Generalising Planning Environment Redesign
Alberto Pozanco, Ramon Fraga Pereira, Daniel Borrajo
摘要
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper3
- Top-Quality Planning: Finding Practically Useful Sets of Best PlansMichael Katz, Shirin Sohrabi, Octavian UdreaAAAI 2020 · 被引用 36 次
- Active Goal RecognitionMaayan Shvo, Sheila A. McIlraithAAAI 2020 · 被引用 9 次
- Stochastic Goal Recognition Design Problems with Suboptimal AgentsChristabel Wayllace, William YeohAAAI 2022 · 被引用 3 次
相关 Paper
- Block-Level Goal Recognition DesignTsz-Chiu AuAAAI 2024 · 被引用 1 次
- Information Shaping for Enhanced Goal Recognition of Partially-Informed AgentsSarah Keren, Haifeng Xu, Kofi Kwapong, David C. Parkes 等AAAI 2020 · 被引用 14 次
- Extended Goal Recognition Design with First-Order Computation Tree LogicTsz-Chiu AuAAAI 2022 · 被引用 2 次
- Marginal Benefit Driven RL Teacher for Unsupervised Environment DesignDexun Li, Wenjun Li, Pradeep VarakanthamAAAI 2025 · 被引用 1 次
- Reducing Goal State Divergence with Environment DesignKelsey Sikes, Sarah Keren, Sarath SreedharanAAAI 2026
