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A general-purpose planning agent requires an open-scope world model: one richenough to tackle any of the wide range of tasks it may be asked to solve overits operational lifetime. This stands in contrast with typical planningapproaches, where the scope of a model is limited to a specific family of tasksthat share significant structure. Unfortunately, planning to solve any specifictask using an open-scope model is computationally intractable - even forstate-of-the-art methods - due to the many states and actions that arenecessarily present in the model but irrelevant to that problem. We proposetask scoping: a method that exploits knowledge of the initial state, goalconditions, and transition system to automatically and efficiently removeprovably irrelevant variables and actions from a planning problem. Our approachleverages causal link analysis and backwards reachability over state variables(rather than states) along with operator merging (when effects on relevantvariables are identical). Using task scoping as a pre-planning step can shrinkthe search space by orders of magnitude and dramatically decrease planningtime. We empirically demonstrate that these improvements occur across a varietyof open-scope domains, including Minecraft, where our approach leads to a 75xreduction in search time with a state-of-the-art numeric planner, even afterincluding the time required for task scoping itself.

Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models