Goal Recognition is the task by which an observer aims to discern the goals that correspond to plans that comply with the perceived behavior of subject agents given as a sequence of observations. Research on Goal Recognition as Planning encompasses reasoning about the model of a planning task, the observations, and the goals using planning techniques, resulting in very efficient recognition approaches. In this article, we design novel recognition approaches that rely on the Operator-Counting framework, proposing new constraints, and analyze their constraints' properties both theoretically and empirically. The Operator-Counting framework is a technique that efficiently computes heuristic estimates of cost-to-goal using Integer/Linear Programming (IP/LP). In the realm of theory, we prove that the new constraints provide lower bounds on the cost of plans that comply with observations. We also provide an extensive empirical evaluation to assess how the new constraints improve the quality of the solution, and we found that they are especially informed in deciding which goals are unlikely to be part of the solution. Our novel recognition approaches have two pivotal advantages: first, they employ new IP/LP constraints for efficiently recognizing goals; second, we show how the new IP/LP constraints can improve the recognition of goals under both partial and noisy observability.

翻译：目标识别是指观察者根据一系列观察到的行为，试图辨别与主体智能体感知行为相一致的计划所对应的目标的任务。作为规划的目标识别研究涉及利用规划技术对规划任务模型、观察结果及目标进行推理，从而形成高效的识别方法。本文设计了基于算子计数框架的新型识别方法，提出了新的约束条件，并从理论与实证角度分析了这些约束的性质。算子计数框架是一种通过整数/线性规划高效计算到达目标的启发式代价估计的技术。在理论层面，我们证明新约束条件为符合观察结果的计划代价提供了下界。我们还通过广泛的实证评估，检验新约束如何提升求解质量，并发现它们在判定哪些目标不太可能成为解的组成部分时尤为有效。我们的新型识别方法有两个关键优势：首先，它采用新的整数/线性规划约束来高效识别目标；其次，我们展示了这些新约束如何在部分可观测及噪声可观测条件下改进目标识别效果。