Generalising Planning Environment Redesign
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 36 references that are in the map. First posted on arXiv 12 Feb 2024.
Builds on
4 of 36 references.
References · 4
- 2018Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behaviorcited by 30
- 2019Active Goal Recognitioncited by 16
- 2018A Unified Framework for Planning in Adversarial and Cooperative Environmentscited by 15
- 2020Designing Environments Conducive to Interpretable Robot Behaviorcited by 8
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Citing papers per year: 2024: 1.
1 paper.