故意 发表于 2025-3-30 10:12:22

Enhancing Logical Tensor Networks: Integrating Uninorm-Based Fuzzy Operators for Complex Reasoningway for more sophisticated artificial intelligence systems. This work lays a foundational stone for future research in the intersection of fuzzy logic and neural-symbolic computing, suggesting directions for further exploration and integration of fuzzy systems elements into Logic Tensor Networks.. h

grounded 发表于 2025-3-30 13:14:18

Parameter Learning Using Approximate Model Countingsing approximation allows more complex queries to be compiled and our experiments show that their addition helps reduce the training loss. However, we observe that there is a limit to the addition of partial circuits after which there is no more improvement.

最初 发表于 2025-3-30 19:10:13

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FEAS 发表于 2025-3-30 23:28:19

Concept Induction Using LLMs: A User Experiment for Assessmentavailable in the data via prompting to facilitate this process. To evaluate the output, we compare the concepts generated by the LLM with two other methods: concepts generated by humans and the ECII heuristic concept induction system. Since there is no established metric to determine the human under

Yourself 发表于 2025-3-31 03:49:16

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Inveterate 发表于 2025-3-31 07:22:17

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按时间顺序 发表于 2025-3-31 10:48:12

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桶去微染 发表于 2025-3-31 16:26:05

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训诫 发表于 2025-3-31 21:12:43

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向前变椭圆 发表于 2025-3-31 23:05:32

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查看完整版本: Titlebook: Neural-Symbolic Learning and Reasoning; 18th International C Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn Conference proceedings 2024