Automatic Learning of Admissible Heuristic Evaluation Functions

这个项目的目标是开发一套健壮的算法，用于自动学习允许的启发式算法-即识别组合问题的最优解并给出解的实际成本下限的函数。启发式函数越准确，找到最优解的效率就越高，或者如果不需要最优解，找到成本更低的解就越快。这项研究正在开发一种方法，通过应用一种技术来以一种与领域无关的方式学习这些启发式函数，该技术已被证明在自动学习更准确的可接受启发式函数方面很有用，这些启发式函数需要更多的时间来计算，但可以用它们为使用启发式算法节省的时间来弥补。这项工作将为人工智能的组合问题提供更好的解决方案，随着计算机速度的提高和更大问题的提出，组合问题的成本效益将越来越高。

The objective of this project is to develop a robust set of algorithms for automatically learning admissible heuristics - that is, functions that identify optimal solutions to combinatorial problems and give a lower bound on the actual cost of a solution. The more accurate the heuristic function, the more efficiently an optimal solution can be found, or if an optimal solution is not required the more quickly a lower-cost solution can found. This research is developing ways to learn these heuristics in a domain-independent manner by applying a technique that has proved useful in automatically learning more accurate admissible heuristic functions that take more time to compute, but make up for it in the time they save for the algorithm using the heuristic. This work will provide better solutions of combinatorial problems of artificial intelligence which will be increasingly cost-effective as computers get faster and larger problems are presented for solution.