An examination is made of the fundamental trade-off between exploration and exploitation in a genetic algorithm (GA). An immigration operator is introduced that infuses random members into successive GA populations. It is theorized that immigration maintains much of the exploitation of the GA while increasing exploration. To test this theory, a set of functions that often require the GA to perform an excessive number of evaluations to find the global optimum of the function is designed. For These functions, it is shown experimentally that a GA enhanced with immigration (1) reduces the number of trials that require an excessive number of evaluations and (2) decreases the average number of evaluations needed to find the optimum function.>

A three-tier structure consisting of organization, coordination, and execution levels forms the architecture of an intelligent machine using the principle of increasing precision with decreasing intelligence from a hierarchically intelligent control. This system has been formulated as a probabilistic model, where uncertainty and imprecision can be expressed in terms of entropies. The optimal strategy for decision planning and task execution by the intelligent machine can be found by minimizing the total entropy in the system. The focus is on the design of the organization level of the intelligent machine as a Boltzmann machine, as described in current neural network literature. Since this level is responsible for planning the actions of the machine, the problem at this tier is formulated as the construction of the right sequence of tasks or events that minimizes the entropy for the desired action. The Boltzmann machine is reformulated to use entropy as the cost function to be minimized. Simulated annealing, expanding subinterval random search, and the genetic algorithm are presented as search techniques to efficiently find the desired action sequence. The genetic algorithm is modified to ensure convergence in probability to the minimum entropy of the system. Simulations of these algorithms are shown and performance is analyzed.>

The authors propose a robotic assembly system that is based on the least-commitment principle, in which the successive stages proceeding from planning to execution become progressively more specific. The system is partitioned into an offline automatic programming system and an online execution manager. The framework and objectives are presented, followed by an overview of the global system. The submodules of a system that has actually executed a simple pick/place task are described. Some conclusions and perspectives are outlined.>

To increase task flexibility in a robotic assembly environment, a hierarchical planning and execution system is being developed which will map user-specified, three-dimensional part assembly tasks into various target robotic workcells, and execute these tasks efficiently using manipulators and sensors available in the workcell. One level of this hierarchy, the supervisor, is responsible for assigning subtasks of a system-generated task plan to a set of task specific specialists, and online coordination of the activity of these specialists to accomplish the user-specified assembly. The design of the supervisor is broken down into five major functional blocks: resource management; concurrency detection; task scheduling; error recovery; and interprocess communication. Using concepts from operating systems, the design of each of these supervisor blocks is examined.>