Automatic Learning of Admissible Heuristic Evaluation Functions

该项目的目标是开发一套强大的算法，用于自动学习可接受的算法， 在本发明中， 功能 确定最佳的 解决方案 组合问题，并给出了一个解决方案的实际成本的下限。 启发式函数越精确，可以越有效地找到最优解，或者，如果不需要最佳解决方案，则可以更快地找到成本更低的解决方案。这项研究正在开发在某个领域学习这些知识的方法-独立的方式，通过应用技术，已被证明是有用的，在自动学习更准确的容许启发式函数，需要更多的时间来计算，但用他们节省的时间来弥补 算法的启发式。 这项工作将提供更好的解决人工智能的组合问题，这将是越来越符合成本效益的计算机变得更快，更大的问题提出解决方案。

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.