DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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interactions as a dynamical system and the second is based on moving averages. The paper is organized as follows. In Section 2, we discuss the modeling approach adopted to build the predictors. In Section 3, software implementation of the predictors as plugins within a Cougaar agent is discussed. In Section 4, we discuss experimental results based on the implementation of the predictors on a logistics system built on Cougaar. Approaches adopted to tune the predictors based on historical data is also discussed. Section 5 discusses conclusions and possible future areas of research and development. 2 Predictor Design and Algorithms In this section we discuss the predictor algorithms that were implemented in Cougaar. Before we discuss the technical details of the algorithms we present a brief description of the logistics application domain, as some aspects of the design and implementation are specific to the application. 2.1 Logistics Scenario The Cougaar multi-agent society considered in this effort is the Full society that was developed as a part of DARPA’s Ultralog Program (See [1] for more details) The Full is a military supply chain logistics society that consists of many different supply classes. Each agent in the society represents a military unit performing a certain logistics operation in the supply chain. For e.g. the TRANSCOM agent represents the transportation command authority for the US military. It issues directives to its subordinate units regarding the transportation to be provided to a particular agent for a particular type of shipment. Figure 1 shows the organizational structure of the prototype Full society. Figure 1. Full society hierarchical structure There are five main supply chain threads in the prototype military logistics society. They are (i) Ammunition Supply Chain (ii) Petroleum, Oil and Lubricants Supply Chain (BulkPOL and PackagedPOL) (iii) Subsistence Supply Chain (Food, Water) (iv) Repair Parts Supply Chain; and (v) Transportation Supply Chain Within each supply chain there exists a customersupplier relationship between various agents. A customer makes requests for various items (POL, ammunition etc) to its supplier and the supplier in turn attempts to meet these demands based on its current inventory, or forwards the requests up the supply chain hierarchy. Thus, depending on its positioning in the hierarchy, a supplier can also be the customer for another agent. Figure 2 shows a part of the supply chain. Here, FSB is a supplier. ARBN and INFBN are customers of FSB (Note: FSB is also a customer of MSB.) These agents send demand requests to the FSB, which are managed by its Inventory Manager. Based on the operation plan, Optempo and current inventory each customer (agent) requests items from its supplier. Figure 2. Predictor Implementation in Agent Network
2.2 Role of Predictors As mentioned earlier, when deployed in a battlefield this agent society is subject to several stresses related to varying wartime loads, kinetic and information warfare. These stresses may result in node failures, denial of service and other network related faults that will result in the lack of communications between agents. In this decentralized application the inability of customer/ suppliers to make/meet requests can significantly impact the performance of the various operational units. In this setting, predictors can play an important role in maintaining the supply chain connectivity, while network related faults are restored. The predictors can provide the ability to approximate the “expected behavior” by continuing to make appropriate demand/supply projections. We focus on two classes of predictors (i) a customer predictor that resides at the supplier agent and estimates the customer’s demand when communications are lost (ii) a supplier predictor inserted at the customer agent that predicts the allocation results for the tasks generated by the supplier. As shown in Figure 2, the customer predictor residing on FSB forecasts each customer’s (ARBN and INFBN) demand when communications are lost. In a similar fashion the supplier predictor residing on INFBN agent predicts the supplier’s behavior. These agents use the predicted values and continue execution of their functionality. Depending on the accuracy of the predictors, typically predicted states are not identical to the actual states. Thus when communications are restored, and actual demands/supply values are available, any errors between estimated and actual values will need to be resolved. This process, termed as “reconciliation”, requires any surplus tasks to be rescinded and new tasks added for any shortfalls. The predictors in turn will need to update their models based on available data. 2.3 Predictor Design 2.3.1 Customer Predictor The customer predictor is implemented in the form of two plugins. One plugin is used during the planning mode where it collects the data about the customersupplier relationship and items involved. It also collects the Optempo of these items. Another plugin is used during the execution mode. This Plugin monitors the demand from the customer and predicts the demand when there is a communication loss. Figure 3 shows the framework of the customer predictor during the execution mode. Figure 3. Customer Predictor 2.3.2 Supplier Predictor This predictor is also built in the form of two plugins. One plugin resides at the supplier and other resides at the customer. The Plugin at the supplier periodically sends the snapshots of the inventory levels for each item in all the supply classes to the Plugin at the customer. The plugin at the customer uses this information for predicting the allocation results of the demand task. The design of the supplier predictor is represented diagrammatically in Figure 4. Figure 4. Supplier Predictor 2.4 Predictor Algorithms Based on the nature of the supply chain dynamics (uncertainty in demand, model complexity), duration of communication loss, computational requirements different approaches for the predictors were investigated. These ranged from dynamical systems, to classification theory to traditional forecasting [3,4,6,7,8]. While some of our research [5] and prototypes indicates that we may get better prediction results by using a non parametric method such as support vector machine, radial basis function neural network etc., from an implementation/computational perspective this becomes impractical. The primary reason is that as the society size scales it becomes increasingly difficult to generate historical patterns for each agent and classify its
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Ultra*Log PSU/IAI Final Report for
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Contents Contents .................
Page 5: Executive Summary Ultra*Log is a De
Page 8 and 9: 2.3 Gnanasambandam, N., Lee, S., Ku
Page 10 and 11: 6 Characterization and analysis of
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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 60 and 61: Extensive testing and validation of
Page 62 and 63: 5 Conclusions and Future Research T
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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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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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