Learning to Understand by Evolving Theories
Machine Learning
2013-07-30 v1 Artificial Intelligence
Abstract
In this paper, we describe an approach that enables an autonomous system to infer the semantics of a command (i.e. a symbol sequence representing an action) in terms of the relations between changes in the observations and the action instances. We present a method of how to induce a theory (i.e. a semantic description) of the meaning of a command in terms of a minimal set of background knowledge. The only thing we have is a sequence of observations from which we extract what kinds of effects were caused by performing the command. This way, we yield a description of the semantics of the action and, hence, a definition.
Cite
@article{arxiv.1307.7303,
title = {Learning to Understand by Evolving Theories},
author = {Martin E. Mueller and Madhura D. Thosar},
journal= {arXiv preprint arXiv:1307.7303},
year = {2013}
}
Comments
KRR Workshop at ICLP 2013