Abstract
The performance of domain-independent planning
systems heavily depends on how the planning task
has been modeled. This makes task reformulation
an important tool to get rid of unnecessary complexity and increase the robustness of planners with
respect to the model chosen by the user. In this paper, we represent tasks as factored transition systems (FTS), and use the merge-and-shrink (M&S)
framework for task reformulation for optimal and
satisficing planning. We prove that the flexibility of
the underlying representation makes the M&S reformulation methods more powerful than the counterparts based on the more popular finite-domain
representation. We adapt delete-relaxation and
M&S heuristics to work on the FTS representation
and evaluate the impact of our reformulation