Incremental Learning in Inductive Programming

Inductive programming systems characteristically exhibit an exponential explosion in search time as one increases the size of the programs to be generated. As a way of overcoming this, we introduce incremental learning, a process in which an inductive programming system automatically modifies its inductive bias towards some domain through solving a sequence of gradually more difficult problems in that domain. We demonstrate a simple form of incremental learning in which a system incorporates solution programs into its background knowledge as it progresses through a sequence of problems. Using a search-based inductive functional programming system modelled on the MagicHaskeller system of Katayama (2007), we perform a set of experiments comparing the performance of inductive programming with and without incremental learning. Incremental learning is shown to produce a performance improvement of at least a factor of thirty on each of the four problem sequences tested. We describe how, giv...

• Type: Conference
• Year: 2009
• Where: AAIP
• Authors: Robert Henderson
• Keywords: AAIP 2009, Incremental Learning, Inductive Programming, Programming Languages, Programming System