Agent Applications in Defense Logistics
4. The Adaptive Logistics Project
Figure 9. Functional and Management Agents in the Experi- mental Baseline.
3.4.3. Survivability Findings. Because evaluation was a key aspect of the UltraLog program, the program employed several independent Red Teams. In the annual evaluation, the development team would stand up the experimental testbed and the Red Team would employ various techniques to attempt to compromise the operation of the system. Prior to the evaluation, the read team was given full access to the source code, configuration, topology, and other aspects of the system operation. The attacks and stresses were applied in categories, with the results of the system performance being assigned a value from 1 to 10, where 1 was poorest and 10 was best performance against the attack. A value of 0 was assigned where no survivability measures existed and no evaluations were performed. Fig.
10 shows the survivability findings of the evaluations for the UltraLog testbed over the course of the program. By the end of the program, few of the stresses had more than a mild impact on the operation of the system and no attacks were regularly and completely successful in bringing the operation of the testbed below the defined minimal operational level.
Figure 10. UltraLog Survivability Findings.
Adaptive Logistics requires: (1) a network-centric environment that connects logisticians with operations and intelligence to interpret the commander’s intent in terms of missions, environment, desired outcomes, and priorities; (2) the capability to create adaptive Communities of Interest (COIs) that are responsible for plan- ning for, executing, and monitoring logistics responses; (3) analytics that generate alternative courses of action, their feasibility in terms of resource requirements and availability, and the consequent risks for each alternative; and (4) cognitive decision support tools that weigh the factors driving decisions within the cycles of decision makers from the point-of-effect through the strategic base. These re- quirements were largely satisfied by the Cougaar architecture, but lacked support of the shared situation representation and reasoning.
4.1. Situational Understanding as the Basis of Optimized Planning
The objective of the ALCT project was to provide key aspects of the Sense and Respond Logistics (S&RL) vision in a manner that, for a set of meaningful ques- tions, creates situational understanding and actionable information where none previously existed. The ALCT dynamically constructs a sourcing and distribu- tion plan for in theater resourcing of material requests. It builds and maintains a theater wide situational picture of supply, transportation and routing, as shown in Fig. 11, and uses that understanding to find feasible solution sets. From that set, the system chooses the best solution given the current policies and establishes that as sourcing and distribution plan for that requisition, coordinating the solu- tion with all the units involved. As shown in Fig. 11, Unit demand requisitions (1) flow up to the (2) Theater Support Command (TSC). Requisitions are then
Figure 11. The ALCT Theater Agent Planning Network.
analyzed through an optimization algorithm for the “best value” supplier (SSA) (3) and the transportation unit (4) within theater. Once the transportation and SSA is known we find (5) the optimal route based on time and cost and other user defined business rules and weightings. Any requisitions not serviced in-theater will be put back into the normal process and sourced from CONUS. By tying every component of the plan into the situation, events that impact a required resource of that plan - road segment, delivery truck, etc - can immediately be linked back to the affected plans for notification and dynamic replanning. The situational picture is a virtual construct composed of a series of community networks: units, supply, transportation, routing, etc. Each network is composed of the units performing those functions, using the power of distributed intelligent agent technology to re- alize local decision support and local situational representations for each unit in theater. At organizations like the TSC, elements of the individual situational pic- tures and networks are combined to form the aggregate state of the theater and performance of ongoing operations.
4.2. ALCT Demonstrated Capabilities
The ALCT project demonstrated a core set of theater level capabilities that would significantly improve theater planning and operations. These capabilities included:
• Develop a Theater Level Distribution Plan:Agents develop real time distribu- tions plans across Consumer, Supply Support, & Transportation components, using agents to negotiate performance, resource allocation and schedule pa- rameters. The plan is then used to track execution and monitor for impacting events, which under certain circumstances trigger dynamic replanning.
• Maintain Asset Visibility of Equipment and Supplies:Agents manage assets and maintain asset visibility at the item level, identifying shortages, excesses, trends, allocations and current location.
• Transportation, Maintenance and Supply: Agents monitoring the situation maintain current unit location, monitor transportation movement and sched- ules and track overall execution performance.
• Ability to Monitor Performance by Theater Level Metrics: Agents perform analytics on the historical, current and projected situation information deriv- ing performance metrics which are used to flag problem areas and recommend adjustments to the operational policies to improve performance.
4.3. ALCT Situation Reasoning Agents
The ALCT project was developed on a commercial version of the Cougaar archi- tecture developed by Cougaar Software, Inc. called ActiveEdge. ActiveEdge [8]
is a commercial development platform that provides all the capabilities of Cougaar described in Section 6, as well as a variety of additional capabilities like a workflow engine, rule engine, device interface layer, advanced visualization environment, in- tegrated Semantics support [7], SOA and JMS support as well as a Situational Reasoning Framework (SRF) subsystem for deep situational reasoning and Dis- tributed Data Environment (DDE) subsystem for advanced dynamic mediation.
The key technical advancement demonstrated by the ALCT effort was the abil- ity to build and maintain a large scale, complex situational picture composed of individual local situational pictures maintained at the unit level. The higher eche- lons composed the elements of the lower echelon situational pictures to create the composites. The actual composition and analysis was done by a special class of agents known as situational reasoning agents which used a variety of correlation, reasoning and pattern filtering to update the situational picture as new informa- tion flowed in. ALCT demonstrated that this approach, tuned to the data sets and functional reasoning appropriate for in-theater logistics, was effective against theater scale data sets with reasonable performance.