OPUS: An Efficient Admissible Algorithm for Unordered Search
  OPUS is a branch and bound search algorithm that enables efficient admissible
search through spaces for which the order of search operator application is not
significant. The algorithm's search efficiency is demonstrated with respect to
very large machine learning search spaces. The use of admissible search is of
potential value to the machine learning community as it means that the exact
learning biases to be employed for complex learning tasks can be precisely
specified and manipulated. OPUS also has potential for application in other
areas of artificial intelligence, notably, truth maintenance.
