Generalising Planning Environment Redesign
Alberto Pozanco, Ramon Fraga Pereira, Daniel Borrajo
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext c2c3a47a-bf96-4c8e-9367-dee016cdc672Builds on3
- Top-Quality Planning: Finding Practically Useful Sets of Best PlansMichael Katz, Shirin Sohrabi, Octavian UdreaAAAI 2020 · 36 citations
- Active Goal RecognitionMaayan Shvo, Sheila A. McIlraithAAAI 2020 · 9 citations
- Stochastic Goal Recognition Design Problems with Suboptimal AgentsChristabel Wayllace, William YeohAAAI 2022 · 3 citations
Related papers
- Block-Level Goal Recognition DesignTsz-Chiu AuAAAI 2024 · 1 citation
- Information Shaping for Enhanced Goal Recognition of Partially-Informed AgentsSarah Keren, Haifeng Xu, Kofi Kwapong, David C. Parkes et al.AAAI 2020 · 14 citations
- Extended Goal Recognition Design with First-Order Computation Tree LogicTsz-Chiu AuAAAI 2022 · 2 citations
- Marginal Benefit Driven RL Teacher for Unsupervised Environment DesignDexun Li, Wenjun Li, Pradeep VarakanthamAAAI 2025 · 1 citation
- Reducing Goal State Divergence with Environment DesignKelsey Sikes, Sarah Keren, Sarath SreedharanAAAI 2026
