A Consensus-Based Grouping Algorithm for Multi-agent Cooperative Task Allocation with Complex Requirements

Este artículo repasa los usos del llamado paradigma hexaemeral (el relato de la creación del mundo contenido en el primer capítulo del Génesis) en la poesía de Lope, dejando aparte los ejemplos contenidos en su prosa doctrinal y en su teatro, donde el motivo tiene una dilatada presencia en sus autos sacramentales. La escasez de ejemplos en su poesía lírica se explica en parte por la específica materia sacra y catequética del motivo. This paper shows the use of the hexaemeral paradigm (the story of the creation of the world contained in the first chapter of the book of Genesis) in the poetry of Lope, not including other examples from his doctrinal prose and theater, where the topic has an extensive presence, especially in his sacramental plays. The scarcity of cases in Lope’s lyric poetry is partly explained by the specific sacred and catechetical content of the motif

A consensus-based grouping algorithm for multi-agent cooperative task allocation with complex requirements

This paper looks at consensus algorithms for agent cooperation with unmanned aerial vehicles. The foundation is the consensus-based bundle algorithm, which is extended to allow multi-agent tasks requiring agents to cooperate in completing individual tasks. Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments. Using the behaviours observed in bees and ants inspires decentralised algorithms for groups of agents to adapt to changing task demand. Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies. We address the problems of handling these challenges and improve the efficiency of the algorithm for these requirements, whilst decreasing the communication cost with a new data structure. The proposed algorithm converges to a conflict-free, feasible solution of which previous algorithms are unable to account for. Furthermore, the algorithm takes into account heterogeneous agents, deadlocking and a method to store assignments for a dynamical environment. Simulation results demonstrate reduced data usage and communication time to come to a consensus on multi-agent tasks. © 2014 The Author(s)

A consensus-based grouping algorithm for multi-agent cooperative task allocation with complex requirements

This paper looks at consensus algorithms for agent cooperation with unmanned aerial vehicles. The foundation is the consensus-based bundle algorithm, which is extended to allow multi-agent tasks requiring agents to cooperate in completing individual tasks. Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments. Using the behaviours observed in bees and ants inspires decentralised algorithms for groups of agents to adapt to changing task demand. Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies. We address the problems of handling these challenges and improve the efficiency of the algorithm for these requirements, whilst decreasing the communication cost with a new data structure. The proposed algorithm converges to a conflict-free, feasible solution of which previous algorithms are unable to account for. Furthermore, the algorithm takes into account heterogeneous agents, deadlocking and a method to store assignments for a dynamical environment. Simulation results demonstrate reduced data usage and communication time to come to a consensus on multi-agent tasks. © 2014 The Author(s)

Data-driven Metareasoning for Collaborative Autonomous Systems

When coordinating their actions to accomplish a mission, the agents in a multi-agent system may use a collaboration algorithm to determine which agent performs which task. This paper describes a novel data-driven metareasoning approach that generates a metareasoning policy that the agents can use whenever they must collaborate to assign tasks. This metareasoning approach collects data about the performance of the algorithms at many decision points and uses this data to train a set of surrogate models that can estimate the expected performance of different algorithms. This yields a metareasoning policy that, based on the current state of the system, estimated the algorithms’ expected performance and chose the best one. For a ship protection scenario, computational results show that one version of the metareasoning policy performed as well as the best component algorithm but required less computational effort. The proposed data-driven metareasoning approach could be a promising tool for developing policies to control multi-agent autonomous systems.This work was supported in part by the U.S. Naval Air Warfare Center-Aircraft Division

Dynamic Task Allocation in Partially Defined Environments Using A* with Bounded Costs

