Using inverse resolution to learn relations from experiments

We are concerned with learning relations in a reactive environment. A learning agent observes sequences of actions that may change the prop-erties or relationships of objects in the world. The observed sequence is then used to form a theory which can be generalised and tested by imi-tation. That is, the learner attempts to perform its own sequence of ac-tions. If the test completes as expected then the generalisation is ac-cepted. However, if it fails then a regeneralisation occurs. That is, further generalisations are found to explain the difference between expectation and reality. Inverse Resolution is the primary generalisation mechanism. How-ever, learning by experimentation in a reactive environment causes diffi-culties that inverse resolution alone cannot handle. We describe the addi-tions required to the theory that enable us to deal with ‘surprise ’ in an experiment, i.e. learning from unexpected results. New inverse resolu-tion operations are used to allow us to generalise from partial matches between our theory and the world, thus enabling us to explain what went wrong with a theory