Task Allocation Algorithm Based on Auction in RoboCup Rescue Robot Simulation

This paper presents a proposal aimed at improving the task allocation and the coordination of rescue agents in a disaster scenario. Task allocation is related to allocating victims to be rescued by each agent, while coordination is related to how these agents interact among themselves to save a larger number of victims. Rescuing lives in disasters takes place in a dynamic environment in which new victims appear all the time. In addition, the victims' injuries aggravate over time. A due-date-based technique is presented to schedule the tasks in the rescue operations. In this sense, the victims are sequenced in a non-decreasing death time order. To do that, our rescue agents search for the victims and predict their death time to establish a victim priority order in our scheduling algorithm and coordinate the rescue agents. Past rescue experiences of our rescue team are used in the victims' death time estimations. Several experiments using the RoboCup Rescue simulator are presented to prove the feasibility of our approach to improving multiagent performance.

Task allocation is a key issue in multi-agent systems, and finding the optimal strategy for task allocation has been proved to be an NP-hard problem. Existing task allocation methods for multi-agent systems mainly adopt distributed full search strategies or local search strategies. The former requires a lot of computation and communication costs, while the latter cannot ensure the diversity and quality of solutions. Therefore, in this paper, we combine a distributed many-objective evolutionary algorithm called D-NSGA3 with a greedy algorithm to search the task allocation solutions, and we comprehensively consider the constraints related to space, time, energy consumption and agent function in multi-agent systems. Specifically, D-NSGA3 is used to optimize multiple objectives simultaneously so as to ensure the search capability and the diversity of solutions. Alternate search between D-NSGA3 and the greedy algorithm is conducted to enhance the local optimizing ability. Experimental results show that the proposed method can effectively solve large-scale task allocation problems (e.g., the number of agents is not less than 250, and that of tasks is not less than 1000). Compared with the existing work called MSEA, the proposed method could achieve better and more diverse solutions.

From the last two decades, Software agents are playing an important role in the field of Artificial intelligence and the Distributed Problem Solving. The properties of software agents like autonomy, reactivity, pro-activity and their social ability make them more center of focus for the real world problems. The accomplishment of any complex task is required to be done by the agents autonomously without any user intervention in order to achieve high reliability and adaptability. This paper mainly concentrates on the task allocation problem in multi-agent systems. Task Allocation is an important and challenging problem. This can be defined as the problem of allocating tasks among agents within a multi-agent system. Main objective of the task allocation problem is to maximize the number of successfully completed task and overall system utility without any conflict. To accomplish any complex task, agents negotiate, cooperate and coordinate with each other. Many researchers are working in the field of task allocation in multiagent systems. In this paper, various approaches of task allocation in multi-agent systems are discussed. The comparison of these approaches is also providing that lead to a discussion and motivation of the task allocation problem for multi-agent systems. This paper presents a hybrid approach for task allocation in dynamic multi-agent systems. The conclusion drawn from this survey is that, for dynamic multi-agent systems, the distributed task allocation is a better approach.

Task allocation is an important issue in multi-agent systems, and finding the optimal solution of task allocation has been demonstrated to be an NP-hard problem. In many scenarios, agents are equipped with not only communication resources but also computing resources, so that tasks can be allocated and executed more efficiently in a distributed and parallel manner. Presently, many methods have been proposed for distributed task allocation in multi-agent systems. Most of them are either based on complete/full search or local search, and the former usually can find the optimal solutions but requires high computational cost and communication cost; the latter is usually more efficient but could not guarantee the solution quality. Evolutionary algorithm (EA) is a promising optimization algorithm which could be more efficient than the full search algorithms and might have better search ability than the local search algorithms, but it is rarely applied to distributed task allocation in multi-agent systems. In this paper, we propose a distributed task allocation method based on EA. We choose the many-objective EA called NSGA-III to optimize four objectives (i.e., maximizing the number of successfully allocated and executed tasks, maximizing the gain by executing tasks, minimizing the resource cost, and minimizing the time cost) simultaneously. Experimental results show the effectiveness of the proposed method, and compared with the full search strategy, the proposed method could solve task allocation problems with more agents and tasks.

