Other

Gte Internetworking Incorporated

Bbn Corporation

David J Montana

Sean A Moore

Stephen Douglas Milligan

Joseph B Walters

Jos&eacute; Luis Herrero

Robert L Popp

Joseph J Destefano

Daniel C Gabriner

A domain-independent, reconfigurable scheduling system ( ) is provided. The scheduling system ( ) receives a data object describing one or more resources to be scheduled. The data object is related to the domain for which the scheduling system ( ) is being configured. The scheduling system ( ) also receives scheduling semantics related to the domain. The scheduling semantics are received in the form of non-compiled data. The scheduling system ( ) may then be configured for the domain using the data object and scheduling semantics.

Domain-independent reconfigurable scheduler

Methods and systems consistent with the invention provide a robot network that can optimally reconfigure the control logic on-board each robot of the network on a real-time basis. Each robot of the network performs an assigned task according to a control logic. While performing the assigned task, each robot transmits performance data defining a fitness level of the robot for achieving the assigned task. A set of the plurality of robots is then selected based on the performance data transmitted by each robot, and the control logic of the selected set of robots is then sent to the other robots in the network. The robots that receive the control logic then optimize their control logic by producing a new control logic based on the received control logic.

System for dynamically reconfigure wireless robot network

An improved Genetic Algorithm scheduling system includes system for encoding and testing hard constraint information. Each resource and task includes an associated capability and constraint indicating component. A comparison of the capability and constraint components provides an indication of the associated resource is capable of perform the proposed task. The system also includes a method of creating genomes using cost factors and weight settings to produce initial genomes which encode at least partly optimized schedules. The weight settings can be manipulated to emphasize different cost factors during genomes creation. This method also allows changes to be added into a running GA scheduling system, in that new or changed tasks and new or changed resources are encoded into the genome population. The system further includes a method of efficiently detecting and deleting duplicate genomes by converting genomes into a schedule representation, then re-encoding the genomes, and performing a sequential comparison of the genomes.

System and method for genetic algorithm scheduling systems

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Arlington, MA, United States of America

David J Montana