ABSTRACT
There has been much interest over several years in the use of neural networks within human-computer interaction. However, this promise has led to surprisingly few published results. This article reviews those applications which have been addressed by neural networks or similar techniques. It also describes the use of the ADAM neural network for task recognition from traces of user interaction with a bibliographic database. This achieved high accuracy rates in training and in some on-line use. However, there were significant problems with its use. These problems are of interest not just for this system, but for any which is attempting to analyze trace data. The two main problems were due to the continuous sequential data and the presence of literal input (personal names, file names, dates and so on). Those systems which have achieved success in this area have not used neural techniques, but instead more traditional (although often ad hoc) methods. However, it is expected that recurrent networks may be suitable but probably only within a hybrid approach.
