Qualification Problem
The Qualification Problem is a core problem in the field of artificial intelligence in terms of knowledge representation and action reasoning. It focuses on how to determine all the conditions or factors required for an action or event to be successfully executed in a changing environment. This problem involves identifying and dealing with various obstacles that may prevent the expected results from occurring.
It was first pointed out by John McCarthy in 1977. Together with the Frame Problem and the Ramification Problem, it constitutes the three basic problems in formal action theory. The Frame Problem focuses on how to determine what things remain unchanged after an action occurs, while the Ramification Problem focuses on the indirect effects that an action may have.
In practical applications, the Qualification Problem manifests itself as the problem of how to ensure that an action can achieve the expected effect in a specific situation. For example, an AI system for a self-driving car may have learned traffic signs and lights in the training data set, but if it encounters a traffic controller holding a signal flag or a police officer in an emergency, the system may not be able to respond correctly because these situations are not included in its training data.
One way to solve the Qualification Problem is to use a logic programming approach, such as in the Flux action programming language, which handles the basic Frame Problem through a solution built on Fluent Calculus. Flux systems allow planning under the default assumption that actions will succeed as usual, and are able to reason about these assumptions in order to recover from unexpected action failures.
Furthermore, the solution of the Qualification Problem involves the use of non-monotonic reasoning methods that allow for the consideration of possible but not explicitly listed antecedents given the expected effects of an action. This can be achieved by allowing event axioms to be refutable, i.e., if all declarative antecedents of the event are met, then it can be refuted to conclude that the event will have its predicted effect.