Given the increased importance of physical AI, reliable planning in dynamic environments remains a critical function of embodied systems. In particular, care must be taken in how they interact with situations outside the boundaries of their training. While planning languages such as PDDL or STRIPS tend to be highly problem-dependent, LLMs can be applied to broader and more general tasks in dynamic environments, but remain prone to hallucinations. Logic-based systems, in contrast, provide frameworks for a wide variety of problems and offer justifications for complete and correct plans, but offer no explanation when they cannot generate a plan. For example, VECSR, a planning system based on the s(CASP) answer set programming system, can create plans for unseen tasks by generalizing from a small number of training tasks. However, this generalization is limited to tasks achievable with actions learned during training. To address this limitation, we propose and build the Counterfactual Generation Module, which exploits the justification framework of s(CASP) to identify counterfactuals for unachievable tasks. These counterfactuals allow us to detect missing actions that should be incorporated into VECSR’s knowledge base to enable task completion. We validate our proposal by asking VECSR to generate action plans for 55 tasks outside the 10 it was trained on. For 16 tasks, VECSR could not produce plans, and the counterfactuals “identified” the actions that need to be learned to complete 13 of them. Next, we evaluated 56 plans generated by GPT-4o, observing that the module’s assessment of plan correctness corresponded to human evaluation, supporting that the module itself is correct.