Guarantees for Sound Abstractions for Generalized Planning
Authors: Blai Bonet, Raquel Fuentetaja, Yolanda E-Martín, Daniel Borrajo
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we address this limitation by performing an analysis of the abstraction with respect to the collection, and show how to obtain formal guarantees for generalization. These guarantees, in the form of first-order formulas, may be used to 1) define subcollections of instances on which the abstraction is guaranteed to be sound, 2) obtain necessary conditions for generalization under certain assumptions, and 3) do automated synthesis of complex invariants for planning problems. Our contributions are the following: 1) a crisp theoretical foundation for the synthesis of formulas only using as input the relational planning domain and the abstraction... |
| Researcher Affiliation | Academia | Blai Bonet1,2 , Raquel Fuentetaja2 , Yolanda E-Mart ın2 and Daniel Borrajo2 1Universidad Sim on Bol ıvar, Venezuela 2Universidad Carlos III de Madrid, Spain |
| Pseudocode | No | The paper describes algorithms and formulas in text and mathematical notation but does not contain a dedicated 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | No concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) is provided for the methodology described in this paper. |
| Open Datasets | No | The paper does not mention using a dataset for training or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running any computations are provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings). |