Dear NAI Journal Editors, We wish to submit an original research article entitled “Metrizable symbolic data structure for ill-defined problem solving” for consideration by NAI. We confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. In this paper, we develop an approach that allows us to specify data and processes at a symbolic level in such a way that it is embedded in a metric space equipped with all operations required to apply usual algorithms (e.g., clustering or reinforcement learning mechanisms) on the data structure itself, without the requirement construct numerical vectors for each symbol. It thus seems appropriate for publication because it provides a tool allowing complete integration of the symbolic and numerical levels, based on apriori knowledge instead of arbitrary or data-driven embedding. This may be significant because it allows us to directly use explicable and interpretable knowledge when designing neurosymbolic mechanism If this contribution is relevant for NAI, a second paper will be submitted showing applications regarding ill-porblem solving and symbolic reinforcement learning. We have no conflicts of interest to disclose. Thank you for your consideration of this manuscript. Sincerely,