扩展概率答案集编程，支持离散与连续变量联合建模。
Probabilistic Answer Set Programming with Discrete and Continuous Random Variables
- 提出混合概率答案集编程（HPASP），融合离散与连续随机变量。
- 知识编译显著提升精确推理效率，小规模实例可解。
- 采样算法适合大模型，但内存需求可能随规模增长。
在可信语义下的概率答案集编程（PASP）通过伯努利分布的随机事实扩展了答案集编程，以表示不确定性信息。然而，许多现实场景需要同时处理离散与连续随机变量。本文将PASP框架扩展至支持连续变量，提出混合概率答案集编程（HPASP）。我们讨论、实现并评估了两种基于投影答案集枚举和知识编译的精确算法，以及两种基于采样的近似算法。实证结果表明，尽管精确推理仅适用于小规模实例，但知识编译对性能有显著提升；采样方法可处理更大规模实例，但有时需不断增加内存。相关工作正在提交至《理论与实践逻辑编程》（TPLP）。
原文摘要 · Abstract (English)
Probabilistic Answer Set Programming under the credal semantics (PASP) extends Answer Set Programming with probabilistic facts that represent uncertain information. The probabilistic facts are discrete with Bernoulli distributions. However, several real-world scenarios require a combination of both discrete and continuous random variables. In this paper, we extend the PASP framework to support continuous random variables and propose Hybrid Probabilistic Answer Set Programming (HPASP). Moreover, we discuss, implement, and assess the performance of two exact algorithms based on projected answer set enumeration and knowledge compilation and two approximate algorithms based on sampling. Empirical results, also in line with known theoretical results, show that exact inference is feasible only for small instances, but knowledge compilation has a huge positive impact on the performance. Sampling allows handling larger instances, but sometimes requires an increasing amount of memory. Under consideration in Theory and Practice of Logic Programming (TPLP).
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