Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Generalized Planning: Non-Deterministic Abstractions and Trajectory Constraints
Authors: Blai Bonet, Giuseppe De Giacomo, Hector Geffner, Sasha Rubin
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the characterization and computation of general policies for families of problems that share a structure characterized by a common reduction into a single abstract problem. and We show how such global structure can be captured by means of trajectory constraints that in many cases can be expressed as LTL formulas, thus reducing generalized planning to LTL synthesis. and Theorem 10. Let P o/C be the observation projection with trajectory constraint C expressed as the LTL formula ฮจ. Then solving P o/C (and hence all P/C with P P) is 2EXPTIME-complete. In particular, it is double-exponential in |ฮจ| + |T o| and polynomial in |P o|. |
| Researcher Affiliation | Academia | Blai Bonet Univ. Sim on Bol ฤฑvar Caracas, Venezuela EMAIL Giuseppe De Giacomo Sapienza Univ. Roma Rome, Italy EMAIL Hector Geffner ICREA Univ. Pompeu Fabra Barcelona, Spain EMAIL Sasha Rubin Univ. Federico II Naples, Italy EMAIL |
| Pseudocode | No | The paper describes methods conceptually and includes figures like a DPW diagram, but it does not contain any blocks explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or any links to a code repository for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and uses illustrative examples, but it does not mention or provide access information for any publicly available or open dataset used for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no training/test/validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments, thus no specific hardware details are provided. |
| Software Dependencies | No | The paper discusses theoretical concepts like 'LTL synthesis' and 'FOND planners' but does not specify any particular software, libraries, or their version numbers needed for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no specific experimental setup details, hyperparameters, or training configurations are provided. |