Learning to Understand by Evolving Theories

TL;DR

This paper introduces a method for autonomous systems to infer the semantics of commands by inducing theories from observation sequences, linking actions to observed effects with minimal background knowledge.

Contribution

It presents a novel approach to automatically induce semantic theories of commands based solely on observational data and minimal prior knowledge.

Findings

Successfully infers action semantics from observation sequences

Provides a minimal background knowledge framework

Enables autonomous understanding of commands

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.