Decentralized Dynamic Task Allocation Using Overlapping Potential Games

Chapman, Archie C.; Micillo, Rosa Anna; Kota, Ramachandra; Jennings, Nicholas R.

doi:10.1093/comjnl/bxq023

Cited by 24 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Targets A∼C can be realized by the utility model so that a fault task allocation scheme can be quickly developed dynamically [12]. Targets D∼F need the automation of MAS to achieve the adjustment of the rush repairing strategy actually according to the emergency.…”

Section: Multisquad Dynamic Allocation Of the Faultmentioning

confidence: 99%

Real-Time Multifault Rush Repairing Strategy Based on Utility Theory and Multiagent System in Distribution Networks

Zhao

Zhang

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The problem of multifault rush repair in distribution networks (DNs) is a multiobjective dynamic combinatorial problem with topology constraints. The problem consists of archiving an optimal faults’ allocation strategy to squads and an admissible multifault rush repairing strategy with coordinating switch operations. In this article, the utility theory is introduced to solve the first problem and a new discrete bacterial colony chemotaxis (DBCC) algorithm is proposed for the second problem to determine the optimal sequence for each squad to repair faults and the corresponding switch operations. The above solution is called the two-stage approach. Additionally, a double mathematical optimization model based on the fault level is proposed in the second stage to minimize the outage loss and total repairing time. The real-time adjustment multiagent system (RA-MAS) is proposed to provide facility to achieve online multifault rush repairing strategy in DNs when there are emergencies after natural disasters. The two-stage approach is illustrated with an example from a real urban distribution network and the simulation results show the effectiveness of the two-stage approach.

show abstract

Section: Multisquad Dynamic Allocation Of the Faultmentioning

confidence: 99%

Real-Time Multifault Rush Repairing Strategy Based on Utility Theory and Multiagent System in Distribution Networks

Zhao

Zhang

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…They assist agents to achieve their objectives and to maximize the benefits of the system. There are works that address the task-scheduling problem in multi-agent systems [14,15], multi-robot systems [16], disaster-emergency teams [17], robocup rescue simulations [18], and strategic decision making for coordinating actions of a USAR team [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A strategy contains a set of parallel, interdependent, and prioritized threads. A thread is a sub-problem that is composed of a unique ranking, a subset of agents, a subset of task types, and a subset of a geographic area [19].…”

Section: Human Strategic Decisionsmentioning

confidence: 99%

Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-Agent Systems

Nourjou¹,

Smith²,

Hatayama³

et al. 2014

IJMLC

View full text Add to dashboard Cite

Abstract-Problem: This paper addresses a centralized scheduling problem in multi-agent systems in which the incident commander (IC) of a disaster-response team aims to coordinate the actions of the field units (rational agents) to minimize the total operation time in uncertain, dynamic, and spatial environments.Objective: The purpose of this paper is to propose an intelligent software system that assists the IC in the dynamic assignment of geospatial-temporal macro tasks to agents under human strategic decisions. This system autonomously executes a heuristic algorithm to minimize the maximum total dependent duration according to human high-level strategies.Results: The result is a schedule composed of macro decisions, each comprised of seven types of information: 1) what task type is going to be accomplished, 2) who (a subset of agents) are assigned to this assignment, 3) where this task is to be performed (a road segment or zone as a macro geospatial object) containing a subset of tasks, 4) when operations start, 5) when operations finish, 6) how many tasks are estimated to be completed, 7) what task types and the estimated number to be revealed (identified" or "enabled) in this location to complete this job.Conclusion: This result, which is a feasible solution for the addressed problem, permits the IC to coordinate agents, partially specify the activities of the agents in time and space, minimize the overall execution time for all the tasks, calculate the correct time to revise the strategic decisions, evaluate the efficiency of the high-level strategy, and estimate the makespan.

show abstract

“…6) Local views of agents. Due to the communication constraints of disaster environments, each agent can only acquire the information about tasks close to its location and the information of agents with which it can directly communicate [13];Various approaches have been proposed to achieve efficient task allocation in disaster environments [14,11,15]. Some approaches deal with task allocation through a central point [7,6,8].…”

mentioning

confidence: 99%

“…Various approaches have been proposed to achieve efficient task allocation in disaster environments [14,11,15]. Some approaches deal with task allocation through a central point [7,6,8].…”

mentioning

confidence: 99%

Dynamic Task Allocation for Heterogeneous Agents in Disaster Environments Under Time, Space and Communication Constraints

Zhang

Bai

2015

The Computer Journal

View full text Add to dashboard Cite

Task allocation for heterogeneous agents in disaster environments under time, space and communication constraints is a challenging issue in both theory and practice. This paper presents a dynamic task allocation approach for such situations. The proposed approach consists of an information collection mechanism, a group task allocation mechanism and a group coordination mechanism. Initially, the information collection mechanism is applied to help agents in communication networks to reduce their communication connections and select one agent in each network as the network leader in a decentralised manner so as to facilitate the collection of information for task allocation under communication constraints. Then, the group task allocation mechanism is employed by each network leader to allocate tasks and agents in its network to groups with suitable space ranges by considering time, space and communication constraints as well as the differing capabilities of agents. During task execution, due to the dynamics of disaster environments, the original allocation (by the group task allocation mechanism) of tasks and agents in groups may be unsuitable. In order to achieve continuous coordination of the heterogeneous agents among groups under communication constraints, the group coordination mechanism is employed. Experimental results demonstrate that the proposed approach can have better performance than many existing approaches in terms of information collection and dynamic task allocation in disaster environments under time, space and communication constraints.Keywords: Dynamic task allocation; Disaster environments; Time, space and communication constraints a task, it first needs to move to the location of the task [7,6,8]; 3) Communication constraints. In disaster environments, due to the destruction of local communication facilities, the amount of information transferred among agents (i.e., the constraint of communication capacities) and the communication distances of agents (i.e., the constraint of communication ranges) are limited [9, 10]. 4) Dynamics of environments. In disaster environments, agents can enter and leave the environments and tasks can be continuously discovered and finished in the environments [11,12]; 5) Differing capabilities of agents. In disaster environments, each agent has its own capabilities, which determine what kind of tasks it can conduct [7]. 6) Local views of agents. Due to the communication constraints of disaster environments, each agent can only acquire the information about tasks close to its location and the information of agents with which it can directly communicate [13];Various approaches have been proposed to achieve efficient task allocation in disaster environments [14,11,15]. Some approaches deal with task allocation through a central point [7,6,8]. In such approaches, the central controller (i.e., the agent in charge of task allocation) can create an optimal solution for task allocation by considering time and space constraints as well as the differing capabilities of agents ...

show abstract

Decentralized Dynamic Task Allocation Using Overlapping Potential Games

Cited by 24 publications

References 28 publications

Real-Time Multifault Rush Repairing Strategy Based on Utility Theory and Multiagent System in Distribution Networks

Real-Time Multifault Rush Repairing Strategy Based on Utility Theory and Multiagent System in Distribution Networks

Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-Agent Systems

Dynamic Task Allocation for Heterogeneous Agents in Disaster Environments Under Time, Space and Communication Constraints

Contact Info

Product

Resources

About