Incremental Learning from Scratch Using Analogical Reasoning

Abstract

This paper explores the application of formal analogical reasoning to incremental machine learning. The applicative context is the design of an operational assistant. The specific learning task that is focused on is the transfer from requests in natural language to commands in programming language. This work explores two questions for applying analogy in incremental learning situations: How does formal analogical reasoning behave in incremental learning situation? How do the conditions on the learning sequence influence the performance? To address these issues, an experimental setup is proposed in which multiple users are simulated. The knowledge transfer from one to the other is studied. Moreover, we discuss the influence of the order in which examples are presented to the system on the learning process.