Table 9 shows the results for testing the conditional versions of the domains on , MBP, GPT, SGP, and YKA.
MBP: The planner is very similar to MBP in that it uses progression search. uses an AO* search, whereas the MBP binary we used uses a depth first And-Or search. The depth first search used by MBP contributes to highly sub-optimal maximum length branches (as much as an order of magnitude longer than ). For instance, the plans generated by MBP for the Rovers domain have the rover navigating back and forth between locations several times before doing anything useful; this is not a situation beneficial for actual mission use. MBP tends to not scale as well as in all of the domains we tested. A possible reason for the performance of MBP is that the Logistics and Rovers domains have sensory actions with execution preconditions, which prevent branching early and finding deterministic plan segments for each branch. We experimented with MBP using sensory actions without execution preconditions and it was able to scale somewhat better, but plan quality was much longer.
Optimal Planners: GPT and SGP generate better solutions but very slowly. GPT does better on the Rovers and Logistics problems because they exhibit some positive interaction in the plans, but SGP does well on BT because its planning graph search is well suited for shallow, yet broad (highly parallel) problems.
YKA: We see that YKA fares similar to GPT in Rovers and Logistics, but has trouble scaling for other reasons. We think that YKA may be having trouble in regression because of sensory actions since it was able to scale reasonably well in the conformant version of the domains. Despite this, YKA proves to do very well in the BT and BTC problems.