept formation models in cognitive psychology, focusing on prototype-based and examplar-based learning. We continue with lessons learned from the philosophical examination of the nature of natural concepts, including notions of family resemblance and polymorphism, which our learning agent must account for.

We describe an instance-based learning model which builds upon the lessons learned about the human performance in natural concept learning tasks, implemented in C++. We conduct an empirical study comparing the performance of our learning model with existing machine learning algorithms in two learning tasks: