提出多计划生成方法，消除规划器偏差，提升目标识别评估真实性。
Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation
- 用top-k规划生成同一目标的多种不同计划，打破原有数据集偏差
- 引入版本覆盖得分（VCS）量化识别器在不同计划下的鲁棒性
- 发现现有先进模型在观测受限时性能显著下降，适合评估多规划场景
自主代理需具备目标与规划识别能力以在多智能体环境中交互。然而，现有目标识别数据集均受生成系统（基于启发式前向搜索）的系统性偏差影响，导致数据集缺乏真实场景挑战性（如不同规划器使用相同目标），从而影响目标识别器在不同规划器条件下的评估效果。本文提出一种新方法，利用top-k规划为同一目标假设生成多个不同计划，构建可缓解偏差的新基准。该方法支持引入版本覆盖得分（VCS）来衡量目标识别器在不同计划集合下的鲁棒性。实验表明，当前最先进目标识别器在低可观测性条件下鲁棒性显著下降。
原文摘要 · Abstract (English)
Autonomous agents require some form of goal and plan recognition to interact in multiagent settings. Unfortunately, all existing goal recognition datasets suffer from a systematical bias induced by the planning systems that generated them, namely heuristic-based forward search. This means that existing datasets lack enough challenge for more realistic scenarios (e.g., agents using different planners), which impacts the evaluation of goal recognisers with respect to using different planners for the same goal. In this paper, we propose a new method that uses top-k planning to generate multiple, different, plans for the same goal hypothesis, yielding benchmarks that mitigate the bias found in the current dataset. This allows us to introduce a new metric called Version Coverage Score (VCS) to measure the resilience of the goal recogniser when inferring a goal based on different sets of plans. Our results show that the resilience of the current state-of-the-art goal recogniser degrades substantially under low observability settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。