On abstract modular inference systems and solvers

详细信息    查看全文

• 作者：Yuliya Lierler^a ; ^{ylierler@unomaha.edu" class="auth_mail" title="E-mail the corresponding author} ; Miroslaw Truszczynski^b ; ^{mirek@cs.uky.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Knowledge representation ; Model-generation ; Automated reasoning and inference ; SAT solving ; Answer set programming
• 刊名：Artificial Intelligence
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：236
• 期：Complete
• 页码：65-89
• 全文大小：776 K

文摘

Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience, and computational benefits. We introduce the concepts of abstract inference modules and abstract modular inference systems to study general principles behind the design and analysis of model generating programs, or solvers, for integrated multi-logic systems. We show how modules and modular systems give rise to transition graphs, which are a natural and convenient representation of solvers, an idea pioneered by the SAT community. These graphs lend themselves well to extensions that capture such important solver design features as learning. In the paper, we consider two flavors of learning for modular formalisms, local and global. We illustrate our approach by showing how it applies to answer set programming, propositional logic, multi-logic systems based on these two formalisms and, more generally, to satisfiability modulo theories.