DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Figure 3. The World Model CPY Agent: Each CPY unit is designated a target area for engaging in combat actions. These action require a superior agent (BN) to supply a maneuver plan to each of the CPY agents. This plan enables the CPY agent to move along the x-axis and engage the enemy by firing. Each of these agents simulate sensors and actuators. The CPY agents consume resources and subsequently forward the demand to SUPP agents. The current status is reported to superior agents to enable replanning. BN Agent: The BN agent maintains situational awareness of all the agents under its direct command and performs (re)planning for them using a consistent set of observations that is collected continously. The BN agent has to execute a branch and bound algorithm of a specified planning depth and breadth to generate a maneuver plan for its subordinates. The BN agent serves as a medium for transferring orders from superiors to subordinates. BDE Agent: The BDE agent is responsible for generating maneuver plans for the BN and CPY agents although this implementation does not empower the BDE with that functionality. SUPP Agent: SUPP agents represent an abstracted set of supply and inventory and sustainment services. These agents take maneuver plans from the CPY agents and supply them with fuel or ammunition. It is currently assumed that the SUPP units have infinite inventory. Projected and actual consumption depend on the sustainment plan generated from orders and the presence of enemy targets. 3.1.1 TechSpec Organization Right at the outset, our goal is to embed enough transperancy in the TechSpecs to allow the generation of models (queueing models). Hence, we extract the input/output behavior, state, actions and QoS for each entity within CPE and form the following categories within the TechSpecs : • Internal State of an Agent: Corresponds to continously updated variables or data structures corresponding to the actual working of the agent. • Inputs: Relates to distinct classes of information received or sent to or from an agent respectively. • Outputs: Information provided to other agents. 58
• Actions: Determines the actions that need to be taken as a result of state changes or the dependencies introduced by input/output operations. • Operating Modes: The fidelity or the rate at which outputs are sent may relate to the operating mode of an agent. Switching operating modes may be necessary to alter QoS requirements or as counter-measure for stress. • QoS Measurement (QoS Measurement Points): Indicates the measure of performance that needs to be monitored or measured in order to compute the QoS at the designated measurement point. For example, when we consider queueing models, we would be interested in measuring the average waiting times at different agents to compute a quantity such as the freshness of the maneuver plan. Table 1. TechSpec Categories: Perspective Application • Tradeoffs: While these may not pertain to every agent, some agents have the capability to trade-off a certain measure of performance to gain another. These are specified explicitly in TechSpecs. This categorization facilitates the delineation of specific flows of jobs between agents. For example, consider the following flow: External stimuli at CPY gets converted to update tasks at CPY, delivered to BN as updates, converted to a manueuver plan at BN, delivered to CPY and then forwarded to SUPP for sustainment. From a queueing theory perspective, the update tasks that originates at CPY and end up at BN for the purpose of planning could constitute a class of traffic with CPY and BN acting as servers to process these tasks. Similarly, consider the flow where external stimuli received at CPY end up as updates at BDE through BN. This could be regarded as another class of traffic. At this point it is important to notice that classes of traffic could be derived form the input/output details embedded within TechSpecs. We decribe how we handle these flows in the queueing network formulation in Section 4. Another example of how we could describe something in the application domain (say a QoS metric) with the queueing model is as follows. If one is interested in how fresh a maneuver plan is at its usage point (i.e. CPY), the model could describe it in terms of the queueing delays for a particular class of traffic. In our application, this very quantity happens to be a QoS metric called manuever plan freshness. In the actual MAS, this metric is calculated directly from the timestamps that are tagged to the tasks. 3.1.2 TechSpec Representation Although an elaborate discussion of the format of TechSpec representation is outside the scope of this paper, we present some aspects of the specification directly relating to the application and some infrastructural requirements that need to be part of the specification. Table 1 represents some TechSpecs categories specific to this application. Simply speaking, this is a tabular representation of the information contained in Section 3.1 organized using the aforementioned categories. From Table 1 one can understand that an output called update originates from CPY agent and travels up at BN because BN is CPY’s superior. Similarly, an output called maneuver plan would reach CPY from BN. One assumption that is being made here is that updates travel up the hierarchy and plans downward. These outputs form part of the different classes of traffic if observed from a queueing perspective. Another example would be that the plan action in the BN agent relates to a functionality in the MAS domain and would simply be abstracted by a processing time in the queueing domain. In addition to the above specification, static requirements of the agents in terms of infrastructure are also embedded into TechSpecs. Some of these requirements for BDE, BN, CPY and BDE agents shown in Table 2. 3.2 Translating TechSpecs to the Queueing Domain In order to translate the specs into queueing models we first use the following rules: 1. Inputs and outputs are regarded as tasks; 2. The rate at which external stimuli are received is captured by the arrival rate(λ); 3. Actions take time to perform so they get abstracted by processing times(µ i ); 59
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Executive Summary Ultra*Log is a De
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component is a member of one of the
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Page 160 and 161: Understanding Agent Societies Using
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Page 176 and 177: Acknowledgements The work described
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Page 200 and 201: ealizable. But the inherent complex
Page 202 and 203: Min, H. and Zhou, G., 2002, Supply
Page 204 and 205: In the rest of this paper we report
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