DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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2.3 Gnanasambandam, N., Lee, S., Kumara, S. R. T., Gautam, N., Peng, W., Manikonda, V., Brinn, M. and Greaves, M., “Survivability of a distributed multiagent application – A performance control perspective”, IEEE Symposium on Multi-agent Security and Survivability (MAS&S 2005), Philadelphia, 2005. 2.4 Lee, S., and Kumara, S. R. T., “Survivability through Implementation Alternatives in Large-scale Information Networks with Finite Load,” Proceedings of Open Cougaar Conference, July 2004. 3 Monitoring, situation identification and pattern extraction An essential task for control is sensing. We have developed different tools to monitor and sense distributed systems. With the help of these tools, we devised many situation identification and pattern extraction algorithms based on theory of chaos, knowledge mining and Kalman filter principles. The following are the papers published in this field of research. 3.1 Lee, S., Gautam, N., Kumara, S. R. T., Surana, A., Gupta, H., Hong, Y., Narayanan, V., and Thadakamalla, H. P., “Situation Identification Using Dynamic Parameters in Complex Agent-Based Planning Systems,” Intelligent Engineering Systems Through Artificial Neural Networks, v 12, 2002. 3.2 Lee, S., and Kumara, S. R. T., “Estimating global stress environment through local behavior in a multiagent-based planning system,” IEEE Conference on Automation Science and Engineering (CASE 05), Edmonton, Canada, August 2005. 3.3 Gupta, H., Hong, Y., Thadakamalla, H. P., Manikonda, V., Kumara, S. R. T. and Peng, W., “Using Predictors to Improve the Robustness of Multi-Agent Systems: Design and Implementation in Cougaar”, Proceedings of Open Cougaar Conference, July 2004. 3.4 Hong, Y., Gautam, N., Kumara, S. R. T., Surana, A., Gupta, H., Lee, S., Narayanan, V., and Thadakamalla, H. P., “Survivability of Complex System – Support Vector Machine Based Approach,” Intelligent Engineering Systems Through Artificial Neural Networks, v 12, 2002. 4 Control The heart of adaptivity is control. In our work we therefore, build different control frameworks and methods for distributed systems. The following are the papers published in this research area. 4.1 Gnanasambandam, N., Lee, S., Kumara, S. R. T. and Gautam, N., “A Framework For Performance Control of Distributed Autonomous Agents,” Industrial Engineering Research Conference (IERC), Atlanta, August 2005.
4.2 Gnanasambandam, N., Lee, S., Gautam, N., Kumara, S. R. T., Peng, W., Manikonda, V., Brinn, M. and Greaves, M., “An Autonomous Performance Control Framework for Distributed Multi-Agent Systems: A Queueing Theory Based Approach,” Autonomous Agents and Multi-Agent Systems (AAMAS), Utrecht, Netherlands, July 2005. 4.3 Lee, S. and Kumara, S. R. T. “Adaptive control for large-scale information networks through alternative algorithms to support survivability”, submitted to IEEE Transactions on Automatic Control. 4.4 Lee, S., Kumara, S. R. T. and Gautam, N., “Self-organizing resource allocation for minimizing completion time in large-scale distributed information networks”, submitted to IEEE Transactions on Systems, Man, and Cybernetics. 4.5 Lee, S., Kumara, S. R. T. and Gautam, N., “Efficient method of quantifying minimal completion time for component-based service networks: Network topology and resource allocation”, submitted to IEEE Transactions on Computers. 4.6 Lee, S., Kumara, S. R. T. and Gautam, N., “Market-based model predictive control for large-scale information networks: Completion time and value of solution”, submitted to IEEE Transactions on Parallel and Distributed Systems. 4.7 Hong, Y. and Kumara, S. R. T., “Coordinating control decisions of software agents for adaptation to dynamic environments” 37 th CIRP International Seminar on Manufacturing Systems (ISMS-2004), Budapest, Hungary, May 2004. 5 CPE society modeling and performance analysis We have built a demo society, “CPEDemo”, for identifying the key aspects within a continuous planning and execution scenario. This will help us to identify and demonstrate key concepts in the argument for and concept of “design for survivability”. The following are the papers published related to the CPE society. 5.1 Peng, W., Manikonda, V. and Kumara S. R. T., “Understanding agent societies using distributed monitoring and profiling” Proceedings of Open Cougaar Conference, July 2004. 5.2 Gnanasambandam, N., Lee, S., Gautam, N., Kumara, S. R. T., Peng, W., Manikonda, V., Brinn, M. and Greaves, M., “Reliable MAS Performance Prediction Using Queueing Models,” IEEE Symposium on Multi-agent Security and Survivability (MAS&S 2004), Philadelphia, PA, 2004
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Page 3 and 4: Contents Contents .................
Page 5: Executive Summary Ultra*Log is a De
Page 10 and 11: 6 Characterization and analysis of
Page 12 and 13: timizing simultaneously the link de
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Page 16 and 17: Table 2. GA Results Agent N A D max
Page 18 and 19: connected component, in which a pat
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Page 22 and 23: Growth mechanisms Start with a smal
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Page 30 and 31: DMAS Controller. The functional uni
Page 32 and 33: 1000 500 0 -500 -1000 0.2 0.4 0.6 0
Page 34 and 35: tems. Proceedings of the Second Joi
Page 36 and 37: Proceedings of the 1st Open Cougaar
Page 44 and 45: 1 SITUATION IDENTIFICATION USING DY
Page 46 and 47: 3 3.2 Behavior In SSC society an ag
Page 48 and 49: 5 All the behavior parameters may n
Page 50 and 51: Estimating Global Stress Environmen
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ehavior states under varying system
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Extensive testing and validation of
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5 Conclusions and Future Research T
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2 to function critically even under
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4 points into a corresponding inner
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6 stresses well. The Warehouse 1 ag
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Table 1: Notation Symbol Descriptio
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4 Conclusions and Future Work 4.1 C
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O, U Stresses Physical/Infrastructu
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Manuscript for IEEE Transactions on
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2 discuss problem domain and in Sec
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4 t Si t ∫ + ( ) i ) t When RA i
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6 S UB i ∑ = P LI / LI . (16) i p
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8 policies while underutilizing in
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architecture based on both the Grid
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to machines (network topology) and
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component is a member of one of the
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denotes the immediate predecessors
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limit of large number of tasks. If
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Min-min heuristic algorithm Step 1:
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We set up eight different experimen
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0. 4 8057 8237 0.5 6439 6635 0.6 53
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pp. 191-200. [3] I. Foster, C. Kess
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Technology, Cambridge, MA, 1995. [2
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Manuscript for IEEE TRANSACTIONS ON
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Coordinating Control Decisions of S
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directly. It has coarser scale. It
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Understanding Agent Societies Using
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• For tasks, the UID of the direc
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for tracking the dependencies betwe
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within the monitored enclave. With
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Figure 1. Agent Hierarchy in CPE So
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Figure 3. The World Model CPY Agent
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Table 2. TechSpecs: Infrastructure
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terms of the average waiting times
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Acknowledgements The work described
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key realization to tackle this prob
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are or ignored perturbations. The e
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sustained oscillations. If the inst
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solved by biological systems for li
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non-linear and non-stationarity. Ne
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D k : Outflow from high priority qu
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the system starts to perform self-s
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sustained in a supply chain. Resour
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which are necessary to make good mo
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focusing on the computational compl
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line of research that deals with ne
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ealizable. But the inherent complex
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Min, H. and Zhou, G., 2002, Supply
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In the rest of this paper we report
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shows relatively irregular behavior
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