DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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1 SITUATION IDENTIFICATION USING DYNAMIC PARAMETERS IN COMPLEX AGENT-BASED PLANNING SYSTEMS SEOKCHEON LEE, N. GAUTAM, S. KUMARA, Y. HONG, H. GUPTA, A. SURANA, V. NARAYANAN, H. THADAKAMALLA, M. BRINN, M. GREAVES Department of Industrial Engineering The Pennsylvania State University University Park, PA 16802 ABSTRACT Survivability of multi-agent systems is a critical problem. Real-life systems are constantly subject to environmental stresses. These include scalability, robustness and security stresses. It is important that a multi-agent system adapts itself to varying stresses and still operates within acceptable performance regions. Such an adaptivity comprises of identifying the state of the agents, relating them to stress situations, and then invoking control rules (policies). In this paper, we study a supply chain planning implemented in COUGAAR (Cognitive Agent Architecture) developed by DARPA (Defense Advanced Research Project Agency), and develop a methodology to identify behavior parameters, and relate those parameters to stress situations. Experimentally we verify the proposed method. 1. INTRODUCTION Survivability of multi-agent systems is a critical problem. Real-life systems are inherently distributed and are constantly subject to environmental and internal stresses. These include scalability, robustness and security stresses. It is important that a multiagent system adapts itself to varying stresses and still operates within an acceptable performance region. Such an adaptivity comprises of identifying the state of the agents, relating them to stress situation, and then invoking control rules (policies). One of the fundamental problems is agent state (behavior) identification. In this paper, we study a supply chain planning society called Small Supply Chain (SSC) implemented in COUGAAR (Cognitive Agent Architecture) developed by DARPA (Defense Advanced Research Project Agency), and develop a methodology for behavior parameter identification, and relating it to stress situations. The two important steps in our methodology are: 1. Identify the most discriminable behavior parameter set for situation identification, 2. Apply it to situation identification. To identify the most discriminable behavior parameter set we collect the time series data from one of the agents in SSC (TAO) and compute 38 statistical and deterministic parameters to represent the collected time series. In essence, these 38 parameters are the features of agent state. In our earlier work (Ranjan et al., 2002) we prove that SSC shows chaotic behavior from an inventory fluctuation point of view and computed chaos indicators (which we call as deterministic parameters without loss of generality). Though we compute 38 different parameters, next question we address is whether all these are really useful and necessary for identifying several stress situations. So, we develop a discriminability index and identify the most discriminable behavior parameter set based on this index as a
2 representative parameter set for identifying several stress situations. Using those parameters we develop a nearest neighbor classification based method to identify stress situations. 2. SSC (SMALL SUPPLY CHAIN) SOCIETY SSC is a COUGAAR society for supply chain planning composed of 26 agents. Each agent generates logistics plan depending on its relative position in the supply chain. TAO is an important agent of the SSC and we have selected it to test our schema. Figure 1 shows the detailed view. In TAO GenerateProjection Tasks are expanded to Supply Tasks, which are for internal consumption. Each Supply Task is expanded to Withdrawal Task, which is allocated to inventory asset. Supply Tasks are also transferred from other agents. They are expanded to Withdrawal Tasks, which are allocated to inventory asset. MaintainInventory Tasks, which are for the maintenance of inventory assets in TAO, are expanded to Supply Tasks. Each Supply Task is allocated to other agents. MaintainInventory ProjectSupply Supply TAO GenerateProjection ProjectSupply Supply ProjectWithdrawl Withdrawl Inventory Asset Figure 1. TAO in SSC 3. STRESSES AND BEHAVIOR For the sake of analysis we have parameterized the stress situations and system behavior. 3.1 Stress Stress refers to survivability stress and includes scalability, security, and robustness stresses. Scalability is defined as the ability of a solution to a problem to work when the size of the problem increases. And, survivability (regarding security and robustness) is defined as as the capability of a system to fulfill its mission, in a timely manner, in the presence of attacks, failures, or accidents (Ellison et al., 1997). There can be diverse stress situations, but in this paper we consider stress situations formed by two scalability stress types given below: • Problem Complexity: Problem complexity is determined by the complexity of the planning task. This includes many aspects and we have chosen one of the stress types, called OpTempo of each agent. OpTempo defines operation tempo. • Query Frequency: Each agent provides query service for its planning information to human operators. We have chosen query frequency (# of query request per second) to each agent as one of stress types. Although SSC society is composed of 26 agents there are only 8 agents that are directly affected by OpTempo. We define stress levels: Low/Medium/High. So, the size of our stress situation space becomes 3 34 .
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Page 3 and 4: 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 16 and 17: Table 2. GA Results Agent N A D max
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Page 22 and 23: Growth mechanisms Start with a smal
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Page 46 and 47: 3 3.2 Behavior In SSC society an ag
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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
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Page 72 and 73: Table 1: Notation Symbol Descriptio
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Manuscript for IEEE Transactions on
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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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