Optimization Methods for Logical Inference: Chandru/Optimization
Optimization Methods for Logical Inference: Chandru/Optimization
复制标题
逻辑推理的优化方法：Chandru/Optimization
DOI：
10.1002/9781118033166
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
J. Hooker
中科院分区：
文献类型：
--
作者：
V. Chandru;J. Hooker
在演绎推理中融合逻辑和数学：一种创新的前沿方法。逻辑推理的优化方法当然，维贾伊·鲁和约翰·胡克说，他们是这一迅速发展的领域的两个主要贡献者。而且即使”用优化方法解决逻辑推理问题可能看起来有点像用筷子吃酸菜.是问题的数学结构决定了优化模型是否可以帮助解决它，而不是问题发生的上下文。通过为逻辑推理问题提供强大的、经过验证的优化技术，Alberru和Hooker展示了优化模型不仅可以用于解决人工智能和数学编程中的问题，而且在一般复杂系统中也有巨大的应用。他们调查最近的研究，从过去十年的逻辑/优化接口，结合自己的一些结果，并强调逻辑类型最容易接受的优化方法命题逻辑，一阶谓词逻辑，概率和相关逻辑，逻辑，联合收割机证据，如Dempster-Shafer理论，规则系统的置信因子，和约束逻辑编程系统。不需要逻辑背景，并从零开始清楚地解释所有主题，逻辑推理的优化方法是不同领域的科学家和学生的宝贵指南，包括运筹学，计算机科学，人工智能，决策支持系统和工程。
Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though" solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks... it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs." Presenting powerful, proven optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering.