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AMC
Allocator Barrel Allocator has been developed as part of the Advanced Automated Scheduling (AAS) component of the CAMPS development effort, which is aimed specifically at applying and transitioning new scheduling technologies developed within the DARPA/RL Planning Initiative. The Barrel Allocator relies on incremental, constraint-based scheduling techniques. This allows selective re-optimization of allocation decisions to accommodate new, higher priority missions while minimizing disruption to most previous assignments. Mission scheduling and resource allocation capabilities can be invoked in automated or semi-automated modes. In the latter case, the system generates and compares different options that might be taken. Planners interact with Barrel Allocator through graphical displays, which incorporate mission-oriented, resource-resource and map-based views of the current set of commitments.[MORE] |
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COMIREM:
Web-based Planning and Scheduling Services The explosion of the Web in recent years has increased both the opportunity and the need for collaborative planning and scheduling tools. On one hand it has provided an infra-structure that offers unprecedented potential for putting planning and scheduling capabilities in the hands of mobile and physically distributed decision-making agents. Planning and scheduling tools and technologies can now be coupled with widely accessible browser interfaces and increasingly standardized data formats and application communication protocols. On the other hand, the emergence of this technological infrastructure has placed increased emphasis on dynamic, real-time tracking of execution status and on incremental, distributed management of plans/schedules as circumstances evolve. Unfortunately, current planning and scheduling tools provide little support for these capabilities.[MORE] |
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COORDINATORS The COORDINATORS program defines a challenging multi-agent application, with agents operating in a highly dynamic environment, where no agent has a complete view of the problem. Unexpected execution events require the agents to coordinate and respond quickly, by adjusting their schedules to match the evolving execution circumstances, in a way that continues to maximize the quality of their joint activities. We implemented an incremental scheduling framework designed to support the joint management of inter-dependent schedules. Our approach combines an underlying flexible-times representation of the schedule, which exploits temporal flexibility to absorb executional uncertainty and insulate dependencies across agent schedules, with an incremental approach to schedule revision, which promotes solution stability and tends to minimize the ripple effect of change across agent schedules.[MORE] |
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MKIDS Knowledge-intensive dynamic systems (KIDS) present complex scheduling and coordination problems. KIDS refer generally to large-scale, multi-actor systems that plan and execute production processes with the following characteristics: (1) they principally involve the collection, manipulation and management of knowledge products, (2) they exhibit perpetual novelty in process structure, and (3) they are unpredictable in their outcomes and require continual dynamic adjustment and revision. The establishment of a schedule is crucial to effective management and control of KIDS; it is the means by which global coordination of executing agents (actors) is achieved and maintained. Attention must be paid to the cost of continual solution change and to maintaining continuity in currently executing processes, as evolving requirements, changing priorities and new resource availability constraints continually force changes to previously planned tasks and resource assignments. Like many other practical domains, KIDS are further complicated by the presence of multiple performance objectives, the need to optimize under complex (and often idiosyncratic) constraints and the need for flexible human decision-making involvement.[ MORE] |
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Profile-based
Scheduling Procedures Research in constraint-based scheduling has typically formulated the problem as one of finding a consistent assignment of start times for each goal activity. In contrast, we are investigating approaches to scheduling that operate with a problem formulation more akin to least-commitment planning frameworks, where the goal is to post sufficient additional precedence constraints between pairs of activities contending for the same resources to ensure feasibility with respect to time and capacity constraints. Solutions generated in this way generally represent a set of feasible schedules (i.e., the sets of activity start times that remain consistent with posted sequencing constraints), as opposed to a single assignment of start times.[MORE] |
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CMRADAR People spend a significant amount of their time scheduling events. For instance, arranging even a simple two-person meeting requires at least three messages, e.g., proposal - acceptance - confirmation, in the best case scenario, and many more in less than optimal scenarios. The primary goal of CMRadar is to free up busy users from such mundane tasks by providing a complete end-to-end calendar management that can autonomously handle scheduling tasks with intelligence. CMRadar utilizes statistical learning algorithms to learn its user's scheduling preferences. At the same time, it also learns models of other meeting parcitipants through interactions so that it can choose more efficient negotiation strategies in the future.[MORE] |
