Recent work on plan recognition as planning has shown great promise in the use of a domain theory and general planning algorithms for the plan recognition problem. In this paper, we propose to extend previous work to (1) address observations over fluents, (2) better address unreliable observations (i.e., noisy or missing observations), and (3) recognize plans in addition to goals. To this end, we introduce a relaxation of the plan-recognitionas- planning formulation that allows unreliable observations. That is, in addition to the original costs of the plan, we define two objectives that account for missing and noisy observations, and optimize for a linear combination of all objectives. We approximate the posterior probabilities of generated plans by taking into account the combined costs that include penalties for missing or noisy observations, and normalizing over a sample set of plans generated by finding either diverse or high-quality plans. Our experiments show that this approach improves goal recognition in most domains when observations are unreliable. In addition, we evaluate plan recognition performance and show that the high-quality plan generation approach should be preferred in most domains.