Reasoning about action II
Matthew L. Ginsberg, David Edward Smith · Artificial Intelligence · 1988
We present a computationally effective approach to representing and reasoning about actions with many qualifications. The approach involves treating actions as qualified not by specific facts that may or may not hold when the action is executed, but instead as potentially qualified by general constraints describing the domain being investigated. Specifically, we suggest that the result of the action be computed without considering these qualifying domain constraints, and take the action to be qualified if and only if any of the constraints is violated after the computation is complete. Our approach is presented using the framework developed in [6], where we discussed a solution to the frame and ramification problems based on the notion of possible worlds, and compared the computational requirements of that solution to the needs of more conventional ones. In the present paper, we show that the domain constraint approach to qualification, coupled with the possible worlds approach described earlier, has the remarkable property that essentially no computational resources are required to confirm that an action is unqualified. As before, we also make a quantitative comparison between the resources needed by our approach and those required by other formulations.