Weaknesses: My main concerns for the work are about specific assumptions made regarding the agent's planning algorithm and how close the effectiveness of the goal recognition system is tied to having access to the specific planning algorithm and parameters used by the agent generating the observations. I would have liked to see experimental results that at least shows some level of robustness of the system towards mismatch between the planning algorithm used by the goal recognition system and the method used to generate the observations for the study. Below I have provided a more detailed discussion of my main concerns Specific Algorithm Used: The paper makes some specific assumptions on the kind of algorithm that could be used to simulate the bounded decision making. I see no reason to believe that this is general enough to capture behavior of any arbitrary resource bounded decision-maker (for example consider one that is quite similar to the one discussed, but is also memory bounded and can only hold limited possible nodes in its open list) or that this is in anyways similar to how a human would make such decisions (which is important if the primary goal is to be able to predict human goals). While the paper notes that people use heuristics as well, those may be quite different from the ones that are popular in planning literature.

In goal recognition, the basic problem domain consists of the following: Recent advances in robotics and artificial intelligence have brought a variety of assistive robots designed to help humans - a set E of environment fluents; accomplish their goals. However, many have limited autonomy and lack the ability to seamlessly integrate with - a state S that is a value assignment to those fluents; human teams. One capability that can facilitate such humanrobot - a set A of actions that describe potential transitions between teaming is the robot's ability to recognize its teammates' states (with preconditions and effects defined over goals, and react appropriately. This function permits E, and parameterized over a set of environment objects the robot to actively assist the team and avoid performing O); and redundant or counterproductive actions.