Abduction with probabilistic logic programming under the distribution semantics

被引：9
|
作者：
Azzolini, Damiano [1 ]
Bellodi, Elena [2 ]
Ferilli, Stefano [3 ]
Riguzzi, Fabrizio [1 ]
Zese, Riccardo [4 ]
机构：
[1] Univ Ferrara, Dipartimento Matemat & Informat, Via Saragat 1, I-44122 Ferrara, Italy
[2] Univ Ferrara, Dipartimento Ingn, Via Saragat 1, I-44122 Ferrara, Italy
[3] Univ Bari, Dipartimento Informat, Via Orabona 4, I-70125 Bari, Italy
[4] Univ Ferrara, Dipartimento Sci Chim Farmaceut & Agr, Via Luigi Borsari 46, I-44121 Ferrara, Italy
来源：
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2022年 / 142卷
关键词：
Abduction; Distribution semantics; Probabilistic logic programming; Statistical relational artificial intelligence; INFERENCE; LANGUAGE;
D O I：
10.1016/j.ijar.2021.11.003
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In Probabilistic Abductive Logic Programming we are given a probabilistic logic program, a set of abducible facts, and a set of constraints. Inference in probabilistic abductive logic programs aims to find a subset of the abducible facts that is compatible with the constraints and that maximizes the joint probability of the query and the constraints. In this paper, we extend the PITA reasoner with an algorithm to perform abduction on probabilistic abductive logic programs exploiting Binary Decision Diagrams. Tests on several synthetic datasets show the effectiveness of our approach. (C) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页码：41 / 63
页数：23
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