Evaluation and implementation of reinforcement learning and pattern recognition algorithms for task automation on web interfaces

Automated task execution in a web context is a major challenge today. One of the main fields in which this is needed is undoubtedly that of Information Security, where it is becoming increasingly necessary to find techniques that allow security tests to be carried out without human intervention. Not only to relieve programmers from performing repetitive tasks, but above all to be able to perform many more tests in the same amount of time. Although techniques already exist to automate the execution of actions on web interfaces, these solutions are often limited to running in the environment for which they were designed. It is, indeed, impossible for them to execute the learnt behaviour in different and unseen environments. The aim of this thesis project is to analyse different Machine Learning techniques in order to find an optimal solution to this problem. In other words, to obtain an agent capable of executing a task in all the environments in which it operates. The approaches analysed and implemented can be traced back to two areas of Machine Learning, Reinforcement Learning and Pattern Recognition. Each approach was tested using real web applications in order to measure their abilities in a context as close to reality as possible. Although Reinforcement Learning approaches were found to be the most automated, they failed to achieve satisfactory results. On the contrary, the Pattern Recognition approach was found to be the most capable of executing tasks, even complex ones, in different and unseen environments, requiring, however, a lot of preliminary work.