合理目标排序与强制目标排序及其在规划算法中的应用
On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm
摘要：
本文讨论了计算目标排序的问题，这是人工智能规划中的一个长期存在的问题。它作出了两项新的贡献。首先，它正式定义和讨论了两种不同的目标排序，这是所谓的合理和强制排序。这两种排序都是为简单的ADL运算符以及支持否定和条件效应的更复杂的ADL运算符定义的。这些订单的复杂性进行了调查，并讨论了它们的实际意义。其次，提出了两种计算合理目标排序的方法。其中之一是基于规划图，而其他调查的行动直接。最后，它示出了如何的排序关系，这已经得到了一组给定的目标G，可以用来计算一个所谓的目标议程，划分成一组有序的子目标G。原则上，任何计划者都可以使用目标议程来计划越来越多的子目标。这可以导致指数级的复杂性降低，因为复杂规划问题的解决方案是通过解决更容易的子问题来找到的。由于只有一个多项式的开销是由目标议程计算，一个潜在的存在，以显着加快规划算法，因为我们在实证评估中证明，我们使用这种方法在IPP规划。
英文摘要：
The paper addresses the problem of computing goal orderings, which is one of the longstanding issues in AI planning. It makes two new contributions. First, it formally defines and discusses two different goal orderings, which are called the reasonable and the forced ordering. Both orderings are defined for simple STRIPS operators as well as for more complex ADL operators supporting negation and conditional effects. The complexity of these orderings is investigated and their practical relevance is discussed. Secondly, two different methods to compute reasonable goal orderings are developed. One of them is based on planning graphs, while the other investigates the set of actions directly. Finally, it is shown how the ordering relations, which have been derived for a given set of goals G, can be used to compute a so-called goal agenda that divides G into an ordered set of subgoals. Any planner can then, in principle, use the goal agenda to plan for increasing sets of subgoals. This can lead to an exponential complexity reduction, as the solution to a complex planning problem is found by solving easier subproblems. Since only a polynomial overhead is caused by the goal agenda computation, a potential exists to dramatically speed up planning algorithms as we demonstrate in the empirical evaluation, where we use this method in the IPP planner.
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。
