The Planning Domain Definition Language (PDDL) is known for its capabilities of symbolic planning in the state space. A solver will find a sequence of steps to bring the system from a start state to the goal state. A common example of this is the monkey-and-banana problem. At first, the monkey sits on the ground and, after doing some actions in the scene, the monkey will have reached the banana.
The way a PDDL planner works is by analyzing the preconditions and effects of each primitive action. This will answer the question of what happens if a certain action is executed.
However, will a PDDL domain description work the other way around as well, not for planning, but for action recognition?
I've searched in the literature to get an answer, but all the papers I've found are describing PDDL only as a planning paradigm.
My idea is to use the given precondition and effects as a parser to identify what the monkey is doing and not what he should do. That means, in the example, the robot ape knows by itself how to reach the banana and the AI system has to monitor the actions. The task is to identify a PDDL action that fits the action by the monkey.