Adaptive collective systems are common in biology and beyond. Typically, such systems require a task allocation algorithm: a mechanism or rule-set by which individuals select particular roles. Here we study the performance of such task allocation mechanisms measured in terms of the time for individuals to allocate to tasks. We ask: (1) Is task allocation fundamentally difficult, and thus costly? (2) Does the performance of task allocation mechanisms depend on the number of individuals? And (3) what other parameters may affect their efficiency? We use techniques from distributed computing theory to develop a model of a social insect colony, where workers have to be allocated to a set of tasks; however, our model is generalizable to other systems. We show, first, that the ability of workers to quickly assess demand for work in tasks they are not currently engaged in crucially affects whether task allocation is quickly achieved or not. This indicates that in social insect tasks such as thermoregulation, where temperature may provide a global and near instantaneous stimulus to measure the need for cooling, for example, it should be easy to match the number of workers to the need for work. In other tasks, such as nest repair, it may be impossible for workers not directly at the work site to know that this task needs more workers. We argue that this affects whether task allocation mechanisms are under strong selection. Second, we show that colony size does not affect task allocation performance under our assumptions. This implies that when effects of colony size are found, they are not inherent in the process of task allocation itself, but due to processes not modeled here, such as higher variation in task demand for smaller colonies, benefits of specialized workers, or constant overhead costs. Third, we show that the ratio of the number of available workers to the workload crucially affects performance. Thus, workers in excess of those needed to complete all tasks improve task allocation performance. This provides a potential explanation for the phenomenon that social insect colonies commonly contain inactive workers: these may be a ‘surplus’ set of workers that improves colony function by speeding up optimal allocation of workers to tasks. Overall our study shows how limitations at the individual level can affect group level outcomes, and suggests new hypotheses that can be explored empirically.

Task allocation is one of the cor e steps to effectively exploit the capabilities of cooperative control of multiple Uninhabited Aerospace Vehicle (UAV) teams. Task allocation is an NP -complete problem. In this paper, we present a new task allocation algorithm that is based on the principl es of genetic algorithm (GA). GA is a class of adaptive search algorithms derived from biological population genetics. We discuss the adaptation and implementation of the GA search strategy to the task allocation problem in the cooperative control of multi ple UAVs. Simulation results indicate that the GA strategy is a feasible approach for the task allocation problem .

<span>When a disaster occurs, the single agent does not have complete knowledge about the circumstances of the disaster. Therefore, the rescue agents should coordinate with each other to perform their allocated tasks efficiently. However, the task allocation process among rescue agents is a complex problem, which is NP-complete problem, and determining the rescue agents that will perform the tasks efficiently is the main problem, which is called the winner determination problem (WDP). This paper proposed an enhanced approach to improve rescue agents’ tasks allocation processes for WDP in reverse combinatorial auctions. The main objective of the proposed approach is to determine the winning bids that will perform the corresponding tasks with minimum cost. The task allocation problem in this paper was transformed into a two-dimensional array, and then the proposed approach was applied to it. The main contribution of the proposed approach is to shorten the search space size to determine the winners and allocate the corresponding tasks for a combination of agents (i.e., more than two agents). The proposed approach was compared to the genetic algorithm regarding the execution time, and the results showed good performance and effectiveness of the proposed approach.</span>

Real time strategy games are complex scenarios where multiple agents must be coordinated in a dynamic, partially observable environment. In this work, we model coordination as a task allocation problem, in which specific tasks must be properly assigned to agents. We employ a task allocation algorithm based on swarm intelligence and adjust its parameters using a genetic algorithm. A fitness estimation method is employed to accelerate execution of the genetic algorithm. To evaluate this approach, we implement this coordination mechanism in the AI of a popular video game: StarCraft: BroodWar. Experiment results show that the genetic algorithm successfully adjusts task allocation parameters. Besides, we assess the trade-off between solution quality and execution time of the genetic algorithm with fitness estimation.

