A Tool for Probabilistic Reasoning Based on Logic Programming and First-Order Theories Under Stable Model Semantics
A Tool for Probabilistic Reasoning Based on Logic Programming and First-Order Theories Under Stable Model Semantics
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稳定模型语义下基于逻辑编程和一阶理论的概率推理工具
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Matthias Nickles
中科院分区：
文献类型：
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作者：
Matthias Nickles
本系统描述论文描述了软件框架 PrASP（“概率答案集编程”）。 PrASP 既是一种不确定性推理和机器学习软件，也是一种基于答案集编程（ASP）的概率逻辑编程语言。除了作为非单调（归纳）概率逻辑编程的研究软件平台外，我们的框架主要针对不确定性流推理领域的应用。 PrASP 程序可以由 ASP (AnsProlog) 以及一阶逻辑公式（具有稳定的模型语义）组成，并用条件或无条件概率或概率区间进行注释。许多替代推理算法允许使系统适应不同的任务特征（例如，是否可以做出独立假设）。
This System Description paper describes the software framework PrASP (“Probabilistic Answer Set Programming”). PrASP is both an uncertainty reasoning and machine learning software and a probabilistic logic programming language based on Answer Set Programming (ASP). Besides serving as a research software platform for non-monotonic (inductive) probabilistic logic programming, our framework mainly targets applications in the area of uncertainty stream reasoning. PrASP programs can consist of ASP (AnsProlog) as well as First-Order Logic formulas (with stable model semantics), annotated with conditional or unconditional probabilities or probability intervals. A number of alternative inference algorithms allow to attune the system to different task characteristics (e.g., whether or not independence assumptions can be made).