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León Illanes

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2 papers

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AAAI Conference 2019 Conference Paper

Generalized Planning via Abstraction: Arbitrary Numbers of Objects

León Illanes
Sheila A. McIlraith

We consider a class of generalized planning problems based on the idea of quantifying over sets of similar objects. We show how we can adapt fully observable nondeterministic planning techniques to produce generalized solutions that are easy to instantiate over particular problem instances. We also describe how we can reformulate a classical planning problem into a quantified one. The reformulation allows us to solve the original planning task without grounding every action with respect to all objects in the problem, and a single solution can be applied to a possibly infinite set of related classical planning tasks. We report experimental results that show our approach is a practical and promising technique for solving an interesting class of problems.

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IJCAI Conference 2017 Conference Paper

Numeric Planning via Abstraction and Policy Guided Search

León Illanes
Sheila A. McIlraith

The real-world application of planning techniques often requires models with numeric fluents. However, these fluents are not directly supported by most planners and heuristics. We describe a family of planning algorithms that takes a numeric planning problem and produces an abstracted representation that can be solved using any classical planner. The resulting abstract plan is generalized into a policy and then used to guide the search in the original numeric domain. We prove that our approach is sound, and we evaluate it on a set of standard benchmarks. We show that it can provide competitive performance when compared to other well-known algorithms for numeric planning, and a significant performance improvement in certain domains.

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