Efficient task allocation remains a fundamental challenge in multi-agent systems, particularly under resource constraints and large-scale deployments. Classical methods, including market-based mechanisms, centralized optimization techniques, and game-theoretic strategies, have been widely applied to address the multi-agent task allocation problem. While effective in small-to-medium-sized settings, these approaches often encounter limitations in terms of scalability, adaptability to dynamic environments, and computational efficiency as the problem size increases. To address these limitations, this study introduces a proximal policy optimization system augmented with a genetic algorithm (GAPPO) that integrates evolutionary search with deep reinforcement learning. GAPPO enables agents to develop energy-efficient task allocation strategies by perceiving environmental states and optimizing their actions through iterative policy updates. The genetic component promotes broader policy exploration beyond local optima, while the proximal policy optimization ensures update stability and sample efficiency. To evaluate the proposed GAPPO algorithm, extensive simulations are conducted across four scenarios, with the largest involving 50 tasks and 500 agents. The results demonstrate that GAPPO achieves superior performance compared to baseline methods, particularly in reducing task completion time. These findings highlight the algorithm’s robustness and efficiency in handling large-scale and computationally intensive coordination tasks.

Task allocation is a very important part of multi-agent systems. When assigning tasks to one of the agents in multi-agent systems, many constraints need to be considered to achieve optimal allocation results. In this paper, an improved genetic algorithm (GA) is proposed to solve the multi-agent task allocation with time window constraints. Firstly, the mathematical model of task allocation is established, and the constraint problem of time window is analyzed. The penalty function method is used to deal with the constraint condition. Secondly, the improved Large Neighborhood Search (LNS) is added to the local search to increase the diversity of population, which can make the algorithm easier to jump out of local optimum. Then genetic algorithm is used to solve the multi-agent task allocation problem with time window constraints. Finally, the simulation verifies the optimization performance of the improved algorithm.

PurposeWith the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system.Design/methodology/approachThe TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise.FindingsExperiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots.Originality/valueThis paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots.

This paper presents an improved algorithm for solving the complex task allocation problem in constrained multiple robot cooperative tasks. The existing multiple robot task allocation mechanisms do not discuss much about complex tasks, instead they treat tasks as simple, indivisible entities. Complex tasks are tasks that can be decomposed into a set of subtasks and so can be executed by several possible ways. The goal of cooperative task allocation algorithm for multiple mobile robots is to find which robot should execute which task in order to maximize the global efficiency and minimize the cost. Some factors such as benefit, cost, resources, and time should be considered during the course of task allocation. The meta-heuristic algorithm proposed here solves the task allocation problems with the characteristics like each task requires a certain amount of resources and each robot has a finite capacity of resource to be shared between the tasks it is assigned. The cost of solution which includes static costs when using robots, assignment cost, and communication cost between the tasks if they are assigned to different robots are also taken into account in developing the solution. A peer search scheme algorithm for solving the constrained task allocation problem is presented. Computational experiments using this algorithm have shown that the proposed method is superior in terms of computation time and solution quality.KeywordsMobile robotsMultiple robot cooperative tasksTask allocationHeuristic algorithmPeer structure

The multi-agent task allocation problem has attracted substantial research interest due to the increasing demand for effective solutions in large-scale and dynamic environments. Despite advancements, traditional algorithms often fall short in optimizing efficiency and adaptability within complex scenarios. To address this shortcoming, we propose a genetic algorithm-enhanced PPO (GAPPO) algorithm, specifically developed to enhance decision-making in challenging task allocation contexts. GAPPO employs a deep reinforcement learning framework, enabling each agent to independently evaluate its surroundings, manage energy resources, and adaptively adjust task allocations in response to evolving conditions. Through iterative refinement, GAPPO achieves balanced task distribution and minimizes energy consumption across diverse configurations. Comprehensive simulations demonstrate that GAPPO consistently outperforms traditional algorithms, resulting in reduced task completion time and heightened energy efficiency. Our findings underscore GAPPO’s potential as a robust solution for real-time multi-agent task allocation.

Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors.