In light of this, this section concentrates on displaying results for LPSAT in an interesting domain and on describing the heuristics and optimization we used to enhance LPSAT's performance. We report LPSAT solve time, running on a Pentium II 450 MHz processor with 128 MB of RAM, averaged over 20 runs per problem, and showing 95 percent confidence intervals. We do not include compile time for the (unoptimized) compiler since the paper's focus is the design and optimization of LPSAT; however, compile time can be substantial (e.g., twenty minutes on log-c).
We report on a sequence of problems in the metric logistics domain, which includes all the features of the ATT logistics domain [Kautz and Selman1996]: airplanes and trucks moving packages among cities and sites within cities. However, our metric version adds fuel and distances between cities; airplanes and trucks both have individual maximum fuel capacities, consume fuel to move (the amount is per trip for trucks and based on distance between cities for airplanes), and can refuel at depots. log-a through log-d are the same as the ATT problems except for the addition of fuel. easy-1 through easy-4 are simplifications of log-a with more elements retained in the higher numbered problems. We report on highly successful experiments with learning and backjumping as well as two other interesting optimizations.