OPUS: An Efficient Admissible Algorithm for Unordered Search

TL;DR

OPUS is a novel branch and bound algorithm that efficiently performs admissible unordered search, enabling precise bias specification in complex machine learning tasks and potential applications in AI areas like truth maintenance.

Contribution

It introduces OPUS, an efficient admissible search algorithm tailored for unordered spaces, improving search efficiency in large machine learning and AI problems.

Findings

Demonstrates efficiency on large search spaces

Enables precise bias control in machine learning

Potential applications in AI areas like truth maintenance

Abstract

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.