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View article: Finding Minimal Plan Reductions Using Classical Planning
Finding Minimal Plan Reductions Using Classical Planning Open
While classical planning research has made tremendous progress in the last decades, many complex tasks can still only be solved suboptimally. The satisficing plans found for these tasks often contain actions that can be removed while maint…
View article: NL2Plan
NL2Plan Open
NL2Plan is the first fully automatic system for generating complete PDDL tasks from minimal natural language descriptions, a powerful tool for assistive PDDL modeling and a step towards solving natural language planning task with interpret…
View article: Alternation-Based Novelty Search
Alternation-Based Novelty Search Open
One key decision for heuristic search algorithms is how to balance exploration and exploitation. In classical planning, the two strongest approaches for this problem are to alternate between different heuristics and to enhance heuristics w…
View article: Symmetry-Aware Transformer Training for Automated Planning
Symmetry-Aware Transformer Training for Automated Planning Open
While transformers excel in many settings, their application in the field of automated planning is limited. Prior work like PlanGPT, a state-of-the-art decoder-only transformer, struggles with extrapolation from easy to hard planning probl…
View article: Symbolic Search for Cost-Optimal Planning with Expressive Model Extensions
Symbolic Search for Cost-Optimal Planning with Expressive Model Extensions Open
In classical planning, the task is to derive a sequence of deterministic actions that changes the current fully-observable world state into one that satisfies a set of goal criteria. Algorithms for classical planning are domain-independent…
View article: Cost Partitioning for Multiple Sequence Alignment
Cost Partitioning for Multiple Sequence Alignment Open
Multiple Sequence Alignment (MSA) is a fundamental problem in computational biology that is used to understand the evolutionary history of protein, DNA, or RNA sequences. An optimal alignment for two sequences can efficiently be found usin…
View article: Dissecting Scorpion: Ablation Study of an Optimal Classical Planner
Dissecting Scorpion: Ablation Study of an Optimal Classical Planner Open
Currently, one of the predominant approaches for optimal classical planning is A* search with heuristics that partition action costs among several abstractions of the input planning task. One example of this approach is the Scorpion planne…
View article: Expressing and Exploiting Subgoal Structure in Classical Planning Using Sketches
Expressing and Exploiting Subgoal Structure in Classical Planning Using Sketches Open
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 serializable in this way or when some of the subproblems have a high …
View article: Efficiently Computing Transitions in Cartesian Abstractions
Efficiently Computing Transitions in Cartesian Abstractions Open
Counterexample-guided Cartesian abstraction refinement yields strong heuristics for optimal classical planning. The approach iteratively finds a new abstract solution, checks where it fails for the original task and refines the abstraction…
View article: Abstraction Heuristics for Factored Tasks
Abstraction Heuristics for Factored Tasks Open
One of the strongest approaches for optimal classical planning is A* search with heuristics based on abstractions of the planning task. Abstraction heuristics are well studied in planning formalisms without conditional effects such as SAS+…
View article: Versatile Cost Partitioning with Exact Sensitivity Analysis
Versatile Cost Partitioning with Exact Sensitivity Analysis Open
Saturated post-hoc optimization is a powerful method for computing admissible heuristics for optimal classical planning. The approach solves a linear program (LP) for each state encountered during the search, which is computationally deman…
View article: Consolidating LAMA with Best-First Width Search
Consolidating LAMA with Best-First Width Search Open
One key decision for heuristic search algorithms is how to balance exploration and exploitation. In classical planning, novelty search has come out as the most successful approach in this respect. The idea is to favor states that contain p…
View article: The 2023 International Planning Competition
The 2023 International Planning Competition Open
In this article, we present an overview of the 2023 International Planning Competition. It featured five distinct tracks designed to assess cutting‐edge methods and explore the frontiers of planning within these settings: the classical (de…
View article: Sensitivity Analysis for Saturated Post-Hoc Optimization in Classical Planning
Sensitivity Analysis for Saturated Post-Hoc Optimization in Classical Planning Open
Cost partitioning is the foundation of today’s strongest heuristics for optimal classical planning. However, computing a cost partitioning for each evaluated state is prohibitively expensive in practice. Thus, existing approaches make an a…
View article: Cartesian Abstractions and Saturated Cost Partitioning in Probabilistic Planning
Cartesian Abstractions and Saturated Cost Partitioning in Probabilistic Planning Open
Stochastic shortest path problems (SSPs) capture probabilistic planning tasks with the objective of minimizing expected cost until reaching the goal. One of the strongest methods to solve SSPs optimally is heuristic search guided by an adm…
View article: PARIS: Planning Algorithms for Reconfiguring Independent Sets
PARIS: Planning Algorithms for Reconfiguring Independent Sets Open
