Pioneering work on formal learning was done by Solomonoff (1964). As before, the setting is one in which the data consist of strings of 0's and 1's, and in which an agent is attempting to identify the pattern in these data. So, for example, the data may be a string of the form \(0101010101\ldots\), and the challenge is to identify this strings as an alternating sequence. The central idea of Solomonoff is that all possible computable patterns must be considered by the agent, and therefore that no restrictive choice on statistical hypotheses is warranted. Solomonoff then defined a formal system in which indeed all patterns can be taken into consideration, effectively using a Bayesian analysis with a cleverly constructed prior over all computable hypotheses.