DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Manuscript for IEEE Transactions on Automatic Control 20 8. Empirical results We ran several experiments using discrete-event simulation to validate the designed control mechanism. Though we use a small network in the experimentation for validation purpose, the decentralized model, especially, can handle much larger networks. 8.1 Experimental design The experimental network is composed of fifteen components with a tree structure as shown in Fig. 8. Each component in the lowest position has 200 root tasks. Also, all the components have a common linear value function and the cost of completion time is linear increasing function as indicated in the figure. A 1 A 2 CCT(T) = 4T A 3 A 15 A 4 A 5 A 8 A 9 A 10 A 11 A 6 A 7 A 12 A 13 A 14 200 200 200 200 200 200 200 200 Fig. 8. Experimental network configuration We set up four different experimental conditions as shown in Table 2. There can be stressors which share resources with components. We assign weight w i to a component i and w i ′ to a stressor sharing resource with component i. A stressor, which has infinite work (continuously requiring resource), can impose different levels of stress on the component directly by changing w i ′. When it is zero there is no stress, and as it increases the stress level increases. We implement the stress environment by using a weighted round-robin scheduling, in which CPU time received
Manuscript for IEEE Transactions on Automatic Control 21 by each thread in a round is equal to its assigned weight. Also, the distribution of CPU time can be deterministic or stochastic. While using stochastic value function we repeat 5 experiments. Table 2. Experimental conditions Condition Stress f i (v i ) Con1 Unstressed Deterministic Con2 Unstressed Exponential Con3 Stressed Deterministic Con4 Stressed Exponential w i =0.1for all i∈A, w A4 ′ =1 in 500≤t≤1000 for Con3 and Con4 Initial value mode: (2, 5, 5, 3 for A 4 to A 15 ) We use four different control policies for each experimental condition as shown in Table 3. FL and FH use fixed value modes over time. AC-X policies represent the adaptive control mechanism we have designed. In AC-N the system is controlled under naïve decision model while in AC-S under stable decision model. When using adaptive policies the system makes decision every 100 time units (i.e., SW=100). Table 3. Control policies used for experimentation Control policy FL FH AC-N AC-S Description Fixed with lowest value mode Fixed with highest value mode Adaptive control under naïve decision model Adaptive control under stable decision model 8.2 Results Numerical results from the experimentation are summarized in Table 4. The adaptive control policies show significant advantages compared to non-adaptive ones in all different conditions. But, the benefit of AC-S is not clear in the numerical results. Though AC-S outperforms AC-N in deterministic environments, AC-N outperforms AC-S in stochastic environments. This means that AC-S cannot guarantee better performance especially in stochastic environments. But, we
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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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1 SITUATION IDENTIFICATION USING DY
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Page 60 and 61: Extensive testing and validation of
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Page 72 and 73: Table 1: Notation Symbol Descriptio
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Page 118 and 119: component is a member of one of the
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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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