Van Parunak - Academia.edu (original) (raw)

Papers by Van Parunak

All in-text references underlined in blue are linked to publications on ResearchGate, letting you... more All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.

Sven Brueckner, Van Parunak Altarum Institute RAPSIDY (Resource Allocation Problem Solver Incorpo... more Sven Brueckner, Van Parunak Altarum Institute RAPSIDY (Resource Allocation Problem Solver Incorporating Dynamics) is a repair solver based on some of the concepts developed by the Altarum team during the ANT program. By “repair solver” we mean one that begins with an existing schedule and a set of new constraints and attempts to deliver a scheduled that incorporates the new constraints with minimal change to the previous schedule. RAPSIDY interacts with the ISI SNAP system (Figure 1). It receives the definition of the current scheduling problem as well as user preferences and requirements and it delivers a set of assignments that (partially) solve the problem under the given constraints. A scheduling problem as presented by SNAP (Figure 2) comprises a set of resources (pilots, planes, ranges, simulators, ...) and a set of tasks (missions). Tasks specify a number of requirements that all have to be met to fulfill the task. Requirements provide the constraints on the eligibility of re...

Proceedings of the second international joint conference on Autonomous agents and multiagent systems - AAMAS '03, 2003

International Journal of Geographical Information Science, 2006

Autonomous Agents and Multi-Agent Systems, 2006

2nd AIAA "Unmanned Unlimited" Conf. and Workshop & Exhibit, 2003

Lecture Notes in Computer Science, 2006

Competitive pressures are moving manufacturers toward shorter product cycles, lower inventories, ... more Competitive pressures are moving manufacturers toward shorter product cycles, lower inventories, higher equipment utilization, and shorter lead times. As a result, the problem of scheduling and controlling the shop floor grows in importance. Manufacturing scheduling and control has traditionally been viewed as a top-down process of command and response that relies heavily on hierarchical models of the manufacturing enterprise. This white paper suggests a different perspective, one that focuses on the local autonomy of workstations, parts, and operators, without sacrificing the predictability required in a business context. Such an approach will be more robust and easier to modify than conventional centralized systems, and new technologies promise to make it manageable as well.

Many manufactured systems (both consumer goods and manufacturing systems) consist of a number of ... more Many manufactured systems (both consumer goods and manufacturing systems) consist of a number of discrete subsystems and components that interact with one another through various interfaces to provide the required functionality. Designing such a system requires finding values for interface variables that are compatible among the various components, and is analogous to the constraint optimization problem, with subsystems and components playing the role of constraints among the variables. Today’s business environment requires design to be done by distributed teams of engineers, so the analogy can be extended to distributed constraint optimization (DCOP). This paper develops the parallel between distributed component-centered design (DCCD) and DCOP, discusses the particular flavor that industrial requirements impart to the mapping, and reports how this parallel is being exploited in the RAPPID system for agent-based distributed design,

: Most social network modeling tools are descriptive.That is, they focus on representing and visu... more : Most social network modeling tools are descriptive.That is, they focus on representing and visualizing entities and relationships given historic data. While such representations are valuable for understanding the structure of organizations and groups, they leave the process of projecting the entities future actions within the context of the network entirely to the user. Building on technologies from DARPAs RAID and COMPOEX programs, we are working to fill this gap by developing a system to aid analysts in projecting future activities in space, time, and socio-political dimensions. Our approach uses a generative model of a social network, related physical processes, and the environment (a geo-spatial model). The nodes (entities) in our networks have goals, resources and locations. They act, using their resources, to achieve their goals. These models generate estimates of future behavior using faster than real time simulation within the network, process, and geo-spatial models. Usin...

Integrated Computer-Aided Engineering