  Thus, a related issue in AI has been dubbed the computational aspect of the frame problem (McDermott 1987). This is the question of how to compute the consequences of an action without the computation having to range over the action's non-effects. The solution to the computational aspect of the frame problem adopted in most symbolic AI programs is some variant of what McDermott calls the “sleeping dog” strategy (McDermott 1987). The idea here is that not every part of the data structure representing an ongoing situation needs to be examined when it is updated to reflect a change in the world. Rather, those parts that represent facets of the world that have changed are modified, and the rest is simply left as it is (following the dictum “let sleeping dogs lie”). In our example of the robot and the tea-cup, we might apply the sleeping dog strategy by having the robot update its beliefs about the location of the cup and the contents of the cupboard. But the robot would not worry about some possible spoon that may or may not be on or in the cup, since the robot's goal did not directly involve any spoon.  