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Computer simulation of human motions helps test hypotheses on human motion planning and fosters timely and high-quality human-machine/environment interaction design. The current study introduces a novel simulation approach termed memory-based motion simulation (MBMS), and presents its key element "motion modification" (MoM) algorithm. The proposed approach implements a computational model inspired by the generalized motor program (GMP) theory. Operationally, when a novel motion scenario is submitted to the MBMS system, its motion database is searched to find relevant existing motions. The selected motions, referred to as "root motions", most likely do not meet exactly the novel motion scenario, and therefore, they need to be modified by the MoM algorithm. This algorithm derives a parametric representation of possible variants of a root motion in a GMP-like manner, and adjusts the parameter values such that the new modified motion satisfies the novel motion scenario, while retaining the root motion's overall angular movement pattern and inter-joint coordination. An evaluation of the prediction capability of the algorithm, using both seated upper body reaching and whole-body load-transfer motions, indicated that the algorithm can accurately predict various human motions with errors comparable to the inherent variability in human motions when repeated under identical task conditions.