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31#
發(fā)表于 2025-3-26 23:55:33 | 只看該作者
Molecules, Languages and Automata approach that has even been extended to the much more complex structures of proteins. Processive enzymes and other “molecular machines” can also be cast in terms of automata. This paper briefly reviews linguistic approaches to molecular biology, and provides perspectives on potential future applications of grammars and automata in this field.
32#
發(fā)表于 2025-3-27 01:36:47 | 只看該作者
Inferring Regular Trace Languages from Positive and Negative Sampleson. After presenting the algorithm we provide a proof of its convergence (which is more complicated than the proof of convergence of the RPNI because there are no Minimal Automata for Asynchronous Automata), and we discuss the complexity of the algorithm.
33#
發(fā)表于 2025-3-27 08:24:39 | 只看該作者
34#
發(fā)表于 2025-3-27 12:01:20 | 只看該作者
35#
發(fā)表于 2025-3-27 15:25:49 | 只看該作者
36#
發(fā)表于 2025-3-27 21:20:55 | 只看該作者
37#
發(fā)表于 2025-3-28 01:02:22 | 只看該作者
Using Grammar Induction to Model Adaptive Behavior of Networks of Collaborative Agentss. We have implemented our theoretical framework in a workbench that can be used to run simulations. We discuss some results of these simulations. We believe that this approach provides a viable framework to study processes of self-organization and optimization of collaborative agent networks.
38#
發(fā)表于 2025-3-28 04:50:33 | 只看該作者
Grammatical Inference: Theoretical Results and Applications
39#
發(fā)表于 2025-3-28 07:15:54 | 只看該作者
https://doi.org/10.1007/978-3-322-86678-3niques such as Binary Feature Grammars and Distributional Lattice Grammars. The class of .s that can be learned in this way includes inherently ambiguous and thus non-deterministic languages; this approach therefore breaks through an important barrier in . inference.
40#
發(fā)表于 2025-3-28 11:53:38 | 只看該作者
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