TL;DR
This work proposes to abstract the planning task for abstract solutions that are then repaired into concrete plans for the input task, and showcases domains in which drawing an abstract plan and repairing it is significantly faster than solving the original problem.
Abstraction heuristics are among the most effective approaches in optimal classical planning. For numeric planning, however, existing abstraction heuristics suffer from the infiniteness of the abstract state spaces, which is an immediate consequence of numeric state variables. This has recently been analyzed for numeri...
Background: Counterexample-Guided Abstraction Refinement (CEGAR) is a prominent technique to generate Cartesian abstractions for guiding search in cost-optimal planning. The core idea is to start from the trivial abstraction—an abstract state representing all concrete states—and iteratively refine it in a loop by split...
AI planning is concerned with finding a sequence of actions that achieves a specified goal. It relies on explicit models of the world, commonly represented in the Planning Domain Definition Language (PDDL). An active line of research investigates how errors in such models can be detected and repaired. For example, user...
In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a particular formula. It has many applications, such as allowing for more robust plan execution and providing compact policies for non-deterministic planning. Although relatively simple t...
Timed Partial Orders (TPOs), originally proposed for workflows, provide an interpretable framework for robot task specification with planning algorithms based on mixed-integer linear programming (MILP). However, TPOs are limited in expressivity, capturing only partial-order events with simple timing constraints. In thi...
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