通过延迟仿真减少内存
Memory Reduction via Delayed Simulation
摘要：
我们解决了无限游戏理论中的一个中心（和经典）问题：减少在常规无限游戏中实现获胜策略所需的内存大小（即，当规范是规则Omega语言时，确保针对环境动作的正确行为的控制器）。我们提出了一种方法，攻击这个问题之前，建设的战略，首先减少游戏图，是从规范。对于由“请求-响应”要求和一般“公平”条件表示的规范的情况下，我们表明，内存大小的指数增益是可能的。
英文摘要：
We address a central (and classical) issue in the theory of infinite games: the reduction of the memory size that is needed to implement winning strategies in regular infinite games (i.e., controllers that ensure correct behavior against actions of the environment, when the specification is a regular omega-language). We propose an approach which attacks this problem before the construction of a strategy, by first reducing the game graph that is obtained from the specification. For the cases of specifications represented by "request-response"-requirements and general "fairness" conditions, we show that an exponential gain in the size of memory is possible.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
