On Portfolios for Backtracking Search in the Presence of Deadlines

Constraint satisfaction and propositional satisfiability problems are often solved using backtracking search. Previous studies have shown that portfolios of backtracking algorithms—a selection of one or more algorithms plus a schedule for executing the algorithms—can dramatically improve performance on some instances. In this paper, we consider a setting that often arises in practice where the instances to be solved arise over time, the instances all belong to some class of problem instances, and a limit or deadline is placed on the computational resources that the backtracking algorithm can consume in solving any instance. For such a scenario, we present a simple scheme for learning a good portfolio of backtracking algorithms from a small sample of instances. We demonstrate the effectiveness of our approach through an extensive empirical evaluation on a real-world instruction scheduling testbed.

• Type: Conference
• Year: 2007
• Where: ICTAI
• Authors: Huayue Wu, Peter van Beek
• Keywords: Artificial Intelligence, Backtracking, Backtracking Algorithms, Backtracking Search, ICTAI 2007