  The task of artificial intelligence is to provide representation techniques
for describing problems, as well as search algorithms that can be used to
answer our questions. A widespread and elaborated model is state-space
representation, which, however, has some shortcomings. Classical search
algorithms are not applicable in practice when the state space contains even
only a few tens of thousands of states. We can give remedy to this problem by
defining some kind of heuristic knowledge. In case of classical state-space
representation, heuristic must be defined so that it qualifies an arbitrary
state based on its "goodness," which is obviously not trivial. In our paper, we
introduce an algorithm that gives us the ability to handle huge state spaces
and to use a heuristic concept which is easier to embed into search algorithms.
