Linear Algebraic Computation of Propositional Horn Abduction
Linear Algebraic Computation of Propositional Horn Abduction
复制标题
命题角外延的线性代数计算
DOI：
10.1109/ictai52525.2021.00040
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Chiaki Sakama
中科院分区：
文献类型：
--
作者：
Tuan Nguyen Quoc;Katsumi Inoue;Chiaki Sakama
逻辑程序的线性代数表征已被研究用于在大规模知识库中进行逻辑推理，并取得了令人鼓舞的结果。本文利用程序矩阵的转置，进一步扩展了溯因推理中的线性代数表征。然后，我们提出了一种高效的穷举搜索策略，该策略将数值计算的灵活性和鲁棒性与集合运算的紧凑性和效率相结合，用于计算溯因性Horn命题任务的解。实验结果表明，我们的方法与冲突驱动技术相比具有竞争力，并且具有在并行计算平台上加速的潜力。
Linear algebraic characterization of logic programs has been investigated to perform logical inference in large-scale knowledge bases and has gained encouraging results. In this paper, we further extend the linear algebraic characterization in abductive reasoning by exploiting the transpose of the program matrix. Then we propose an efficient exhaustive search strategy, which combines the flexibility and robustness of numerical computation with the compactness and efficiency of set operations, in order to compute solutions of abductive Horn propositional tasks. Experimental results demonstrate that our method is competitive with conflict-driven techniques and has the potential to speed up on parallel computing platforms.