New Techniques for Algorithm Portfolio Design

We present and evaluate new techniques for designing algorithm portfolios. In our view, the problem has both a scheduling aspect and a machine learning aspect. Prior work has largely addressed one of the two aspects in isolation. Building on recent work on the scheduling aspect of the problem, we present a technique that addresses both aspects simultaneously and has attractive theoretical guarantees. Experimentally, we show that this technique can be used to improve the performance of state-of-the-art algorithms for Boolean satisfiability, zero-one integer programming, and A.I. planning.

• Type: Conference
• Year: 2008
• Where: UAI
• Authors: Matthew J. Streeter, Stephen F. Smith
• Keywords: Artificial Intelligence, Attractive Theoretical Guarantees, Machine Learning Aspect, Scheduling Aspect, UAI 2008