The distribution semantics is one of the most prominent approaches for the
combination of logic programming and probability theory. Many languages follow
this semantics, such as Independent Choice Logic, PRISM, pD, Logic Programs
with Annotated Disjunctions (LPADs) and ProbLog. When a program contains
functions symbols, the distribution semantics is well-defined only if the set
of explanations for a query is finite and so is each explanation.
Well-definedness is usually either explicitly imposed or is achieved by
severely limiting the class of allowed programs. In this paper we identify a
larger class of programs for which the semantics is well-defined together with
an efficient procedure for computing the probability of queries. Since LPADs
offer the most general syntax, we present our results for them, but our results
are applicable to all languages under the distribution semantics. We present
the algorithm "Probabilistic Inference with Tabling and Answer subsumption"
(PITA) that computes the probability of queries by transforming a probabilistic
program into a normal program and then applying SLG resolution with answer
subsumption. PITA has been implemented in XSB and tested on six domains: two
with function symbols and four without. The execution times are compared with
those of ProbLog, cplint and CVE, PITA was almost always able to solve larger
problems in a shorter time, on domains with and without function symbols.

探讨了分布语义的适用范围和计算查询概率的有效性，提出了用于将概率程序转换为普通程序，并应用 SLG 分辨率和答案包含进行计算的算法 “基于表和答案包 计算的概率推理（PITA）” 来计算查询的概率。