Learning Residual Alternating Automata
Authors: Sebastian Berndt, Maciej Li_kiewicz, Matthias Lutter, Rdiger Reischuk
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we disprove this conjecture by constructing a counterexample. As our main positive result we design an efficient learning algorithm, named AL , and give a proof that it outputs residual AFAs only. In addition, we investigate the succinctness of these different FA types in more detail. |
| Researcher Affiliation | Academia | Sebastian Berndt, Maciej Li skiewicz, Matthias Lutter, R udiger Reischuk Institute for Theoretical Computer Science, University of L ubeck Ratzeburger Allee 160, 23562 L ubeck, Germany {berndt,liskiewi,lutter,reischuk}@tcs.uni-luebeck.de |
| Pseudocode | Yes | Algorithm 1: AL for the target language L. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper discusses learning algorithms for abstract 'regular languages' but does not specify or provide access information for any concrete, publicly available datasets used for training or evaluation. |
| Dataset Splits | No | The paper generally discusses concepts of training, validation, and test sets in the context of learning algorithms, but does not provide specific dataset split information (percentages, sample counts, or explicit splitting methodology) for any experiments conducted by the authors. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running any experiments. |
| Software Dependencies | No | The paper mentions 'specially designed software tools' but does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, concrete hyperparameter values, training configurations, or system-level settings, as it is primarily a theoretical paper. |