An Investigation of Learning Behavioural Functions

1. An Investigation of Learning Behavioural Functions http://www.cs.ru.ac.za/research/g98H3690/ vhata@rucus.ru.ac.za Jonathan Hitchcock, Computer Science Honours, Rhodes University Supervised by George Wells • APPLICATIONS • Learning interfaces can adapt to users preferences • Datamining: learning which advertisements “work”, for example. • Image recognition • Stock market predictions • AIMS • investigate the various methods • discover whether learning is possible for various functions • ascertaining which methods are best for them. Stimulus Environment History ACTION Behavioural Function Different Implementations O U T P U T I N P U T 1:2 Generated from the gene INPUT OUTPUT New altered genes 2:1 GENE Brute-force/Look-ahead Genetic Neural Net The function is generated from a set of data called the Gene. The Gene is altered several times, and each new alteration is tested. Beneficial alterations are kept, bad ones are discarded, resulting in overall improvement All alternative outputs for a particular input are tested, and the best one is chosen, based on how many “good” and “bad” results it leads to. Neural Nets are capable of simulating any function, and of adapting their output so that it better approximates the correct output, thus learning to operate correctly.