We start by comparing the heuristic approaches within our
planners. In the next section, we continue by describing how our
planners, using the best heuristics, compare against other state
of the art approaches. In this section we intend to validate our claims that
belief space heuristics that measure overlap perform well across several domains.
We further justify using the 
over multiple planning graphs and applying mutexes to
improve heuristics in regression through pruning belief states.
We compare many techniques within
CAltAlt and 
on our conformant planning domains, and in
addition we test the heuristics in
on the conditional
domains. Our performance metrics include the total planning time
and the number of search nodes expanded. Additionally, when
discussing mutexes we analyze planning graph construction time.
We proceed by showing how the heuristics perform in CAltAlt and
then how various mutex computation schemes for the
can
affect performance. Then we present how
performs with the
different heuristics in both conformant and conditional domains,
explore the effect of sampling a proportion of worlds to build
, 
, and
graphs, and compare the heuristic
estimates in
to the optimal plan length to gauge heuristic
accuracy. We finish with a summary of important conclusions.
We only compute mutexes in the planning graphs for CAltAlt
because the planning graph(s) are only built once in a search
episode and mutexes help prune the inconsistent belief states
encountered in regression search. We abstain from computing
mutexes in
because in progression we build new planning
graphs for each search node and we want to keep graph computation
time low. With the exception of our discussion on sampling worlds
to construct the planning graphs, the planning graphs are
constructed deterministically. This means that the single graph
is the unioned single graph 
, and the
and
graphs
are built for all possible worlds.