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About 10,000 papers for “task allocation”

Task Allocation

Kim Sarnoff · AEA Randomized Controlled Trials · 2023

Cited by 0

Adversarial Task Allocation

Chen Hajaj, Yevgeniy Vorobeychik · arXiv (Cornell University) · 2017

The problem of allocating tasks to workers is of long standing fundamental importance. Examples of this include the classical problem of assigning computing tasks to nodes in a distributed computing environment, as well as the more recent problem of crowdsourcing where a broad array of tasks are slated to be completed by human workers. Extensive research into this problem generally addresses important issues such as uncertainty and, in crowdsourcing, incentives. However, the problem of adversarial tampering with the task allocation process has not received as much attention. We are concerned with a particular adversarial setting in task allocation where an attacker may target a specific worker in order to prevent the tasks assigned to this worker from being completed. We consider two attack models: one in which the adversary observes only the allocation policy (which may be randomized), and the second in which the attacker observes the actual allocation decision. For the case when all tasks are homogeneous, we provide polynomial-time algorithms for both settings. When tasks are heterogeneous, however, we show the adversarial allocation problem to be NP-Hard, and present algorithms for solving it when the defender is restricted to assign only a single worker per task. Our experiments show, surprisingly, that the difference between the two attack models is minimal: deterministic allocation can achieve nearly as much utility as randomized.

Cited by 0Open access

Adaptive Task Allocation

Skye Taylor · ODU Digital Commons (Old Dominion University) · 2021

Adaptive task allocation is used in many human-machine systems and has been proven to improve operator’s monitoring and/or performance with automated systems. However, there is little knowledge surrounding the benefits of adaptive task allocation in automated vehicles. In this study, participants will be presented with media depicting driving scenarios with either low or high workload. The participants will report which tasks they feel comfortable allocating to themselves or the automated system in each driving scenario, as well as when they would conduct the manual task allocation. It is hypothesized that participants will assign a greater number of primary driving tasks, such as steering, to themselves when presented with depictions of low workload driving environments. When a high workload environment is depicted, participants are expected to allocate primary driving tasks to the vehicle. It is also expected that participants will conduct manual task allocation during periods where workload is low.

Cited by 1Open access

Task allocation in supersystems

Efe, K., Leeds Univ. (UK). Dept. of Computer Studies · OpenGrey (Institut de l'Information Scientifique et Technique) · 1983

Cited by 0

Scheduling and Task Allocation

Hesham El‐Rewini, Mostafa Abd‐El‐Barr · 2004

After a computational job is designed and realized as a set of tasks, an optimal assignment of these tasks to the processing elements in a given architecture needs to be determined. This problem is called the scheduling problem and known to be one of the most challenging problems in parallel and distributed computing. The goal of scheduling is to determine an assignment of tasks to processing elements in order to optimize certain performance indexes. Performance and efficiency are two characteristics used to evaluate a scheduling system. We should evaluate a scheduling system based on the quality of the produced task assignment (schedule) and the efficiency of the scheduling algorithm (scheduler). The produced schedule is judged based on the performance criterion to be optimized, while the scheduling algorithm is evaluated based on its time complexity. For example, if we try to optimize the completion time of a program, the less the completion time, the better the schedule will be. Also, if two scheduling algorithms produce task assignments that have the same quality, the less complex algorithm is clearly the better. The scheduling problem in known to be computationally intractable in many cases. Fast optimal algorithms can only be obtained when some restrictions are imposed on the models representing the program and the distributed system. Solving the general problem in a reasonable amount of time requires the use of heuristic algorithms. These heuristics do not guarantee optimal solutions to the problem, but they attempt to find near optimal solutions. This chapter addresses the scheduling problem in many of its variations. We survey a number of solutions to this important problem. We cover program and system models, optimal algorithms, heuristic algorithms, scheduling versus allocation techniques, and homogeneous versus heterogeneous environments.

Cited by 2

Operator/system task allocation

Stephen J. Andriole · 2002

Some strategies for allocating tasks across the human and electronic components of a system are explored. There exist the questions of what human operators should do and what should be left to semi- and fully-autonomous electronic systems. It is argued here that the allocation strategy must be based on: (a) the nature of the tasks to be performed by the system, (b) the situational milieu in which the system will operate, and (c) the power of advanced analytical methods. The author indicates that there are some relatively straightforward steps that can be taken for the optimal allocation of tasks across human and electronic or software-intensive system components.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 0

Multi-Robot Task-Allocation . . .

Torbjørn S. Dahl, Maja J. Matarić, Gaurav S. Sukhatme · 2003

This paper presents an algorithm for task allocation in groups of homogeneous robots. The algorithm is based on vacancy chains, a resource distribution strategy common in human and animal societies. We define a class of task-allocation problems for which the vacancy chain algorithm is suitable and demonstrate how Reinforcement Learning can be used to make vacancy chains emerge in a group of Behavior-Based robots. Experiments in simulation show that the vacancy chain algorithm consistently outperforms random and static task allocation algorithms when individual robots are prone to distractions or breakdowns, or when task priorities change.

Cited by 0

Mechanism Reform for Task Allocation

E. Jason Baron, Richard Lombardo, Joseph P. Ryan et al. · RePEc: Research Papers in Economics

Reforming an existing system for allocating tasks among agents introduces additional political and institutional constraints relative to designing one in isolation. We develop a general mechanism-design framework for using data on agents’ performance to improve outcomes while ensuring that no agents are made worse off relative to the status quo. As an illustration, we apply our results to the assignment of Child Protective Services investigators to maltreatment cases. Simulations show the mechanism reduces false positives (unnecessary foster care placements) by up to 14% while also lowering false negatives (missed maltreatment cases) and overall placements.

Cited by 0

Task Allocation in Server Farms

J. G. Dai, J. Michael Harrison · Cambridge University Press eBooks · 2020

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Cited by 0

An Optimized Multirobot Task Allocation

Bibhuti Bhusan Choudhury, Bibhuti Bhusan Biswal · 2008

Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. One of the most important aspects in the design of MRS is the allocation of tasks among the robots in a productive and efficient manner. Optimal solutions to multirobot task allocation (MRTA) can be found through an exhaustive search. Since there are ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible. Task allocation methodologies must ensure that not only the global mission is achieved, but also the tasks are well distributed among the robots. This paper presents task allocation methodologies for MRS by considering their capability in terms of time and space. A two-phase solution methodology is used to solve the MRTA problem wherein the task capacity of the robots is determined during the first phase and the task allocation optimization is done during the second phase using linear programming (LP).

Cited by 3

Designing for dynamic task allocation

van Dongen, K., van Maanen, P.P. · Data Archiving and Networked Services (DANS) · 2005

Future platforms are envisioned in which human-machine teams are able to share and trade tasks as demands in situations change. It seems that human-machine coordination has not received the attention it deserves by past and present approaches to task allocation. In this paper a simple way to make coordination requirements explicit is proposed and for dynamic task allocation a dual-route approach is suggested. Advantages of adaptable automation, in which the human adjusts the way tasks are divided and shared, are complemented with those of adaptive automation, in which the machine allocates tasks. To be able to support design for dynamic task allocation, a theory about task allocation decision making by means of modeling of trust is proposed. It is suggested that dynamic task allocation is improved when information about situational abilities of agents is provided and the cost of observing and re-directing agents is reduced.

Cited by 0Open access