Combinatorial reconfiguration is the problem of transforming one solution of a combinatorial problem into another, where each transformation may only apply small changes to a solution and may not leave the solution space. An important exam…
View article: Learning Hierarchical Policies by Iteratively Reducing the Width of Sketch Rules
Learning Hierarchical Policies by Iteratively Reducing the Width of Sketch Rules Open
Hierarchical policies are a key ingredient of intelligent behavior, expressing the different levels of abstraction involved in the solution of a problem. Learning hierarchical policies, however, remains a challenge, as no general learning …
View article: Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets"
Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets" Open
The archive chirsten-et-al-ecai2023-solvers contains the code necessary to generate the singularity images used in the experimental evaluation, except for CPLEX which is needed for some PARIS images. The solvers can be built using the buil…
View article: Eliminating Redundant Actions from Plans Using Classical Planning
Eliminating Redundant Actions from Plans Using Classical Planning Open
Even though automated planning is PSPACE-complete in general, satisficing planners are able to solve large planning tasks quickly. However, the found plans are often far from optimal and may even contain actions that can be removed while m…
View article: Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets"
Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets" Open
The archive chirsten-et-al-ecai2023-solvers contains the code necessary to generate the singularity images used in the experimental evaluation, except for CPLEX which is needed for some PARIS images. The solvers can be built using the buil…
View article: Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets"
Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets" Open
The archive chirsten-et-al-ecai2023-solvers contains the code necessary to generate the singularity images used in the experimental evaluation, except for CPLEX which is needed for some PARIS images. The solvers can be built using the buil…
View article: Finding Matrix Multiplication Algorithms with Classical Planning
Finding Matrix Multiplication Algorithms with Classical Planning Open
Matrix multiplication is a fundamental operation of linear algebra, with applications ranging from quantum physics to artificial intelligence. Given its importance, enormous resources have been invested in the search for faster matrix mult…
View article: Learning and Exploiting Progress States in Greedy Best-First Search
Learning and Exploiting Progress States in Greedy Best-First Search Open
Previous work introduced the concept of progress states. After expanding a progress state, a greedy best-first search (GBFS) will only expand states with lower heuristic values. Current methods can identify progress states only for a singl…
View article: Explainable Planner Selection for Classical Planning
Explainable Planner Selection for Classical Planning Open
Since no classical planner consistently outperforms all others, it is important to select a planner that works well for a given classical planning task. The two strongest approaches for planner selection use image and graph convolutional n…
View article: Data for domains without generator for the ICAPS 2021 paper "Automatic Instance Generation for Classical Planning"'
Data for domains without generator for the ICAPS 2021 paper "Automatic Instance Generation for Classical Planning"' Open
This dataset contains raw data (log files) and parsed data (JSON files) of all planners used in the paper run on planning domains for which there is no generator that could directly be used for the Autoscale training process. This dataset …
View article: Data for domains without generator for the ICAPS 2021 paper "Automatic Instance Generation for Classical Planning"'
Data for domains without generator for the ICAPS 2021 paper "Automatic Instance Generation for Classical Planning"' Open
This dataset contains raw data (log files) and parsed data (JSON files) of all planners used in the paper run on planning domains for which there is no generator that could directly be used for the Autoscale training process. This dataset …
View article: Code, Benchmarks and Experiment Data for the ICAPS 2022 HSDIP workshop paper "A Comparison of Abstraction Heuristics for Rubik's Cube"
Code, Benchmarks and Experiment Data for the ICAPS 2022 HSDIP workshop paper "A Comparison of Abstraction Heuristics for Rubik's Cube" Open
This is a collection of code, data, and benchmarks for reproducing all experiments reported in the paper. `buechner-et-al-icaps2022wshsdip-code.zip` contains the implementation, which is based on Scorpion (in turn based on Fast Downward 21…
View article: Best-First Width Search for Lifted Classical Planning
Best-First Width Search for Lifted Classical Planning Open
Lifted planners are useful to solve tasks that are too hard to ground. Still, computing informative lifted heuristics is difficult: directly adapting ground heuristics to the lifted setting is often too expensive, and extracting heuristics…
View article: Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width
Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width Open
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 the form C -> E over a given set of features where C express…
View article: New Refinement Strategies for Cartesian Abstractions
New Refinement Strategies for Cartesian Abstractions Open
Cartesian counterexample-guided abstraction refinement (CEGAR) yields strong heuristics for optimal classical planning. CEGAR repeatedly finds counterexamples, i.e., abstract plans that fail for the concrete task. Although there are usuall…