An Investigation of Learning Behavioural Functions

Stimulus

Environment

History

ACTIONBehavioural

Function

Brute-force/Look-ahead

Genetic Neural Net

1:2

2:1INPU

TOUTPUT

Generated from the

gene

INPUT

OUTPUT

New altered genes

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.

GENE

Different Implementations

http://www.cs.ru.ac.za/research/g98H3690/vhata@rucus.ru.ac.za

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.

Jonathan Hitchcock, Computer Science Honours, Rhodes UniversitySupervised by George Wells