  One of the main research areas in Artificial Intelligence is the coding of
agents (programs) which are able to learn by themselves in any situation. This
means that agents must be useful for purposes other than those they were
created for, as, for example, playing chess. In this way we try to get closer
to the pristine goal of Artificial Intelligence. One of the problems to decide
whether an agent is really intelligent or not is the measurement of its
intelligence, since there is currently no way to measure it in a reliable way.
The purpose of this project is to create an interpreter that allows for the
execution of several environments, including those which are generated
randomly, so that an agent (a person or a program) can interact with them. Once
the interaction between the agent and the environment is over, the interpreter
will measure the intelligence of the agent according to the actions, states and
rewards the agent has undergone inside the environment during the test. As a
result we will be able to measure agents' intelligence in any possible
environment, and to make comparisons between several agents, in order to
determine which of them is the most intelligent. In order to perform the tests,
the interpreter must be able to randomly generate environments that are really
useful to measure agents' intelligence, since not any randomly generated
environment will serve that purpose.
