*** Answers to questions & miscillaneous points ***
-Ginsberg's Approximate Planning uses a set subtraction operation to
remove subsets of a candidate set that are known to be incorrect. A
plan is approximately correct when (# of ways to be wrong)/ (# of ways
to be right) sufficiently approaches zero.
-Marr stated that any representation allows certain ideas to be
represented more easily, while other become more difficult or even
impossible to express. Using our representation, we can represent
"all sequences that do not start with A7," but we can't represent "all
sequences that do not contain A7."
-Assuming all refinement strategies are monotonic and a minimum
solution take k steps, then k refinements are needed to get a minimal
candidate of length k.
-The basic refinement planning template can search an infinite depth
if no solution exists. The more flexoble Split & Prune method can get
lost in an infinite search even if a solution exists. (Ex. After 1
refinement of 1-way rocket domain, performing a search on [0*Fly()