Two-stage Bilateral Online Priority Assignment in Spatio-temporal Crowdsourcing

An essential quality of a good partner is her responsiveness to other team members. Recent work in dynamic plan execution exhibits elements of this quality through the ability to adapt to the temporal uncertainties of others agents and the environment. However, a good teammate also has the ability to adapt on-the-fly through task assignment. We generalize the framework of dynamic execution to perform plan execution with dynamic task assignment as well as scheduling.This paper introduces Chaski, a multi-agent executive for scheduling temporal plans with online task assignment. Chaski enables an agent to dynamically update its plan in response to disturbances in task assignment and the schedule of other agents. The agent then uses the updated plan to choose, schedule and execute actions that are guaranteed to be temporally consistent and logically valid within the multi-agent plan. Chaski is made efficient through an incremental algorithm that compactly encodes all scheduling policies for all possible task assignments. We apply Chaski to perform multi-manipulator coordination using two Barrett Arms within the authors' hardware testbed. We empirically demonstrate up to one order of magnitude improvements in execution latency and solution compactness compared to prior art.

High-performance computing systems typically employ heterogeneous multicore design to improve both execution performance and efficiency. Task assignment is critical in exploiting the diversity of computation capability, energy consumption, as well as communication cost on heterogeneous multicore processors. In this paper, we explore the opportunity of task assignment on heterogeneous multicore processors to minimize execution and communication costs considering time constraint. The general heterogeneous task assignment problem is NP-Complete. However, we find that optimal task assignment can be achieved for widely used, tree-shaped task graphs using dynamic programming. We first propose a dynamic programming algorithm, the Optimal Tree Assign (OTA) algorithm, to generate optimal assignments for trees. Then, we develop the Integer Linear Programming model of the general task assignment problem for Directed Acyclic Graphs. A polynomial-time heuristic, the Extended Tree Assignment algorithm, is also proposed to produce near-optimal solutions for the general heterogeneous task assignment problem efficiently. The experimental results show that the proposed algorithms outperform both homogeneous task assignment method and greedy strategy for all the benchmarks. The OTA algorithm reduces the total system time by 42.5 percent and 23.5 percent on average compared with the homogeneous task assignment method and greedy algorithm, respectively.

An approach to optimal assignment of tasks with precedence relationships to multiple robots is proposed. The robots are assumed to share a common workspace and work cooperatively to accomplish a given process plan consisting of a set of tasks. The optimal task assignment is defined to be the one that results in spending the least amount of time to complete the plan under the criterion that no robot collision will occur when the assigned tasks are performed. The ordering of the tasks in the process plan is described by a topological tree, which is then expanded to form a larger state‐space tree without redundant tree paths. Each path in the expanded tree represents a partially developed assignment of the tasks to the robots, and a graph formulation scheme is presented for estimating the cost of the assignment. A collision‐free motion schedule for each robot based on each task assignment can be obtained by finding the minimaximal path in a disjunctive graph formulated by the scheme. By using the A* algorithm, a search method for finding the optimal assignment with the minimum cost is presented. Some heuristic rules are also proposed to speed up the search process. Simulation results are illustrated to show the effectiveness of the proposed approach. © 1995 John Wiley & Sons, Inc.

This paper addresses task assignment problem fortree-structure task model on heterogeneous multicore embedded systems with time constraint considering both execution time and communication load. The goal is to minimize the total system cost for a given task graph representing a set of tasks and data dependencies in a heterogeneous multicore systemwhile the time constraint is satisfied. Instead of assigning all the tasks to processors of the same type in a homogeneous environment, heterogeneous task assignment usually can reduce the system cost by exploring various capacities and costs in a heterogeneous multicore system. The general heterogeneous assignment problem is NP-complete. In this paper, we show that optimal task assignment can be found for some widely-used, special task graphs, such as tree-structure graph, using dynamic programming. A dynamic programming algorithm, the Tree Assign (TA) algorithm, is proposed in this paper to solve the heterogeneous task assignment problem for tree-structure task graphs. The experimental results show that our algorithm reduces the total system cost by 31.8% compared with assignment results on homogeneous multicore systems. Also, our algorithm achieves an average cost reduction by 21.9% compared with greedy algorithm.

We study an online task assignment problem for multi-robot systems where robots can do multiple tasks during their mission and the tasks arrive dynamically in groups. Each robot can do at most one task from a group and the total number of tasks a robot can do is bounded by its limited battery life. There is a payoff for assigning each robot to a task and the objective is to maximize the total payoff. A special case, where each group has one task and each robot can do one task is the online maximum weighted bipartite matching problem (MWBMP). For online MWBMP, it is known that, under some assumptions on the payoffs, a greedy algorithm has a competitive ratio of 1 over 3. Our key result is to prove that for the general problem, under the same assumptions on the payoff as in MWBMP and an assumption on the number of tasks arising in each group, a repeated auction algorithm, where each group of tasks is (near) optimally allocated to the available group of robots has a guaranteed competitive ratio. We also prove that (a) without the assumptions on the payoffs, it is impossible to design an algorithm with any performance guarantee and (b) without the assumption on the task profile, the algorithms that can guarantee a feasible allocation (if one exists) have arbitrarily bad performance in the worst case. Additionally, we present simulation results depicting the average case performance of the repeated greedy auction algorithm.

