A case associative mobile robot planning system (CAMRPS) which integrates memory organization is being developed. The purpose of the CAMRPS is to provide the robot with an environment in which it can think of planning in terms of high level tasks and synthesize such plans rapidly. At all stages of the planning process it can consult the case associative memory (CAM) to see what experience knows of similar plans. Efficient use of prior experiences is emphasized. The CAMRPS remembers and recollects all the cases on the basis of internal similarity between cases. With similarity metric, all old cases are grouped into clusters, which of the same commonality metric in the memory. New cases are self-organized into a new cluster or a pre-existing cluster according to the similarity comparison. Generally speaking, a hierarchical indexing structure on CAMRPS is constructed dynamically and extended as the system gradually accumulates new experiences. The framework of the CAMRPS, hierarchical structure of the CAM, and an illustrated example will be given in the paper.
C. L. Philip Chen,
"Case-associative mobile robot planning system", Proc. SPIE 1707, Applications of Artificial Intelligence X: Knowledge-Based Systems, (1 March 1992); doi: 10.1117/12.56889; https://doi.org/10.1117/12.56889