  A general-purpose planning agent requires an open-scope world model: one rich
enough to tackle any of the wide range of tasks it may be asked to solve over
its operational lifetime. This stands in contrast with typical planning
approaches, where the scope of a model is limited to a specific family of tasks
that share significant structure. Unfortunately, planning to solve any specific
task using an open-scope model is computationally intractable - even for
state-of-the-art methods - due to the many states and actions that are
necessarily present in the model but irrelevant to that problem. We propose
task scoping: a method that exploits knowledge of the initial state, goal
conditions, and transition system to automatically and efficiently remove
provably irrelevant variables and actions from a planning problem. Our approach
leverages causal link analysis and backwards reachability over state variables
(rather than states) along with operator merging (when effects on relevant
variables are identical). Using task scoping as a pre-planning step can shrink
the search space by orders of magnitude and dramatically decrease planning
time. We empirically demonstrate that these improvements occur across a variety
of open-scope domains, including Minecraft, where our approach leads to a 75x
reduction in search time with a state-of-the-art numeric planner, even after
including the time required for task scoping itself.
