Reasoning About Constraint Models

PRICAI 2014: TRENDS IN ARTIFICIAL INTELLIGENCE(2014)

引用 23|浏览172
暂无评分
摘要
We propose a simple but powerful framework for reasoning about properties of models specified in languages like AMPL, OPL, Zinc or Essence. Using this framework, we prove that reasoning problems like detecting symmetries, redundant constraints or dualities between models are undecidable even for a very limited modelling language that only generates simple problem instances. To provide tools to help the human modeller (for example, to identify when a model has a particular symmetry), it would nevertheless be useful to automate many of these reasoning tasks. To explore the possibility of doing this, we describe two case-studies. The first uses the ACL2 inductive prover to prove inductively that a model contains a symmetry. The second identifies a tractable fragment of MiniZinc and uses a decision procedure to prove that a model implies a parameterized constraint.
更多
查看译文
关键词
Modelling Language, Problem Instance, Constraint Satisfaction Problem, Reasoning Task, Redundant Constraint
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要