A new artificial intelligence algorithm for logical circuit design using orientation of clusters in multivariate data is developed for decision making in robotic studies. The reliability and risk of decision making based on logical circuit design can be predicted. Clusters in multivariate data is obtained by the method of mixture model clustering based on model selection using the segmentations of heterogeneous variables. The segments of heterogeneous variables forms the number and determines the structures of clusters. The orientations of clusters is used to construct the logical circuit design. The number of all cases for cluster centers determined according to the segmentations of heterogeneous variables. Some of all cases which the assumptions are not satisfied eliminated. The rest cases gives the number of possible cluster centers which the assumptions satisfied. Candidate mixture models are established to determine the number and structures of clusters for possible cluster centers using the partitions of heterogeneous variables. Logical circuit designs are established for possible cluster centers using the orientations of partitions of heterogeneous variables. The best mixture model is chosen among candidate mixture models for data clustering using information criterions. The best mixture model determines the number and the structure of clusters in data. The number of components in the best mixture model corresponds to the number of clusters in data. The components of the best mixture model corresponds to the structure of clusters in multivariate data. Logical circuit design of the best mixture model is used in computations of reliability and risk prediction for decision making in robotic studies.