Glaucoma is a condition that affects the human eye and is an umbrella term for a family of related eye conditions. A common trait of these conditions is a functional abnormality of the retina and optic nerve, leading to loss of visual field. This vision loss is usually only part of the visual field, although, untreated, glaucoma often leads to blindness. It is thought that by 2010, there will be over 60 million people worldwide suffering from the various forms of Glaucoma. Visual field tests are crucial to the diagnosis and management of all types of glaucoma. Such tests require the level of retinal sensitivity to light to be sampled at a number of points typically between 50 and 100, depending on the type of test; the eye is then assigned a numerical value in the range 'no perception' (lowest) to 'perfect perception' (highest). A specialised machine is used to conduct these tests - a typical clinical test can take between six and seven minutes per eye. Once diagnosed with glaucoma or suspected glaucoma, a patient is monitored and recommended to undergo the same tests every six months (or more frequently in some cases). However due to the psychophysical nature of the glaucoma test, the results can therefore vary in quality dramatically. For example, they can be affected by patient fatigue (as the test can last for long periods) and attention span deficits (particularly in the elderly and children).This proposal aims to use data quality metrics (such as false positive and negative rates) to incorporate uncertainty into computational models that will also take into account the spatial and temporal nature of visual field data. The proposed research will use probabilistic Cellular Automata (CA) with an appropriate rule learning approach as the technique to model the visualfield deterioration of glaucoma sufferes. The aim is to accurately model visual field progression and to provide an aid to the clinical practitioners.This project is in collaboration with Moorfields Eye Hospital, London, UK.