In many real-world planning applications, agents might be interested in
finding plans whose actions have costs that are as uniform as possible. Such
plans provide agents with a sense of stability and predictability, which are
key features when humans are the agents executing plans suggested by planning
tools. This paper adapts three uniformity metrics to automated planning, and
introduce planning-based compilations that allow to lexicographically optimize
sum of action costs and action costs uniformity. Experimental results both in
well-known and novel planning benchmarks show that the reformulated tasks can
be effectively solved in practice to generate uniform plans.

在许多实际的规划应用中，代理人可能对寻找其动作成本尽可能均匀的计划感兴趣。本文将三个一致性度量指标应用于自动规划，并引入基于规划的编译方法，允许按词典顺序优化动作成本总和和动作成本的均匀性。在知名和新颖的规划基准实验结果中显示，可以有效地解决重构的任务以产生均匀的计划。

计算具有统一行动成本的计划

On Computing Plans with Uniform Action Costs

Information about action costs is critical for real-world AI planning
applications. Rather than rely solely on declarative action models, recent
approaches also use black-box external action cost estimators, often learned
from data, that are applied during the planning phase. These, however, can be
computationally expensive, and produce uncertain values. In this paper we
suggest a generalization of deterministic planning with action costs that
allows selecting between multiple estimators for action cost, to balance
computation time against bounded estimation uncertainty. This enables a much
richer -- and correspondingly more realistic -- problem representation.
Importantly, it allows planners to bound plan accuracy, thereby increasing
reliability, while reducing unnecessary computational burden, which is critical
for scaling to large problems. We introduce a search algorithm, generalizing
$A^*$, that solves such planning problems, and additional algorithmic
extensions. In addition to theoretical guarantees, extensive experiments show
considerable savings in runtime compared to alternatives.

本文提出了一种带有多个估计器的确定性计划泛化模型，以平衡计算时间和有界估计不确定性，这可以增加计划的准确性，从而增加可靠性，并减少不必要的计算负担，进而应用于大规模问题的研究。通过广泛的实验证明，该算法可以较之其他方法在运行时间上实现显著的节省。

动态估算行为代价的计划

Planning with Dynamically Estimated Action Costs

LAMA is a classical planning system based on heuristic forward search. Its
core feature is the use of a pseudo-heuristic derived from landmarks,
propositional formulas that must be true in every solution of a planning task.
LAMA builds on the Fast Downward planning system, using finite-domain rather
than binary state variables and multi-heuristic search. The latter is employed
to combine the landmark heuristic with a variant of the well-known FF
heuristic. Both heuristics are cost-sensitive, focusing on high-quality
solutions in the case where actions have non-uniform cost. A weighted A* search
is used with iteratively decreasing weights, so that the planner continues to
search for plans of better quality until the search is terminated. LAMA showed
best performance among all planners in the sequential satisficing track of the
International Planning Competition 2008. In this paper we present the system in
detail and investigate which features of LAMA are crucial for its performance.
We present individual results for some of the domains used at the competition,
demonstrating good and bad cases for the techniques implemented in LAMA.
Overall, we find that using landmarks improves performance, whereas the
incorporation of action costs into the heuristic estimators proves not to be
beneficial. We show that in some domains a search that ignores cost solves far
more problems, raising the question of how to deal with action costs more
effectively in the future. The iterated weighted A* search greatly improves
results, and shows synergy effects with the use of landmarks.

LAMA 是一个基于启发式正向搜索的经典规划系统，利用里程碑派生的伪启发式是其核心特征，结合具备成本敏感性的两种启发式可以面对具有非均匀成本的动作，迭代加权 A * 搜索可持续提高搜索质量并表现出与使用里程碑有协同效应；LAMA 在国际计划竞赛 2008 的顺序满足赛道表现最佳，但它的启发式评估中融入动作成本并不具优势，导致在某些情况下，不考虑成本的搜索方法更加成功，这引出了今后应如何更有效地处理行动成本的问题。