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 [4], 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, given some assumptions, inductive programming with incremental learning can be shown to have a polynomial, rather than exponential, time complexity with respect to the size of the program to be generated. We discuss the difficulties involved in constructing suitable problem sequences for our incremental learning system, and consider what improvements can be made to overcome these difficulties. © 2010 Springer-Verlag.
CITATION STYLE
Henderson, R. (2010). Incremental learning in inductive programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5812 LNCS, pp. 74–92). Springer Verlag. https://doi.org/10.1007/978-3-642-11931-6_4
Mendeley helps you to discover research relevant for your work.