The sector of maritime robotics has seen a boom in operations in areas such as surveying and mapping, clean-up, inspections, search and rescue, law enforcement, and national defense. As this sector has continued to grow, there has been an increased need for single unmanned systems to be able to undertake more complex and greater numbers of tasks. As the maritime domain can be particularly difficult for autonomous vehicles to operate in due to the partially defined nature of the environment, it is crucial that a method exists which is capable of dynamically accomplishing tasks within this operational domain. By considering the task allocation problem as a graph search problem, Minion Task, is not only capable of finding and executing tasks, but is also capable of optimizing costs across a range of parameters and of considering constraints on the order that tasks may be completed in. Minion task consists of four key phases that allow it to accomplish dynamic tasking in partially defined environments. These phases are a search space updater that is capable of evaluating the regions the vehicle has effectively perceived, a task evaluator that is capable of ascertaining which tasks in the mission set need to be searched for and which can be executed, a task allocation process that utilizes a modified version of the A* with Bounded Costs (ABC) algorithm to select the best ordering of task for execution based on an optimization routing, and, finally, a task executor that handles transiting to and executing tasks orders received from the task allocator. To evaluate Minion Task’s performance, the modified ABC algorithm used by the task allocator was compared to a greedy and a random allocation scheme. Additionally, to show the full capabilities of the system, a partial simulation of the 2018 Maritime RobotX competition was utilized to evaluate the performance of the Minion Task algorithm. Comparing the modified ABC algorithm to the greedy and random allocation algorithms, the ABC method was found to always achieve a score that was as good, if not better than the scores of the greedy and random allocation schemes. At best, ABC could achieve an up to 2 times improvement in the score achieved compared to the other two methods when the ranges for the score and execution times for each tasks in the task set as well as the space where these tasks could exists was sufficiently large. Finally, using two scenarios, it was shown that Minion Task was capable of completing missions in a dynamic environment. The first scenario showed that Minion Task was capable of handling dynamic switching between searching for and executing tasks. The second scenario showed the algorithm was capable of handling constraints on the ordering of the tasks despite the environment and arrangement of tasks not changing otherwise. This paper succeeded in proving a method, Minion Task, that is capable of performing missions in dynamic maritime environments

Distributed task allocation optimisation techniques in multi-agent systems

A multi-agent system consists of a number of agents, which may include software agents, robots, or even humans, in some application environment. Multi-robot systems are increasingly being employed to complete jobs and missions in various fields including search and rescue, space and underwater exploration, support in healthcare facilities, surveillance and target tracking, product manufacturing, pick-up and delivery, and logistics. Multi-agent task allocation is a complex problem compounded by various constraints such as deadlines, agent capabilities, and communication delays. In high-stake real-time environments, such as rescue missions, it is difficult to predict in advance what the requirements of the mission will be, what resources will be available, and how to optimally employ such resources. Yet, a fast response and speedy execution are critical to the outcome. This thesis proposes distributed optimisation techniques to tackle the following questions: how to maximise the number of assigned tasks in time restricted environments with limited resources; how to reach consensus on an execution plan across many agents, within a reasonable time-frame; and how to maintain robustness and optimality when factors change, e.g. the number of agents changes. Three novel approaches are proposed to address each of these questions. A novel algorithm is proposed to reassign tasks and free resources that allow the completion of more tasks. The introduction of a rank-based system for conflict resolution is shown to reduce the time for the agents to reach consensus while maintaining equal number of allocations. Finally, this thesis proposes an adaptive data-driven algorithm to learn optimal strategies from experience in different scenarios, and to enable individual agents to adapt their strategy during execution. A simulated rescue scenario is used to demonstrate the performance of the proposed methods compared with existing baseline methods

Task allocation and consensus with groups of cooperating Unmanned Aerial Vehicles

The applications for Unmanned Aerial Vehicles are numerous and cover a range of areas from military applications, scientific projects to commercial activities, but many of these applications require substantial human involvement. This work focuses on the problems and limitations in cooperative Unmanned Aircraft Systems to provide increasing realism for cooperative algorithms. The Consensus Based Bundle Algorithm is extended to remove single agent limits on the task allocation and consensus algorithm. Without this limitation the Consensus Based Grouping Algorithm is proposed that allows the allocation and consensus of multiple agents onto a single task. Solving these problems further increases the usability of cooperative Unmanned Aerial Vehicles groups and reduces the need for human involvement. Additional requirements are taken into consideration including equipment requirements of tasks and creating a specific order for task completion. The Consensus Based Grouping Algorithm provides a conflict free feasible solution to the multi-agent task assignment problem that provides a reasonable assignment without the limitations of previous algorithms. Further to this the new algorithm reduces the amount of communication required for consensus and provides a robust and dynamic data structure for a realistic application. Finally this thesis provides a biologically inspired improvement to the Consensus Based Grouping Algorithm that improves the algorithms performance and solves some of the difficulties it encountered with larger cooperative requirements