  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.
