PrASP Report
원문 초록
This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. PrASP is a research software which integrates non-monotonic reasoning based on Answer Set Programming (ASP), probabilistic inference and parameter learning. In contrast to traditional approaches to Probabilistic (Inductive) Logic Programming, our framework imposes only little restrictions on probabilistic logic programs. In particular, PrASP allows for ASP as well as First-Order Logic syntax, and for the annotation of formulas with point probabilities as well as interval probabilities. A range of widely configurable inference algorithms can be combined in a pipeline-like fashion, in order to cover a variety of use cases.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback
Learning Rules Explaining Interactive Theorem Proving Tactic Prediction
Learning Robust Reward Machines from Noisy Labels
Relational decomposition for program synthesis
AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}
Learning big logical rules by joining small rules
토론
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