Task Allocation For Teams Research Articles

Novel hybrid algorithm for Team Orienteering Problem with Time Windows for rescue applications

Robots for rescue operations after a disaster are an interesting and challenging research problem that has the potential to save lives and reduce economic losses after a disaster. We developed TOPTWR, an extension of the popular TOPTW model, to model the issues in task allocation for teams of rescue robots. Our hybrid algorithm is based on a team of heterogeneous humanoid robots trying to optimize five objectives (task rewards, task completion time, total energy, maximum energy consumption for a single robot, and missed deadline penalties). A common approach to solve these kinds of problems are multi-objective evolutionary algorithms (MOEAs), but their major disadvantage is that they cannot deal with dynamic environments easily. This paper presents an efficient solution for TOPTWR by combining MOEAs with learning algorithms. A novel Extended Multi-Start Simulated Annealing Iterated Local Search (EMSAILS) operator using a modern state-of-the-art NSGA-III algorithm is proposed. In addition, we applied Q-Learning to learn the likely changes in the environment and how to react to them. This algorithm, HMO-TOPTWR-NSGA-III (HMO-N-L), uses an artificial neural network (ANN) as a function approximator to make the huge state and action spaces tractable. This paper includes a thorough empirical evaluation demonstrating the effectiveness of the multi-objective algorithm in both static and dynamic environments. The evaluation shows that the proposed algorithm reduces the error by up to 42% against three state-of-the-art approaches to TOPTW (HMO-N, MSA, and IPI).

Improving Multi-Robot Coordination by Game-Theoretic Learning Algorithms

Cooperative games-based robot cooperation is analysed for reoccurring scenarios. It is shown that potential games can be used for robot coordination when the robots have a shared objective. By observing each others’ behaviour in similar scenarios, they estimate each other’s expected actions, which they use for their own choice of action. The resulting learning scheme can enable “tuning” of smooth cooperation by task allocation in teams of robots for various goals and in reoccurring scenarios of their environment. The theoretical results and methods are illustrated in simulation.

Leader Humility and Team Performance: Exploring the Mediating Mechanisms of Team PsyCap and Task Allocation Effectiveness

Although there is a growing interest toward the topic of leader humility, extant research has largely failed to consider the underlying mechanisms through which leader humility influences team outcomes. In this research, we integrate the emerging literature of leader humility and social information processing theory to theorize how leader humility facilitates the development of collective team psychological capital, leading to higher team task allocation effectiveness and team performance. While Owens and Hekman (2016) suggest that leader humility has homogeneous effects on followers, we propose a potential heterogeneous effect based on the complementarity literature (e.g., Tiedens, Unzueta, & Young, 2007) and the principle of equifinality (leaders may influence team outcomes through multiple pathways; Morgeson, DeRue, & Karam, 2010). In three studies conducted in China, Singapore, and Portugal, including an experiment, a multisource field study, and a three-wave multisource field study, we find support for our hypotheses that leader humility enhances team performance serially through increased team psychological capital and team task allocation effectiveness. We discuss the theoretical implications of our work to the leader humility, psychological capital, and team effectiveness literatures; and offer suggestions for future research.

Who is responsible for what tasks within primary care: Perceived task allocation among primary care providers and interdisciplinary team members

Background: Unclear roles in interdisciplinary primary care teams can impede optimal team-based care. We assessed perceived task allocation among primary care providers (PCPs) and staff during implementation of a new patient-centered care model in Veterans Affairs (VA) primary care practices. Methods: We performed a cross-sectional survey of PCPs and primary care staff (registered nurses (RNs), licensed practical/vocational nurses (LPNs), and medical assistants/clerks (MAs)) in 23 primary care practices within one VA region. We asked subjects whether PCPs performed each of 14 common primary care tasks alone, or relied upon staff for help. Tasks included gathering preventive service history, disease screening, evaluating patients and making treatment decisions, intervening on lifestyle factors, educating patients about self-care activities and medications, refilling prescriptions, receiving and resolving patient messages, completing forms, tracking diagnostic data, referral tracking, and arranging home health care. We then performed multivariable regression to determine predictors of perceived PCP reliance on staff for each task. Results: 162 PCPs and 257 staff members responded, a 60% response rate. For 12/14 tasks, fewer than 50% of PCPs reported relying on staff for help. For all 14 tasks, over 85% of RNs reported they were relied upon. For 12/14 tasks, over 50% of LPNs reported they were relied on, while for 5/14 tasks a majority of MAs reported being relied upon. Nurse practitioners and physician assistants (NP/PAs) reported relying on staff less than physicians. Conclusions: Early in the implementation of a team-based primary care model, most PCPs perceived they were solely responsible for most clinical tasks. RNs, and LPNs felt they were relied upon for most of the same tasks, while medical assistants/clerks reported being relied on for fewer tasks. Better understanding of optimal inter-professional team task allocation in primary care is needed.

Team task allocation method for computer generated actor based on game theory

For the complex tasks with time constraints,which can dynamically be added to environment,a task allocation model based on game theory was established,and a task allocation method was proposed,which made Computer Generated Actor(CGA) be able to choose its actions according to the local information owned by itself,and ensured that CGA learned a strict pure strategy Nash equlilibrium quickly by using fictitious play method on behavior coordination.The simulation results show that this method is reasonable,and it can effectively solve the dynamic task allocation problem.

Social networks in simulated multi‐robot environment

PurposeThe purpose of this paper is to present a novel approach to coordination of multi‐agent teams, and in particular multi‐robot teams. The new approach is based on models of organisational sociology, namely the concept of social networks. The social relationships used in the model that is presented in this paper are trust and kinship relationships, but modified for use in heterogeneous multi‐robot teams.Design/methodology/approachThe coordination of a robot team is achieved through task allocation. The proposed task allocation mechanism was tested in the multi‐robot team task allocation simulation.FindingsThe social networks‐based task allocation algorithm has performed according to expectations and the obtained results are very promising. Some intriguing similarities with higher mammalian societies were observed and they are discussed in this paper. The social networks‐based approach also exhibited the ability to learn and store information using social networks.Research limitations/implicationsThe research focused on simulated environments and further research is envisaged in the physical environments to confirm the applicability of the presented approach.Practical implicationsIn this paper, the proposed coordination was applied to simulated multi‐robot teams. It is important to note that the proposed coordination model is not robot specific, but can also be applied to almost any multi‐agent system without major modifications.Originality/valueThe paper emphasizes applicability of considering multi‐robot teams as socially embodied agents. It also presents a novel and efficient approach to task allocation.