Automatic Specialization of Generic Constraint Satisfaction Algorithms

Multi-TAC是一种自动综合启发式程序的学习系统，用于解决组合问题，包括资源分配和调度问题。该项目调查了基础研究领域，将使Multi-TAC用于具有计算挑战性的问题。第一个目标是开发在合成过程中快速搜索Multi-TAC评估的算法空间的方法。特别地，研究重点是基于对示例问题实例的快速测试来预测算法执行情况的方法。第二个目标是使Multi-TAC能够自动合成非常高效的迭代修复算法。这涉及到通用模式的开发，这些模式可以捕获各种此类算法的结构。最后一个目标是使用户能够轻松地为Multi-TAC指定新的问题域，而无需担心其规范的细节如何影响合成过程。特别地，工作集中于重写约束的技术，以便能够有效地评估约束。这个研究项目为解决问题的系统提供了重要的一步，这些系统可以自动为特定的应用配置自己。

Multi-TAC is a learning system that automatically synthesizes heuristic programs for combinatorial problems, including resource allocation and scheduling problems. This project investigates fundamental research areas that will enable Multi-TAC to be used for computationally challenging problems. The first goal is to develop fast methods for searching the space of algorithms evaluated by Multi-TAC during the synthesis process. In particular, the research focuses on methods for predicting how well an algorithm will perform based on a quick test on an example problem instance. The second goal is to enable Multi-TAC to automatically synthesize very efficient iterative repair algorithms. This involves the development of generic schemas that capture the structure of wide variety of such algorithms. The last goal is to enable users to easily specify new problem domains for Multi-TAC, without needing to worry about how the details of their specification impacts the synthesis process. In particular, the work focuses on techniques for rewriting constraints so that they can be efficiently evaluated. This research project provides an important step towards problem- solving systems that can automatically configure themselves for a particular application.