Task (computing)

About: Task (computing) is a research topic. Over the lifetime, 9718 publications have been published within this topic receiving 129364 citations.

The role of planning in Grid computing

Abstract: Grid computing gives users access to widely distributed networks of computing resources to solve large-scale tasks such as scientific computation. These tasks are defined as standalone components that can be combined to process the data in various ways. We have implemented a planning system to generate task workflows for the Grid automatically, allowing the user to specify the desired data products in simple terms. The planner uses heuristic control rules and searches a number of alternative complete plans in order to find a high-quality solution. We describe an implemented test case in gravitational wave interferometry and show how the planner is integrated in the Grid environment. We discuss promising future directions of this work. We believe AI planning will play a crucial role in developing complex application workflows for the Grid.

Method for compiling a master task definition data set for defining the logical data flow of a distributed processing network

Abstract: A compiler method is disclosed which defines a data flow for a specific complex function to be executed on a plurality of data processing elements in a distributed processing system, by means of defining a plurality of control blocks which are associated with each task. The control blocks are in relocatable code so that they may be associated with any one of several similar types of data processing elements within the distributed processing network. The control blocks include local operating system control blocks, token control, post and wait control blocks, and processing element task execution control blocks. The use of the distributed processing system compiler method enables the quick and easy implementation of complex functions having interdependent processing tasks in a distributed processing system.

Coalition formation with spatial and temporal constraints

Abstract: The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP). We show that this problem is NP-hard---in particular, it contains the well-known complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.

Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training

Abstract: Learning to navigate in a visual environment following natural-language instructions is a challenging task, because the multimodal inputs to the agent are highly variable, and the training data on a new task is often limited. In this paper, we present the first pre-training and fine-tuning paradigm for vision-and-language navigation (VLN) tasks. By training on a large amount of image-text-action triplets in a self-supervised learning manner, the pre-trained model provides generic representations of visual environments and language instructions. It can be easily used as a drop-in for existing VLN frameworks, leading to the proposed agent called Prevalent. It learns more effectively in new tasks and generalizes better in a previously unseen environment. The performance is validated on three VLN tasks. On the Room-to-Room benchmark, our model improves the state-of-the-art from 47% to 51% on success rate weighted by path length. Further, the learned representation is transferable to other VLN tasks. On two recent tasks, vision-and-dialog navigation and "Help, Anna!" the proposed Prevalent leads to significant improvement over existing methods, achieving a new state of the art.