DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Extensive testing and validation of the algorithms was performed on a number of societies with varying number of agents. The results obtained with the linear estimator approach seem very encouraging across different supply classes. Due to space limitations we would show results for few of these items 1 . Figure 5 shows actual demand and predicted demand values for an Ammunition item. We can see that the predicted values are very close to the actual values and match with the reorder periods of the actual demand. The communications were cut for 12 days (day 43 -55) during which we can still see the predictions, but no actual demand tasks. In Figure 6 (for Subsistence item) observe that the predicted demand catches up with the actual demand. The initial error is due to the initial model inaccuracies that are reduced as the model is updated with observed data. Figure 6. Actual vs. Predicted values for Subsistence item Figure 5. Actual vs. Predicted values for Ammo item Figure 7 and Figure 8 show the planned demand (model), actual demand, predicted demand without communication loss and predicted demand with communication loss for BulkPOL item and Subsistence item respectively. As actual demand values roll in, the measurement equations reduce the error causing the model to closely mimic the actual demand pattern. With communication loss, the predictor uses the last updated value to forecast demand. Figure 7. With & W/o Comm. Loss Prediction for BulkPOL item 1 Some of the experimental results in this paper are shown for Tiny and Small societies, which are smaller versions of Full society. Note that since the agents are generic, the demand patterns are similar in all the societies. Figure 8. With & W/o Comm. Loss Prediction for Subsistence item
4.2 Moving Average Model Results Table 1 shows some typical data collected for the moving average model based predictor. Each column shows the demand sent to the supplier (MSB) from the customer (FSB). On each execution day the customer sends the demand for about 20 days ahead. Suppose there is a communication loss on day 51, the predictor then forecasts the demand on day 52. The graphs below (Figure 9, Figure 10 and Figure 11) show some of the results for the moving average based predictor. These results are a few representative examples of several runs performed across a number of Cougaar societies for various supply classes. The results show that the forecasted demand is quite close to the original demand thus validating the methodology of the predictor. Table 1. Demand Requests from FSB to MSB Figure 9. Comparison of the forecasted demand with the actual demand for BulkPOL in Small society Figure 10. Comparison of the forecasted demand with the actual demand for BulkPOL in Tiny society Figure 11. Comparison of the forecasted demand with the actual demand for Ammunition in Tiny society
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Ultra*Log PSU/IAI Final Report for
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Contents Contents .................
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Executive Summary Ultra*Log is a De
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2.3 Gnanasambandam, N., Lee, S., Ku
Page 10 and 11: 6 Characterization and analysis of
Page 12 and 13: timizing simultaneously the link de
Page 14 and 15: where ∆ 1 (j) ≥ 0 and ∆ 2 (i,
Page 16 and 17: Table 2. GA Results Agent N A D max
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Page 20 and 21: Random Small-world Scale-free with
Page 22 and 23: Growth mechanisms Start with a smal
Page 24 and 25: Table 2. The proposed network’s c
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Page 30 and 31: DMAS Controller. The functional uni
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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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Page 56 and 57: interactions as a dynamical system
Page 58 and 59: ehavior states under varying system
Page 62 and 63: 5 Conclusions and Future Research T
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Page 66 and 67: 4 points into a corresponding inner
Page 68 and 69: 6 stresses well. The Warehouse 1 ag
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Page 72 and 73: Table 1: Notation Symbol Descriptio
Page 74 and 75: 4 Conclusions and Future Work 4.1 C
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Page 78 and 79: Manuscript for IEEE Transactions on
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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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