DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Manuscript for IEEE Transactions on Automatic Control 16 31.0 30.8 30.6 Mode 30.4 30.2 A1 A2 A3 A4 30.0 A5 0 200 400 600 800 1000 1200 Time Fig. 7. Behavior of v i * under stable decision model The effects of the stability on performance are shown in Table 1. QoS is improved significantly when using the stable decision model. Improved prediction accuracy made the system behaving stable and consequently performing better. Table 1. The effects of stability on performance Decision model Naïve Stable T V QoS T V QoS 1171 15289 3583 1082 15104 4282 T: Completion time, V: Value of solution 7. Decentralization The next question is how to decentralize the programming model. Centralized control mechanisms scale badly, due to the rapid increase of computational and communicational overheads with system size. Single point failure will often lead to failure of the complete system leading to a non-robust network. Decentralization can address these issues by distributing the computations and communications to multiple entities. In addition to these properties
Manuscript for IEEE Transactions on Automatic Control 17 decentralization will give a byproduct, information security. As we discussed earlier our effort is to support survivability. If information is revealed to others directly information security will be in question. In this section we decentralize the programming model through an auction market. 7.1 Two-tier auction market There are two popular methods of decentralizing structured programming models: decomposition methods and auction/bidding algorithms. Considering the compatible structure of the programming model, we decentralize it through a non-iterative auction mechanism, so called multiple-unit auction with variable supply [27]. In this auction a seller may be able and willing to adjust the supply as a function of bidding. In the programming model we have built, all components are coupled with each other. However, the objective function and constraints are separable if one variable T is fixed. This characteristic makes it possible to solve the model through an auctioning process for T. The completion time T is an unbounded resource and the supply can be adjusted as a function of bidding. To design the auction market we define a seller which determines T * based on the bids from the components. We call this auction market as Two-tier Auctioning Model. We define T i as available resource of component i which is required minimally to the amount of T i(min) as in (12) and maximally T i(max) as in (13). T T = [ R ( t ) L ( t ) f ( v )] / MRA ( t ) (12) i (min) i + i i i(min) = [ R ( t ) L ( t ) f ( v )] / MRA ( t ) (13) i (max) i + i Each component bids to the seller with maximal value as a function of T as in (14). The seller decides T * based on the bids by considering CCT(T) as in (15). After the seller broadcasts T * , each component selects its optimal value mode in the limits T * as in (16). Though this auctioning i i(max) i i
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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
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6 Characterization and analysis of
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timizing simultaneously the link de
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where ∆ 1 (j) ≥ 0 and ∆ 2 (i,
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Table 2. GA Results Agent N A D max
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connected component, in which a pat
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Random Small-world Scale-free with
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Growth mechanisms Start with a smal
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Table 2. The proposed network’s c
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DMAS Controller. The functional uni
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1000 500 0 -500 -1000 0.2 0.4 0.6 0
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tems. Proceedings of the Second Joi
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Proceedings of the 1st Open Cougaar
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Page 42 and 43: 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 54 and 55: 100% 1 95% 2 14 15 64% TAO 4 64% 62
Page 56 and 57: interactions as a dynamical system
Page 58 and 59: ehavior states under varying system
Page 60 and 61: Extensive testing and validation of
Page 62 and 63: 5 Conclusions and Future Research T
Page 64 and 65: 2 to function critically even under
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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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Page 108 and 109: 4 t Si t ∫ + ( ) i ) t When RA i
Page 110 and 111: 6 S UB i ∑ = P LI / LI . (16) i p
Page 112 and 113: 8 policies while underutilizing in
Page 114 and 115: architecture based on both the Grid
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Page 118 and 119: component is a member of one of the
Page 120 and 121: denotes the immediate predecessors
Page 122 and 123: limit of large number of tasks. If
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Page 126 and 127: We set up eight different experimen
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Page 130 and 131: pp. 191-200. [3] I. Foster, C. Kess
Page 132 and 133: 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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