DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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6 stresses well. The Warehouse 1 agent can detect exactly all the stress types even though it is far from retailers (see Fig 1.). CONCLUSIONS We have shown an effective application of pattern recognition model for detecting stresses by observing the internal state of each agent. Each agent has a SVM since the influence of the same stress on different agents can be different. Some agents near the retailers can detect stresses very well. However, it is hard to detect the influence of the stress on agents which are far from retailers. Overall performance of predictor of each agent is good. Constructing the capability for stress detection is the first step towards improving the survivability of a multi-agent system. This result is important because we can pursue further research on how we can dampen the effect of stresses based on the result of this study. Based on current detected state each agent can change their behaviors - ordering or planning - to adapt to stress conditions without serious performance degradation of overall supply chain. In addition, our approach is generally useful because it is very hard to model a complex system analytically. ACKNOWLEDGEMENTS Support for this research was provided by DARPA (Grant#: MDA 972-01- 1-0563) under the UltraLog program. REFERENCES Baranger, Michel, “Chaos, Complexity, and Entropy – A physics talk for non-physicists,” MIT- CTP-3112, http://necsi.org/projects/baranger/cce.pdf. Burges, C. J. C., 1998, “A Tutorial on Support Vector Machines for Pattern Recognition,” Knowledge Discovery and Data Mining, Vol. 2, No. 2, pp. 121-167. Chapelle, O., Haffner, P. and Vapnik, V. N., 1999, “Support Vector Machines for Histogram-Based Image Classification,” IEEE Transactions on Neural Networks, Vol. 10, No. 5, pp. 1055-1064. Choi, T., Dooley, K. and Rungtusanatham, M, 2000, " Supply Networks and Complex Adaptive Systems: control versus emergence,” Journal of Operations Management, Vol. 19, pp 351-366. Dooley, K., 2002, “Simulation Research Methods,” Companion to Organizations, Joel Baum (ed.), London: Blackwell, pp. 829-848. Hsu, Chih-Wei and Lin, Chih-Jen, 2002, “A Comparison of Methods for Multiclass Support Vector Machines,” IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415-425. Liang, H. and Lin, Z., 2001, “Detection of Delayed Gastric Emptying from Electrogastrograms with Support Vector Machine,” IEEE Transactions on Biomedical Engineering, Vol. 13, No. 2, pp. 415-425. Moghaddam, B. and Yang, M., 2002, “Learning Gender with Support Faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp. 707-711. Műller, K., Mika, S., Rätch, G., Tsuda, K. and Schőlkopf B., 2001, “A Introduction to Kernel-Based Learning Algorithms,” IEEE Transactions on Neural Networks, Vol. 12, No. 2, pp. 181-202. Osuna, E. E., Freund R. and Girosi, F., 1997, Support Vector Machines: Training and Applications, Technical Report AIM-1602, MIT A.I. Lab. Vapnik, V. N., 1998, Statistical Learning Theory, John wiley & sons, Inc, New York.
A Framework for Performance Control of Distributed Autonomous Agents Nathan Gnanasambandam, Seokcheon Lee, Soundar R.T. Kumara and Natarajan Gautam 310 Leonhard Building, The Pennsylvania State University, University Park, PA, 16802, USA Abstract We propose an autonomous and scalable queueing theory-based methodology to control the performance of a hierarchical network of distributed agents. Multi-agent systems (MAS) such as supply chains functioning in highly dynamic environments need to achieve maximum overall utility during operation. Hence, the objective of the control framework is to identify the trade-offs between quality and performance and adaptively choose the operational settings to posture the MAS for better utility. By formulating the MAS as an open queueing network with multiple classes of traffic we evaluate the performance and subsequently the utility, from which we identify the control alternative for a localized, multi-tier zone. Keywords: Queueing Network, Multi-Agent Systems, Performance control. 1 Introduction With the growing view of agent-oriented software sytems [1] and the increased deployment of distributed multi-agent systems (DMAS) for numerous emerging applications such as computational grids, e-commerce hubs, supply chains and sensor networks, we are faced with large-scale distributed agents whose performance needs to be estimated and controlled. Often times, these DMAS operate in dynamic and stressful environmental conditions, of one type or the other, in which the MAS as whole must survive. While survival notion necessitates adaptivity to diverse conditions along the dimensions of performance, security and robustness, delivering the correct proportion of these quantities can be quite a challenge. In this paper, we address a piece of this problem by building an autonomous performance control framework for MAS. While building large multi-agent societies (such as UltraLog [2]), it is desirable that the associated adaptation framework be generic and scalable. One way to do this is to utilize a methodology similar to Jung and Tambe [3], where the bigger society is composed of smaller building blocks, in this case, corresponding to communities of agents. Although, strategies for co-operativeness and distributed POMDP to have been utilized to analyze performance in [3], an increase in the number of variables in each agent can quickly render POMDP ineffective even in reasonable sized agent communities due to the statespace explosion problem. In [4], Rana and Stout identify data-flows in the agent network and model scalability with Petri nets, but their focus is on identifying synchronization points, deadlocks and dependency constraints with coarse support for performance metrics relating to delays and processing times for the flows. In a recent architecture for autonomic computing, Tesauro et al. [5] build a real-time MAS-based framework that is self-optimizing based on application-specific utility. While [3, 4] motivate the need to estimate performance of large DMAS using a building block approach, [5] justifies the need to use domain specific utility whose basis should be the network’s service-level attributes such as delays, utilization and response times. We believe that by using queueing theory we can analyze data-flows within the agent community with greater granularity in terms of processing delays and network latencies and also capitalize on using a building block approach by restricting the model to the agent community. Queueing theory has been widely used in networks and operating systems [6]. However, the authors have not seen the application of queueing to MAS modeling and analysis. Since, agents lend themselves to being conveniently represented as a network of queues, we concentrate on engineering a queueing theory based adaptation (control) framework to enhance the application-level performance. Inherently, the DMAS can be visualized as a multi-layered system as is depicted in Figure 1a. The top-most layer is where the application resides, usually conforming to some organization such as mesh, tree etc. The infrastructure layer not only abstracts away many of the complexities of the underlying resources (such as CPU, bandwidth), but more importantly provides services (such as Message Transport) and aiding agent-agent services (such as naming, directory etc.). The bottom most layer is where the actual computational resources, memory and bandwidth reside. Most studies in the literature do not make this distinction and as such control is not executed in a layered fashion. Some studies such as [7, 8], consider controlling attributes in the physical or infrastructural layers so that some properties (eg. survivability) could result and/or the facilities provided by these layers are taken advantage of. Often, this requires rewiring the physical layer, availability of a infrastructure level service or the ability of the application to share information with underlying layers in a timely fashion for control purposes. In this work, we consider control only due to application-level trade-offs such as quality of service versus performance and assume that infrastructure level services (such as load-balancing, priority scheduling) or physical level capabilities (such as rewiring) are not possible. This does not exclude the possibility that in future we can combine all approaches to achieve a multi-layered control.
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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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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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