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.

目标识别是观察者根据感知到的主体代理行为的一系列观察结果来识别与计划相对应的目标的任务。本文设计了一种依赖于操作计数框架的新型识别方法，并提出了新的约束条件，从理论和实证上分析了它们的特性。在理论方面，我们证明了新的约束条件能够提供符合观察结果的计划成本的下界。我们还进行了广泛的实证评估，评估了新约束条件如何改善解决方案的质量，并发现它们在决定哪些目标不太可能是解决方案的一部分方面特别有用。我们的新型识别方法具有两个关键优势：第一，它们利用了新的整数 / 线性规划约束来高效识别目标；第二，我们展示了新的整数 / 线性规划约束如何在部分可观测性和噪声可观测性下提高对目标的识别能力。

线性规划通过目标识别

Goal Recognition via Linear Programming

Goal recognition aims to recognize the set of candidate goals that are
compatible with the observed behavior of an agent. In this paper, we develop a
method based on the operator-counting framework that efficiently computes
solutions that satisfy the observations and uses the information generated to
solve goal recognition tasks. Our method reasons explicitly about both partial
and noisy observations: estimating uncertainty for the former, and satisfying
observations given the unreliability of the sensor for the latter. We evaluate
our approach empirically over a large data set, analyzing its components on how
each can impact the quality of the solutions. In general, our approach is
superior to previous methods in terms of agreement ratio, accuracy, and spread.
Finally, our approach paves the way for new research on combinatorial
optimization to solve goal recognition tasks.

本文提出了一种基于算子计数框架的方法来有效计算符合观察结果的解，用于解决目标识别任务，并对部分和噪声观测进行估计和满足观测。通过大规模数据集的实证评估，证明了该方法在一致性比率、准确性和分散性等方面优于以往方法，同时为解决目标识别任务的组合优化开辟了新的研究路径。