Task (computing)

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

Healthcare communication and workflow management system and method

Abstract: A communication and workflow management system and method is provided for integrating a wide range of health care organization workflow management functions, generated by automated systems, manual and automated events associated with patients and staff interactions, through input-output devices such that requests and dispatch requests can be handled locally or over a widely distributed network, and can be tracked and escalated as required. The invention features a rules engine and database that identifies and defines resources, patients, tasks, and task handling. The invention uses extensive logic for the assignment of tasks and communication with resources that can execute tasks, tracking, completion of task, and escalation of tasks. The communication system can be integrated with staff and equipment tracking for automated closure of tasks.

Things are made for what they are: Solving manipulation tasks by using functional object classes

Abstract: Solving arbitrary manipulation tasks is a key feature for humanoid service robots. However, especially when tasks involve handling complex mechanisms or using tools, a generic action description is hard to define. Different objects require different handling methods. Therefore, we try to solve manipulation tasks from point of view of the object, rather than in the context of the robot. Action templates within the object context are introduced to resolve object specific task constraints. As part of a centralized world representation, the action templates are integrated into the planning process. This results in an intuitive way of solving manipulation tasks. The underlying architecture as well as the mechanisms are discussed within this paper. The proposed methods are evaluated in two experiments.

Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks.

Abstract: In general, neural networks are not currently capable of learning tasks in a sequential fashion When a novel, unrelated task is learnt by a neural network, it substantially forgets how to solve previously learnt tasks One of the original solutions to this problem is pseudo-rehearsal, which involves learning the new task while rehearsing generated items representative of the previous task/s This is very effective for simple tasks However, pseudo-rehearsal has not yet been successfully applied to very complex tasks because in these tasks it is difficult to generate representative items We accomplish pseudo-rehearsal by using a Generative Adversarial Network to generate items so that our deep network can learn to sequentially classify the CIFAR-10, SVHN and MNIST datasets After training on all tasks, our network loses only 167% absolute accuracy on CIFAR-10 and gains 024% absolute accuracy on SVHN Our model's performance is a substantial improvement compared to the current state of the art solution

Pick and Place Operations in Logistics Using a Mobile Manipulator Controlled with Deep Reinforcement Learning

Abstract: Programming robots to perform complex tasks is a very expensive job. Traditional path planning and control are able to generate point to point collision free trajectories, but when the tasks to be performed are complex, traditional planning and control become complex tasks. This study focused on robotic operations in logistics, specifically, on picking objects in unstructured areas using a mobile manipulator configuration. The mobile manipulator has to be able to place its base in a correct place so the arm is able to plan a trajectory up to an object in a table. A deep reinforcement learning (DRL) approach was selected to solve this type of complex control tasks. Using the arm planner’s feedback, a controller for the robot base is learned, which guides the platform to such a place where the arm is able to plan a trajectory up to the object. In addition the performance of two DRL algorithms ((Deep Deterministic Policy Gradient (DDPG)) and (Proximal Policy Optimisation (PPO)) is compared within the context of a concrete robotic task.

Assigned tasks are not the same as self-chosen Web search tasks

Abstract: Short assigned question-answering style tasks are often used as a probe to understand how users do search. While such assigned tasks are simple to test and are effective at eliciting the particulars of a given search capability, they are not the same as naturalistic searches. We studied the quantitative differences between assigned tasks and self-chosen "own" tasks finding that users behave differently when doing their own tasks, staying longer on the task, but making fewer queries and different kinds of queries overall. This finding implies that user's own tasks should be used when testing user behavior in addition to assigned tasks, which remain useful for feature testing in lab settings