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 which 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 what happens, if a certain action is executed. But 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 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 doing. 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 which fits to the action by the monkey.
 Wikipedia PDDL, https://en.wikipedia.org/wiki/Planning_Domain_Definition_Language
 Wikipedia Monkey and Banana problem, https://en.wikipedia.org/wiki/Monkey_and_banana_problem
 Yordanova, Kristina, Frank Krüger, and Thomas Kirste. "Context aware approach for activity recognition based on precondition-effect rules." 2012 IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE, 2012.