Efficient Commands Research Articles

Connectionist approaches to the control of manipulation robots at the executive hierarchical level: An overview

One of the most interesting and important properties of connectionist systems is their ability to control sophisticated manipulation robots, i.e. to produce a large number of efficient control commands in real-time. This paper represents an attempt to give a comprehensive report of the basic principles and concepts of connectionism in robotics, with an outline of a number of recent algorithms used in learning control of a manipulation robot. A major concern in this paper is the application of neural networks for off-line and on-line learning of kinematic and dynamic relations used in robot control at the executive hierarchical level.

Using neural net modeling for user assistance in HCI tasks

In the UNIX operating system, many complex operations can be done using a single command line, in the most efficient method, or they can be done using several command lines of simple commands, in a less efficient method. Recognizing when and how to use these efficient commands is difficult for novice users and for many experts. Five UNIX‐based tasks were constructed that could be done using many simple commands, or they could be performed using one or two more complex and efficient commands. Subjects were asked to perform these tasks using the most efficient methods they could. Many different command sequences were generated from these subjects. These data were then used in a neural net model to map the commands to message markers for task assistance. An experiment was devised to test how well users could utilize the mappings of inefficient commands to help messages from the neural net. In the neural net assisted condition, subjects received assistance for the most efficient command whenever the neural ne...

Control of manipulators by neural networks

One of the most intriguing properties of natural neural systems is their ability to control exceedingly sophisticated manipulators like arms, legs or trusses, i.e. to produce a large number of efficient motor commands in real time. This remarkable capability of neural systems is based on parallel information processing. The concepts of parallel information processing are not very well understood, mainly because they differ fundamentally from the algorithmic concepts of sequential information processing prevalent in contemporary computers.