Trial and error learning

Trial and error is a method for obtaining knowledge, both propositional knowledge and know-how. In trial and error, one tries an option to see if it works. If it works, then we have a solution. If it doesn't work - there is an error - then one tries another option.

In some versions of trial and error, the option that is a priori viewed as the most likely one should be tried first, followed by the next most likely, and so on until a solution is found, or all the options are exhausted. In other versions, options are simply tried at random.

Trial and error has a number of features:


 * solution-oriented: trial and error makes no attempt to discover why a solution works, merely that it is a solution.
 * problem-specific: trial and error makes no attempt to generalise a solution to other problems.
 * non-optimal: trial and error is an attempt to find a solution, not all solutions, and not the best solution.
 * needs little knowledge: trial and error can proceed where there is little or no knowledge of the subject.

Trial and error has traditionally been the main method of finding new drugs, such as antibiotics. Chemists simply try chemicals at random until they find one with the desired effect.

The scientific method can be regarded as containing an element of trial and error in its formulation and testing of hypotheses. Also compare genetic algorithms, simulated annealing and reinforcement learning - all varieties of search which apply the basic idea of trial and error.

Biological Evolution is also a form of trial and error. Random mutations and sexual genetic variations can be viewed as trials and poor reproductive fitness as the error. Thus after a long time 'knowledge' of well-adapted genomes accumulates simply by virtue of them being able to reproduce.

Bogosort can be viewed as a trial and error approach to sorting a list.

In mathematics the method of trial and error can be used to solve formulae - it is a slower, less precise method than algebra, but is easier to understand.