Mobile hybrid software router

• US 20060085558A1
• Filed: 09/14/2005
• Published: 04/20/2006
• Est. Priority Date: 09/16/2004
• Status: Active Grant

• Current Assignee: Solomon Research LLC
• Original Assignee: Geodesic Dynamics
• Inventors: Solomon, Neal
• Granted Patent: US 7,526,460 B2
• Time in Patent Office: Days
• US Class Current: 709/238
• CPC Class Codes: H04L 41/048 mobile agents
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence

Abstract

A hybrid router for dynamical control systems is described. The mobile hybrid software router (MHSR) combines distinctive computational and mathematical techniques, including evolutionary computation (EC), probabilistic simulations (PS), machine learning and artificial neural networks (A-NNs), in order to solve unique problems encountered in an unknown environment in real time. Embodied in intelligent mobile software agents (IMSAs), the MHSR operates within a multi-agent system (MAS) to continually optimize system operation. The MHSR is applied to several major complex system categories. In one embodiment of the system, the MHSR is implemented in hardware, including continuously programmable field programmable gate arrays (CP-FPGAs), for perpetually reconfigurable evolvable hardware operation. Whether in application-specific or multi-functional mode, the MHSR is useful to groups of agents in intelligent systems for adaptation to uncertain environments in order to perform self-organization capabilities.

Claims

1. A dynamic control system architecture having a plurality of system layers interconnected to one another, comprising:

a first layer including a hybrid technique typology;

a second layer including a typology of combinations of hybrid techniques for specific applications;

a third layer including a router implementation of hybrid technique combinations;

a fourth layer including intelligent mobile software agent (IMSA) implementations of the router;

a fifth layer including group behaviors of multiple routers;

a sixth layer including auto-programming of a network of IMSAs and routers;

a seventh layer including implementation to a field programmable gate array (FPGA);

an eighth layer including a system of multiple FPGAs; and

a ninth layer including collective behavior of self-organizing networks.

10. A system as claimed in claim 1, in which the MHSR is applied to:

an enterprise resource management system;

routing plasticity in communications networks in dynamic environments;

commercial trading networks for supply chain management solutions;

bio systems, including structural proteomics, functional proteomics and personalized medicine solutions modeling, and

collective robotics systems.

2. A method to organize a mobile hybrid software router (MHSR) by combining computational or mathematical techniques into hybrid techniques or sequences of hybrid techniques, such techniques comprised of:

an evolutionary computation (EC) technique;

a probabilistic simulations (PS) technique;

an artificial neural networks (A-NN) technique or;

a machine learning (ML) technique.

3. A method as claimed in claim 2, in which implementation of the MHSR is performed in an intelligent mobile software agent (IMSA) in a multi-agent system (MAS) to:

solve computational or engineering problems;

complete computational or engineering tasks or;

optimize complex applications.

4. A method as claimed in claim 2, in which mobility is functionally performed by MHSRs or IMSAs by sending program code between computers in a network.

5. A method as claimed in claim 2, in which a database, or a combination of distributed databases, is accessed by a MHSR or IMSA.

6. A method as claimed in claim 2, in which implementation is made to a continuously programmable field programmable gate array (CP-FPGA) to facilitate adaptation to a changing environment and;

in which multi-CP-FPGA interoperation processes are implemented and coordinated to perform self-organization.

7. A method as claimed in claim 2, in which multi-IMSA interoperation processes are implemented and coordinated to perform self-organization in an intelligent system.

8. A method as claimed in claim 2, in which multi-functional MHSRs coordinate and cooperate to solve a problem or complete a task.

9. A method as claimed in claim 2, in which multiple application-specific MHSRs coordinate and cooperate to solve a problem or complete a task.