A sustainable production of high-quality agricultural products calls for personalized- rather than for massive- operations. The aforementioned (personalized) operations can be pursued by human-like reasoning applicable per case. The interest here is in agricultural grape robot harvest where a binary decision needs to be taken, given a set of ambiguous constraints represented by a Boolean lattice ontology of inequalities. Fuzzy lattice reasoning (FLR) is employed for decision making. Preliminary experimental results on expert data demonstrate the advantages of the proposed method including parametrically tunable, rule-based decision-making involving, in principle, either crisp or ambiguous measurements also beyond rule support; combinatorial decision-making is also feasible.


V. G. Kaburlasos, C. Lytridis, G. Siavalas, T. Pachidis, S. Theocharis, Fuzzy lattice reasoning (FLR) for decision-making on an ontology of constraints toward agricultural robot harvest”, In: Qinglin Sun, Jie Lu, Xianyi Zeng, Etienne E. Kerre, Tianrui Li (Eds.), Proceedings of the 15th International FLINS (Fuzzy Logic and Intelligent Technologies in Nuclear Science) Conference (FLINS 2022) on Machine Learning, Multi Agent and Cyber Physical Systems, Tianjin, China, 26-28 August 2022. World Scientific Proceedings Series on Computer Engineering and Information Science 13, pp. 80-87. (Best Paper Award). https://www.worldscientific.com/worldscibooks/10.1142/13231#t=toc