In Environment Design, one interested party seeks to affect another agent's decisions by applying changes to the environment. Most research on planning environment (re)design assumes the interested party's objective is to facilitate the recognition of goals and plans, and search over the space of environment modifications to find the minimal set of changes that simplify those tasks and optimise a particular metric. This search space is usually intractable, so existing approaches devise metric-dependent pruning techniques for performing search more efficiently. This results in approaches that are not able to generalise across different objectives and/or metrics. In this paper, we argue that the interested party could have objectives and metrics that are not necessarily related to recognising agents' goals or plans. Thus, to generalise the task of Planning Environment Redesign, we develop a general environment redesign approach that is metric-agnostic and leverages recent research on top-quality planning to efficiently redesign planning environments according to any interested party's objective and metric. Experiments over a set of environment redesign benchmarks show that our general approach outperforms existing approaches when using well-known metrics, such as facilitating the recognition of goals, as well as its effectiveness when solving environment redesign tasks that optimise a novel set of different metrics.

翻译：在环境设计中，某利益相关方试图通过改变环境来影响另一智能体的决策。现有关于规划环境（重）设计的研究大多假设利益相关方的目标是促进目标与计划的识别，并通过在环境修改空间中进行搜索，以找到能简化这些任务并优化特定指标的最小修改集。该搜索空间通常难以处理，因此现有方法开发了基于指标的剪枝技术以提高搜索效率，导致这些方法无法在不同目标和/或指标间泛化。本文认为，利益相关方的目标与指标可能不必然与识别智能体的目标或计划相关。为此，我们提出一种通用的环境重设计方法，该方法与指标无关，并利用近期关于顶级质量规划的研究成果，根据任意利益相关方的目标与指标高效重设计规划环境。在一组环境重设计基准上的实验表明，当使用识别目标等经典指标时，我们的通用方法优于现有方法；且当解决优化一组新型不同指标的环境重设计任务时，该方法同样表现出有效性。