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20212025
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cs.AI2025

Dynamic Tree Databases in Automated Planning

Oliver Joergensen, Dominik Drexler, Jendrik Seipp

A central challenge in scaling up explicit state-space search for large tasks is compactly representing the set of generated states. Tree databases, a data structure from model che…

cs.AI2025

Lifted Successor Generation in Numeric Planning

Dominik Drexler

Most planners ground numeric planning tasks, given in a first-order-like language, into a ground task representation. However, this can lead to an exponential blowup in task repres…

cs.AI2024

Symmetries and Expressive Requirements for Learning General Policies

Dominik Drexler, Simon Ståhlberg, Blai Bonet +1

State symmetries play an important role in planning and generalized planning. In the first case, state symmetries can be used to reduce the size of the search; in the second, to re…

cs.AI2022

Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version

Dominik Drexler, Jendrik Seipp, Hector Geffner

Recently, sketches have been introduced as a general language for representing the subgoal structure of instances drawn from the same domain. Sketches are collections of rules of t…

cs.AI2021

Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches: Extended Version

Dominik Drexler, Jendrik Seipp, Hector Geffner

Width-based planning methods deal with conjunctive goals by decomposing problems into subproblems of low width. Algorithms like SIW thus fail when the goal is not easily serializab…