Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation

TL;DR

This paper introduces a new dataset generation method using top-k planning to create diverse plan sets for goal recognition benchmarks, reducing bias and improving evaluation of recognisers' robustness.

Contribution

It proposes a novel multi-plan dataset generation approach and a new metric, VCS, to assess goal recogniser resilience against plan diversity and bias.

Findings

State-of-the-art goal recogniser performance degrades with low observability.

Generated datasets reduce bias caused by planning system limitations.

VCS effectively measures recogniser resilience across diverse plan sets.

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 settings.