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[2602.14691] Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation
Summary
This paper presents a method to eliminate planner bias in goal recognition using multi-plan dataset generation, enhancing the evaluation of goal recognizers in AI systems.
Why It Matters
Goal recognition is crucial for autonomous agents in multi-agent environments. Current datasets are biased due to the planning systems used to generate them, limiting their effectiveness. This research introduces a new approach that improves dataset diversity and evaluation metrics, which is essential for advancing AI capabilities in real-world scenarios.
Key Takeaways
- Existing goal recognition datasets are biased due to heuristic-based planning systems.
- The proposed method generates multiple plans for the same goal, reducing bias.
- A new metric, Version Coverage Score (VCS), is introduced for better evaluation of goal recognizers.
- The resilience of state-of-the-art goal recognizers decreases significantly under low observability.
- This research contributes to more realistic benchmarks for evaluating AI systems.
Computer Science > Artificial Intelligence arXiv:2602.14691 (cs) [Submitted on 16 Feb 2026] Title:Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation Authors:Mustafa F. Abdelwahed, Felipe Meneguzzi Kin Max Piamolini Gusmao, Joan Espasa View a PDF of the paper titled Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation, by Mustafa F. Abdelwahed and 2 other authors View PDF HTML (experimental) Abstract: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 sett...
