David Speck
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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: Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning
Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning Open
Automated problem reformulation is a common technique in classical planning to identify and exploit problem structures. Decoupled search is an approach that automatically decomposes planning tasks based on their causal structure, often sig…
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: 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: 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: 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: 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: On Partial Satisfaction Planning with Total-Order HTNs
On Partial Satisfaction Planning with Total-Order HTNs Open
Since its introduction, partial satisfaction planning (PSP), including both oversubscription (OSP) and net-benefit, has received significant attention in the classical planning community. However, hierarchical aspects have been mostly igno…
View article: Data and Code for "On Partial Satisfaction Planning with Total-Order HTNs"
Data and Code for "On Partial Satisfaction Planning with Total-Order HTNs" Open
Domains, problems, generated intermediate problems, runtime data for the paper "On Partial Satisfaction Planning with Total-Order HTNs" (ICAPS 2023)
View article: Data and Code for "On Partial Satisfaction Planning with Total-Order HTNs"
Data and Code for "On Partial Satisfaction Planning with Total-Order HTNs" Open
Domains, problems, generated intermediate problems, runtime data for the paper "On Partial Satisfaction Planning with Total-Order HTNs" (ICAPS 2023)
View article: On Bidirectional Heuristic Search in Classical Planning: An Analysis of BAE*
On Bidirectional Heuristic Search in Classical Planning: An Analysis of BAE* Open
Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search algorithms have been around for a long time, but only recent advances have led to algorithms like BAE* that have the potential to outperform…
View article: Loopless Top-K Planning
Loopless Top-K Planning Open
In top-k planning, the objective is to determine a set of k cheapest plans that provide several good alternatives to choose from. Such a solution set often contains plans that visit at least one state more than once. Depending on the appli…
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…
View article: Symbolic search for optimal planning with expressive extensions
Symbolic search for optimal planning with expressive extensions Open
In classical planning, the goal is to derive a course of actions that allows an intelligent agent to move from any situation it finds itself in to one that satisfies its goals. Classical planning is considered domain-independent, i.e., it …
View article: Trial-Based Heuristic Tree Search for MDPs with Factored Action Spaces
Trial-Based Heuristic Tree Search for MDPs with Factored Action Spaces Open
MDPs with factored action spaces, i.e., where actions are described as assignments to a set of action variables, allow reasoning over action variables instead of action states, yet most algorithms only consider a grounded action representa…
View article: Symbolic Search for Optimal Total-Order HTN Planning
Symbolic Search for Optimal Total-Order HTN Planning Open
Symbolic search has proven to be a useful approach to optimal classical planning. In Hierarchical Task Network (HTN) planning, however, there is little work on optimal planning. One reason for this is that in HTN planning, most algorithms …
View article: Symbolic Search for Oversubscription Planning
Symbolic Search for Oversubscription Planning Open
The objective of optimal oversubscription planning is to find a plan that yields an end state with a maximum utility while keeping plan cost under a certain bound. In practice, the situation occurs whenever a large number of possible, ofte…
View article: Subset-Saturated Transition Cost Partitioning
Subset-Saturated Transition Cost Partitioning Open
Cost partitioning admissibly combines the information from multiple heuristics for optimal state-space search. One of the strongest cost partitioning algorithms is saturated cost partitioning. It considers the heuristics in sequence and as…
View article: On the Compilability and Expressive Power of State-Dependent Action Costs
On the Compilability and Expressive Power of State-Dependent Action Costs Open
While state-dependent action costs are practically relevant and have been studied before, it is still unclear if they are an essential feature of planning tasks. In this paper, we study to what extent state-dependent action costs are an es…
View article: Learning Heuristic Selection with Dynamic Algorithm Configuration
Learning Heuristic Selection with Dynamic Algorithm Configuration Open
A key challenge in satisficing planning is to use multiple heuristics within one heuristic search. An aggregation of multiple heuristic estimates, for example by taking the maximum, has the disadvantage that bad estimates of a single heuri…
View article: When Perfect Is Not Good Enough: On the Search Behaviour of Symbolic Heuristic Search
When Perfect Is Not Good Enough: On the Search Behaviour of Symbolic Heuristic Search Open
Symbolic search has proven to be a competitive approach to cost-optimal planning, as it compactly represents sets of states by symbolic data structures. While heuristics for symbolic search exist, symbolic bidirectional blind search empiri…
View article: Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces"
Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces" Open
This data set contains the code of our planner and of the planner that was used as baseline, the benchmark set that was used to perform experiments as well as the parsed values and basic reports that are reported in the paper. More informa…
View article: Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces"
Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces" Open
This data set contains the code of our planner and of the planner that was used as baseline, the benchmark set that was used to perform experiments as well as the parsed values and basic reports that are reported in the paper. More informa…
View article: Symbolic Top-k Planning
Symbolic Top-k Planning Open
The objective of top-k planning is to determine a set of k different plans with lowest cost for a given planning task. In practice, such a set of best plans can be preferred to a single best plan generated by ordinary optimal planners, as …
View article: Learning to Request Guidance in Emergent Communication
Learning to Request Guidance in Emergent Communication Open
Previous research into agent communication has shown that a pre-trained guide can speed up the learning process of an imitation learning agent. The guide achieves this by providing the agent with discrete messages in an emerged language ab…
View article: Symbolic Planning with Axioms
Symbolic Planning with Axioms Open
Axioms are an extension for classical planning models that allow for modeling complex preconditions and goals exponentially more compactly. Although axioms were introduced in planning more than a decade ago, modern planning techniques rare…
View article: An Analysis of the Probabilistic Track of the IPC 2018
An Analysis of the Probabilistic Track of the IPC 2018 Open
This archive contains data and results used for the paper 'An Analysis of the Probabilistic Track of the IPC 2018'. See the attached README for more details.