An evaluation system called the associative rule memory (ARM) that operates with an interactive or automatic planner in a robot-based world, such as the world of the NASA Flight Telerobotic Servicer (FTS), is described. The ARM is constructed from a neural network model called a Boltzmann Machine, and ranks alternative robotic actions based on the probability that the action works as expected in achieving a desired effect. The system is experience-based, and can predict the probability of achieving a desired effect for robotic actions that have not been explicitly tested in the past. The ARM is designed to quickly and efficiently find high probability of effect for robotic actions for a given desired effect. This paper details the construction of the ARM for the NASA FTS robotic environment. Examples are also provided that demonstrate the use of the ARM within a current NASA symbolic planning system.