Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models
Michael Fishman, Nishanth Kumar, Cameron Allen, Natasha Danas, Michael, Littman, Stefanie Tellex, George Konidaris

TL;DR
The paper introduces task scoping, a method to automatically reduce the complexity of open-scope planning models by removing irrelevant variables and actions, significantly improving planning efficiency across diverse domains.
Contribution
It presents a novel task scoping technique that leverages causal analysis and reachability to efficiently simplify planning models for open-scope environments.
Findings
Achieves up to 75x reduction in search time in Minecraft domain.
Effectively shrinks search space by orders of magnitude.
Demonstrates broad applicability across multiple open-scope domains.
Abstract
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…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Software Testing and Debugging Techniques · Topic Modeling