A crowdsourcing system, such as the Amazon Mechanical Turk (AMT), provides a platform for a large number of questions to be answered by Internet workers. Such systems have been shown to be useful to solve problems that are difficult for computers, including entity resolution, sentiment analysis, and image recognition. In this paper, we investigate the online task assignment problem: Given a pool of n questions, which of the k questions should be assigned to a worker? A poor assignment may not only waste time and money, but may also hurt the quality of a crowdsourcing application that depends on the workers' answers. We propose to consider quality measures (also known as evaluation metrics) that are relevant to an application during the task assignment process. Particularly, we explore how Accuracy and F-score, two widely-used evaluation metrics for crowdsourcing applications, can facilitate task assignment. Since these two metrics assume that the ground truth of a question is known, we study their variants that make use of the probability distributions derived from workers' answers. We further investigate online assignment strategies, which enables optimal task assignments. Since these algorithms are expensive, we propose solutions that attain high quality in linear time. We develop a system called the Quality-Aware Task Assignment System for Crowdsourcing Applications (QASCA) on top of AMT. We evaluate our approaches on five real crowdsourcing applications. We find that QASCA is efficient, and attains better result quality (of more than 8% improvement) compared with existing methods. © 2015 ACM, Inc.

Planning-based mobile crowdsourcing bidirectional multi-stage online task assignment

With the popularity of mobile devices and Online To Offline (O2O) marketing model, various spatial crowdsourcing platforms, such as Gigwalk, WeGoLook, TaskRabbit and gMission, are getting popular. An important task of spatial crowdsourcing platforms is to allocate spatial tasks to suitable workers. Existing approaches only simply focus on maximizing the number of completed spatial tasks but neglect the influence of supplies and demands from real crowdsourcing market, which leads to different optimal objectives for crowdsourcing task assignments. In this paper, to address the shortcomings of the existing approaches, we first propose a more general spatial crowdsourcing task assignment problem, called Market-driven Optimal Task Assignment (MOTA) problem, consisting of two real scenarios, Excess Demand of Crowd Workers and Insufficient Supply of Spatial Tasks, in daily life. Unfortunately, we prove that the two variants of this problem are NP-Hard. Thus, we design two approximation algorithm to solve this problem. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on synthetic datasets.

Energy and latency aware mobile task assignment for green cloudlets

This work develops distributed hierarchical task assignments and communication algorithms for flocks of unmanned aerial vehicles dispersed in an unknown theater while engaging multiple moving targets. The dynamical changes require distributed algorithms, without relying on a central agent, which may constitute a single point-of-failure and is exposed to communication breaks. Our methodology overcomes the typical deficiencies of a centralized solution by organizing the agents in spanning trees. Whenever possible, the spanning trees merge into a single tree that clusters the maximum number of agents. Using relaxation methods and inputs from its parent and children, every agent attempts to optimize its own solution for task assignment while communicating using an ad-hoc protocol. Simulation experiments show clear-cut advantages of using the proposed task assignment and communication algorithms.

This paper presents the first systematic investigation of the potential performance gains for crowdsourcing systems, deriving from available information at the requester about individual worker earnestness (reputation). In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple “maximum a-posteriori“ decision rule. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the LRA decision rule introduced in the literature.

For the task assignment problem in a smart logistic center with many autonomous mobile robots, a task assignment method is proposed based on a market auction idea. Due to the existence of asymmetrical shelf-shift tasks within the center and diverse self-finished cost of each task besides the correlation between tasks, the problem of multi-robot task assignment in a logistic center is different from multi-robot routing problem. By introducing the correlation function and self-finished cost function, the model of task assignment is presented. Then, the bidding strategy is given by analyzing the performance difference. On these bases, an auction approach is developed for the static and dynamic task assignment. Finally, the simulation results show the applicability of the algorithm.

An improved partial solution to the task assignment and multiway cut problems

Wireless mobile sensing systems are hierarchical and heterogeneous in nature with components that have different energy and performance capabilities. In such systems allocation of tasks to different devices affects both performance and the entire system battery lifetime. In this paper we formulate the problem of optimal task assignment with objectives related to minimizing the total system energy consumption and maximizing the system lifetime as an Integer Linear Program (ILP). We describe a heuristic algorithm and two dynamic graph-based partitioning algorithms that are computationally efficient and that are able to adapt in real-time to changing system conditions. ILP based solutions are able to achieve optimal task assignment, but cannot be used in dynamically changing conditions due to their computationally expensive nature. We evaluate the performance of our three dynamic algorithms using Qualnet and show that they have up to 88% longer system lifetime than the ILP based solutions.

Using multiple unmanned aerial vehicles (UAVs) to perform some tasks cooperatively has received growing attention in recent years. Task assignment is a difficult problem in mission planning. Multiple tasks assignment problem for cooperating homogeneous UAVs is considered as a traditional combinatorial optimization problem. This paper addresses the problem of assigning multiple tasks to cooperative homogeneous UAVs, minimizing the total cost and balancing the cost of each UAV. We propose a centralized task assignment scheme which is based on minimum spanning tree. This scheme involves two phases. In the first phase, we use the Kruskal algorithm and the breadth first search algorithm to assign all tasks to UAVs and get a proper initial task assignment solution. The second phase involves the Pareto optimization improvement in the solution generated from the first phase. For a single UAV, we use the dynamic programming algorithm to calculate the total cost of completing all assigned tasks. The performance of the proposed scheme is compared to that of heuristic simulated annealing algorithm. The simulation results show that the proposed scheme can solve the homogeneous multi-UAV cooperative task assignment problem effectively.