On the Gains from Using Action Observations in Domain Repair

Designing a PDDL planning domain is an error-prone task, which can result in unsolvable planning tasks or unexpected plans. Existing domain repair methods either rely on a complete plan to identify unsatisfied preconditions or operate without any input plan by compiling the flawed planning task into a new planning task with self-repair actions. In contrast, learning approaches often benefit from a range of input observations to infer domain models. In this paper, we extend the self-repair compilation to also accept as input a variable number of action observations. Experimental results show improved domain repair quality and generally strong performance compared to previous domain repair and learning methods.