Autonomous agents that operate in the real world must often deal with partial observability, which is commonly modeled as partially observable Markov decision processes (POMDPs). However, traditional POMDP models rely on the assumption of complete knowledge of the observation source, known as fully observable data association. To address this limitation, we propose a planning algorithm that maintains multiple data association hypotheses, represented as a belief mixture, where each component corresponds to a different data association hypothesis. However, this method can lead to an exponential growth in the number of hypotheses, resulting in significant computational overhead. To overcome this challenge, we introduce a pruning-based approach for planning with ambiguous data associations. Our key contribution is to derive bounds between the value function based on the complete set of hypotheses and the value function based on a pruned-subset of the hypotheses, enabling us to establish a trade-off between computational efficiency and performance. We demonstrate how these bounds can both be used to certify any pruning heuristic in retrospect and propose a novel approach to determine which hypotheses to prune in order to ensure a predefined limit on the loss. We evaluate our approach in simulated environments and demonstrate its efficacy in handling multi-modal belief hypotheses with ambiguous data associations.

翻译：在真实世界中运行的自主智能体通常必须应对部分可观测性问题，这类问题常被建模为部分可观测马尔可夫决策过程。然而，传统POMDP模型依赖对观测来源的完全已知假设，即全观测数据关联。为突破这一局限，我们提出一种维护多种数据关联假设的规划算法，该算法以信念混合形式表示，其中每个分量对应不同的数据关联假设。但该方法会导致假设数量指数级增长，从而产生显著计算开销。为应对这一挑战，我们引入了一种基于剪枝的模糊数据关联规划方法。核心贡献在于推导出基于完整假设集的估值函数与基于剪枝子集的估值函数之间的界，从而建立计算效率与性能间的权衡关系。我们证明了这些界既可被用于事后验证任意剪枝启发式方法的有效性，又可提出一种全新方法来确定需剪枝的假设，以保证预定义的性能损失上限。通过在仿真环境中的评估，我们验证了该方法在处理具有模糊数据关联的多模态信念假设时的有效性。