自动推理:可满足性

Automated Reasoning: satisfiability

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EIT 数字
Coursera
  • 完成时间大约为 13 个小时
  • 中级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

In this course you will learn how to apply satisfiability (SAT/SMT) tools to solve a wide range of problems.

Several basic examples are given to get the flavor of the applications: fitting rectangles to be applied for printing posters, scheduling problems, solving puzzles, and program correctness. Also underlying theory is presented: resolution as a basic approach for propositional satisfiability, the CDCL framework to scale up for big formulas, and the simplex method to deal with linear inequallities.

The light weight approach to following this course is just watching the lectures and do the corresponding quizzes. To get a flavor of the topic this may work out fine. However, the much more interesting approach is to use this as a basis to apply SAT/SMT yourself on several problems, for instance on the problems presented in the honor’s assignment.

课程大纲

SAT/SMT basics, SAT examples

This module introduces SAT (satisfiability) and SMT (SAT modulo theories) from scratch, and gives a number of examples of how to apply SAT.

SMT applications

This module shows a number of applications of satisfiability modulo the theory of linear inequalities (SMT)

Theory and algorithms for CNF-based SAT

This module describes how a rule called Resolution serves to determine whether a propositional formula in conjunctive normal form (CNF) is unsatisfiable. It is shown how an approach called DPLL does the same job, and how it is related to resolution. Finally, it is shown how current SAT solvers essentially implement and optimize DPLL.

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