15,099 research outputs found
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems
This paper presents a human-robot trust integrated task allocation and motion
planning framework for multi-robot systems (MRS) in performing a set of tasks
concurrently. A set of task specifications in parallel are conjuncted with MRS
to synthesize a task allocation automaton. Each transition of the task
allocation automaton is associated with the total trust value of human in
corresponding robots. Here, the human-robot trust model is constructed with a
dynamic Bayesian network (DBN) by considering individual robot performance,
safety coefficient, human cognitive workload and overall evaluation of task
allocation. Hence, a task allocation path with maximum encoded human-robot
trust can be searched based on the current trust value of each robot in the
task allocation automaton. Symbolic motion planning (SMP) is implemented for
each robot after they obtain the sequence of actions. The task allocation path
can be intermittently updated with this DBN based trust model. The overall
strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
automata
Fair task allocation in transportation
Task allocation problems have traditionally focused on cost optimization.
However, more and more attention is being given to cases in which cost should
not always be the sole or major consideration. In this paper we study a fair
task allocation problem in transportation where an optimal allocation not only
has low cost but more importantly, it distributes tasks as even as possible
among heterogeneous participants who have different capacities and costs to
execute tasks. To tackle this fair minimum cost allocation problem we analyze
and solve it in two parts using two novel polynomial-time algorithms. We show
that despite the new fairness criterion, the proposed algorithms can solve the
fair minimum cost allocation problem optimally in polynomial time. In addition,
we conduct an extensive set of experiments to investigate the trade-off between
cost minimization and fairness. Our experimental results demonstrate the
benefit of factoring fairness into task allocation. Among the majority of test
instances, fairness comes with a very small price in terms of cost
Task allocation in a distributed computing system
A conceptual framework is examined for task allocation in distributed systems. Application and computing system parameters critical to task allocation decision processes are discussed. Task allocation techniques are addressed which focus on achieving a balance in the load distribution among the system's processors. Equalization of computing load among the processing elements is the goal. Examples of system performance are presented for specific applications. Both static and dynamic allocation of tasks are considered and system performance is evaluated using different task allocation methodologies
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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
Psychological Issues in Online Adaptive Task Allocation
Adaptive aiding is an idea that offers potential for improvement over many current approaches to aiding in human-computer systems. The expected return of tailoring the system to fit the user could be in the form of improved system performance and/or increased user satisfaction. Issues such as the manner in which information is shared between human and computer, the appropriate division of labor between them, and the level of autonomy of the aid are explored. A simulated visual search task was developed. Subjects are required to identify targets in a moving display while performing a compensatory sub-critical tracking task. By manipulating characteristics of the situation such as imposed task-related workload and effort required to communicate with the computer, it is possible to create conditions in which interaction with the computer would be more or less desirable. The results of preliminary research using this experimental scenario are presented, and future directions for this research effort are discussed
Profitable Task Allocation in Mobile Cloud Computing
We propose a game theoretic framework for task allocation in mobile cloud
computing that corresponds to offloading of compute tasks to a group of nearby
mobile devices. Specifically, in our framework, a distributor node holds a
multidimensional auction for allocating the tasks of a job among nearby mobile
nodes based on their computational capabilities and also the cost of
computation at these nodes, with the goal of reducing the overall job
completion time. Our proposed auction also has the desired incentive
compatibility property that ensures that mobile devices truthfully reveal their
capabilities and costs and that those devices benefit from the task allocation.
To deal with node mobility, we perform multiple auctions over adaptive time
intervals. We develop a heuristic approach to dynamically find the best time
intervals between auctions to minimize unnecessary auctions and the
accompanying overheads. We evaluate our framework and methods using both real
world and synthetic mobility traces. Our evaluation results show that our game
theoretic framework improves the job completion time by a factor of 2-5 in
comparison to the time taken for executing the job locally, while minimizing
the number of auctions and the accompanying overheads. Our approach is also
profitable for the nearby nodes that execute the distributor's tasks with these
nodes receiving a compensation higher than their actual costs
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