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CIS Structure

Collaborative Intelligent Systems William A. Gruver Dilip B. Kotak FLINT – CIBI 2003 UC Berkeley - 15 December 2003. CIS Structure. Monitoring Identification. Communi- cations. Collaboration Coordination. Systems Architecture. Manufac. Supply Chain. Service. Infrastructure

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CIS Structure

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  1. Collaborative Intelligent SystemsWilliam A. GruverDilip B. KotakFLINT – CIBI 2003UC Berkeley - 15 December 2003

  2. CIS Structure Monitoring Identification Communi- cations Collaboration Coordination Systems Architecture Manufac. Supply Chain Service Infrastructure Elec. Power Infrastructure Transport.

  3. Technologies • Self Monitoring, Identification & Control • Hardware – sensors, monitoring & actuators Auto-ID Software Soft Computing • Agent Communications Infrastructure • Hardware – wired, wireless RF, 802.11 • Software – ontology, protocols, security FIPA, JADE • Agent Collaboration & Coordination • Negotiation Contract Net, Blackboard, Auctions • Soft Computing Fuzzy Logic, Neural Networks, Genetic Algorithms • Agent Architecture • Hardware Distributed Computing • Software PROSA, Multi-Agent Architectures

  4. Applications • Manufacturing & Supply Chain Sector • Wood Products Manufacturing Loewen Windows • Service Sector • Health, Banking, Insurance, Food • Transportation Infrastructure • Public, Fleet • Energy Infrastructure • Electrical – Distributed Power • Hydrogen & Alternative Fuels – Mobile & Stationary Fuel Cells

  5. Typical Rough Mill Layout ChopSaw Scanner RipSaw Scanner LUMBER WAREHOUSE Lumber

  6. Summary of Key Issues • Select most suitable jag – 2,500 choices 30-50 times / day • Assign arbor/ripping priority – 18choices 5-10 times / day • Schedule components on kickers – 5 x 1020 choices 150 times / day • Coordinate these three • Optimizing the overall performance ofthe following: • Costs • Yield • Productivity • Grade & length utilization • Order-file satisfaction • On time delivery

  7. System Architecture - for Rough Mill Production Local optimization Rip each piece of lumber into a best combination of strip by width Agent 1 Agent 2 Mediator Agent Select a best load or jag of lumber from the warehouse • Facilitator’s functions • Static optimized overall schedule • Task decomposition and allocation • System coordination and decision making • Learning Agent 3 Optimize component schedule to produce a best combination of components for each strip

  8. Applications – Three Layers • Manufacturing • Supply Chain & Factory coordination of logistics • Work-Cell coordination of production • Device intelligent autonomous devices • Energy Infrastructure • Inter Community coordination of supply & demand • Intra-Community coordination of devices • Devices • Photovoltaic cell, wind turbine, electrolyzer, reformers, storage, compressor, dispensers • Generalize to Multi-Layered Holarchy • Hierarchy of collaborative agents

  9. Intelligent Wireless MicroRouter • Multi-point, multi-hop, wireless connectivity of computers, sensors, displays, PDAs • Intelligent routing and priority scheduling over extended distances at 11-54 Mbs • Based on IEEE 802.11x protocol standards • Distributed systems implementation in JADE • In development by Intelligent Robotics Corporation and Simon Fraser University

  10. Extended Coverage using 802.11

  11. Features • Hubless • Self organizing: connectivity without centralized communication • Intelligent • Self configuring: without server or external router • Distributed scheduling of tasks by priority, time, due dates • Wireless • Protocol: IEEE 802.11b/g • Interfaces: USB2.0, RS232, IEEE 802.3 • Scalable • Additional network devices and services may be easily added • Robust • Adaptability to user demands and network failures • Secure • Advanced encryption algorithms

  12. Industrial ApplicationUtility Metering • Enables utility meters to be networked without the need for hubs • Utility meter data intelligently routed over networks with millions of nodes • Provides network management, load balancing, and priority scheduling of services • Applications also to transportation and other infrastructure systems

  13. Holon 1 Holon 2 Time Distributed Combinatorial Scheduler • Process • task performed by one agent • Operation • sequence of processes • never revisits an agent • Fully distributed algorithm • Software • multi-threaded C++ and OpenGL • being ported to Java for operation in JADE environment DCS1 DCS2

  14. Conclusions • Collaborative intelligent systems have broad applications • manufacturing resource management, planning, and control • transportation, energy, and other infrastructure systems • service systems • Collaborative intelligent systems provide • improved flexibility • reduced setup-time • higher robustness • integration of human intelligence • Collaborative intelligent systems are characterized by • reconfigurability • task level programming • high-level cooperation

  15. Further information … www.ensc.sfu.ca/irms Intelligent Robotics and Manufacturing Systems Laboratory www.ieeesmc.org IEEE-SMC Technical Committee “Collaborative Intelligent Systems” hms.ifw.uni-hannover.de Holonic Manufacturing Systems Consortium www.fipa.org Foundation for Intelligent Physical Agents www.ims.org Intelligent Manufacturing Systems Program

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