论文标题
使用约束编程生成随机逻辑程序
Generating Random Logic Programs Using Constraint Programming
论文作者
论文摘要
在广泛的问题实例上测试算法对于确保对一种算法优越的任何主张的有效性至关重要。但是，当涉及概率逻辑程序的推理算法时，实验评估仅限于几个程序。现有的生成随机逻辑程序的方法仅限于命题程序，并且经常施加严格的句法限制。我们提出了一种使用约束编程生成随机逻辑程序和随机概率逻辑程序的新方法，引入了一种新的约束来控制潜在概率分布的独立性结构。我们还为模型的正确性提供了一个组合参数，显示模型如何用参数值缩放，并使用模型比较综合问题范围内的概率推理算法。我们的模型允许推理算法开发人员评估和比较各种实例的算法，从而提供了其（比较）优势和缺点的详细图片。
Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions. We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint to control the independence structure of the underlying probability distribution. We also provide a combinatorial argument for the correctness of the model, show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems. Our model allows inference algorithm developers to evaluate and compare the algorithms across a wide range of instances, providing a detailed picture of their (comparative) strengths and weaknesses.
