Adapting TED-A* to AGGLPlanner
In TED-A* we’ve
operator chunks consisting of several operators and each chunk has it’s own
run time. TED-A* consists of many cycles, in each cycle A* search is performed. Each cycle takes different amount of
operator chunks into consideration. New
operator chunks are introduced every cycle. In a cycle, A* is performed using an
operator chunk for time equal to its
Let’s say in a cycle we’ve:
having corresponding run-times:
In TED-A* algorithm we’ll perform A* search using an
operator_chunki for it’s
run_timei. We’ll do it for all
operator chunks under consideration in the current cycle.
Three important things to note are:
i) In TED-A, we assume all operator chunks to be disjoint.
ii) Number of *
operator chunk under consideration will be superset of it’s preceeding cycles.
iii) When no longer
operator chunk can be considered, a cycle will saturate. Every cycle after that will contain all possible
operator chunk and each chunk will be run for it’s corresponding
When introducing TED-A* to the current implementation of AGGLPlanner, we tweak the proposed TED-A* algorithm a bit for the ease of implementation. Now for every cycle we’ll have only one
operator chunk (which is union of all operator chunks for a cycle in TED-A). Also now, instead of *
operator chunk, every cycle will have a
The only difference is the time allocation pattern to each