Time-out protocol for task allocation in multi-agent systems

In multi-agent system, the implementation of a time-out protocol can improve workflow coherence and reduce the communication congestion due to the coordination process among agents. This approach is part of effective CIM workflow planning and control for agent-based operations emerging in assembly, manufacturing, service, and logistics. The case study illustrated in this article concerns industrial testing and rework services provided by a shared resource. Results of analyses have been verified by simulation to illustrate how performance improves or deteriorates when different time-out protocols are applied. A general model is introduced for numerical evaluation, identifying the recommended time-out threshold, and the regions where time-out protocols are, or are not useful for better workflow. Measures of workflow coordination performance, including time-oriented and viability-oriented measures are discussed.     

Dynamic task allocation models for large distributed computingsystems

Dynamic task allocation for distributed computing systems (DCS) is an important goal to be achieved for engineering applications. The purpose of dynamic task allocation is to increase the system throughput in a dynamic environment, which can be done by balancing the utilization of computing resources and minimizing communication between processors during run time. In this paper, we propose two dynamic task allocation models which are: 1) the clustering simulated annealing model (CSAM); and 2) the mean field annealing model (MFAM). Both of these models combine characteristics of statistical and deterministic approaches. These models provide the rapid convergence characteristic of the deterministic approaches while preserving the solution quality afforded by simulated annealing. Simulation results of the CSAM and MFAM provide a stable and balanced system with 50% and 10% of the convergence time needed by simulated annealing, respectively. The results of this research are important in that it presents the feasibility of applying statistically based task allocation models on large DCSs in a dynamic environment. Solutions of these models depend on the annealing process instead of the structures of the input data, providing the possibility of obtaining better solutions by using more efficient computing hardware     

分布式约束满足问题及其在MAS任务分配中的应用*

针对多智能体系统(MAS)任务分配问题中多个任务与MAS两者的分布式特征,将任务分配问题形式化为分布式约束满足问题(DCSP)进行求解,分别建立了以任务为中心和以agent为中心两种MAS任务分配模型,基于改进的DCSP分布式并行求解算法,提出了基于DCSP的MAS任务分配问题求解框架。该方法适合求解agent间通信有随机延迟以及agent间存在多约束的问题,应用实例的求解表明了其实用性与有效性。     

混合分布式任务分配机制在足球机器人系统中的应用研究

在分析各种多智能体任务分配机制的优缺点的基础上,结合基于市场法的任务分配机制和基于规则的任务分配机制,提出了一种混合分布式的多机器人任务分配机制用于足球机器人系统的角色分配。该角色分配算法在动态地分配角色的同时能够有效地避免角色的非期望震荡。仿真和实际比赛均验证了该算法的有效性。     

A multi-agent approach for dynamic channel allocation

We propose a multi‐agent approach for dynamic channel allocation (MA‐DCA) in mobile cellular networks. Our approach assumes that each cell in a cellular network works as an agent that negotiates its bandwidth (channel) requirements with its neighbors to minimize its call drop probability. Using stochastic simulation, we compare our MA‐DCA approach with simple fixed channel allocation (FCA) and dynamic channel borrowing approaches for different call arrival rates, cellular network sizes and number of available channels. The results of our experiments show that the proposed MA‐DCA approach performs better than dynamic channel borrowing and FCA approaches.     

An analysis of parallel heuristics for task allocation in multicomputers

In literature there exist many heuristic optimisation techniques which have been proposed as general-purpose methods for solving difficult problems. Of course, the question which of them is more powerful is in general meaningless, however, their application and comparison on real, well-limited problems is quite interesting and intriguing. Furthermore, parallel versions for such techniques are welcome, allowing to reduce the search times or to find new innovative solutions unreachable in a sequential environment. Within this paper we describe two such techniques, the Genetic Algorithms and the Simulated Annealing, and provide a general parallelisation framework for heuristic methods which is based on a locally linked search strategy. A comparative analysis of the parallel versions of these techniques is performed on the solution of a set of different-sized Task Allocation Problems in terms of better absolute solution quality, of lower convergence time to a same solution and of robustness expressed as lower variance around the mean value.     

Kernel matching pursuit for large datasets

Kernel matching pursuit is a greedy algorithm for building an approximation of a discriminant function as a linear combination of some basis functions selected from a kernel-induced dictionary. Here we propose a modification of the kernel matching pursuit algorithm that aims at making the method practical for large datasets. Starting from an approximating algorithm, the weak greedy algorithm, we introduce a stochastic method for reducing the search space at each iteration. Then we study the implications of using an approximate algorithm and we show how one can control the trade-off between the accuracy and the need for resources. Finally, we present some experiments performed on a large dataset that support our approach and illustrate its applicability.     

基于效用函数的多机器人系统任务分配

目前所采用的多机器人系统任务分配方法大多都忽略了任务分配的解质量问题。从定量的角度出发,提出了一种基于效用函数的多机器人系统任务分配策略,在机器人能力向量和子任务要求的能力向量基础上,建立了效用函数的数学模型,根据效用函数大小进行任务分配。仿真实验在足球机器人仿真比赛平台上进行,结果表明该任务分配算法对异构多机器人系统合作具有很好的通用性,且算法快速简单,能够实现任务到机器人的最优映射。     

Formal verification of multi-agent systems behaviour emerging from cognitive task analysis

The main goal of this paper is to present a novel formal approach to the verification of cognitive task analysis (CTA), an analytic tool that has been successfully used in the design of reactive behaviours, on multi-agent architectures. To achieve this, a formal logical system is developed, whose purpose is to formally check the possible success or failure of the resulting implementation. This logic's focus is on modelling an agent's behaviour based on her goals, perceptions and actions. The article starts by giving a brief introduction to current research in reactive systems and cognitive task analysis. Simple definitions are offered of the basic concepts in these fields: agent, object, reactive behaviour, control, etc. As illustration, the paper offers the results of applying CTA to a simple model of postal delivery. Then, the syntax and semantics of the proposed logic are defined. Finally, the logic is applied to the verification of some of the behaviours resulting of the previous CTA analysis.     

Mining co-distribution patterns for large crime datasets

Crime activities are geospatial phenomena and as such are geospatially, thematically and temporally correlated. We analyze crime datasets in conjunction with socio-economic and socio-demographic factors to discover co-distribution patterns that may contribute to the formulation of crime. We propose a graph based dataset representation that allows us to extract patterns from heterogeneous areal aggregated datasets and visualize the resulting patterns efficiently. We demonstrate our approach with real crime datasets and provide a comparison with other techniques.     

一种基于认知无线电系统的公平信道分配算法*

由于在认知无线电网络中主用户与认知用户共存,如何在不对主用户造成干扰的前提下.对认知用户进行合理的资源分配显得尤为重要.以纳什协调解为优化目标,并考虑功率约束,提出一种公平信道分配算法.在仿真中,对以吞吐量最大化为目标的Maximal-Rate策略和考虑公平的Max-Min策略进行了对比,仿真结果表明.本算法有着与Ma...     

Communication constraints multi-agent territory exploration task

A common assumption made in multi-robot research is the connectedness of the underlying network. Although this seems a valid assumption for static networks, it is not realistic for mobile robotic networks, where communication between robots usually is distance dependent. Motivated by this fact, we explicitly consider the communication limitations. This paper extends the LFIP based exploration framework previously developed by Pal et al. (Cogn. Comput. doi:10.1007/s12559-012-9142-7, 2012), to address the Multi-Agent Territory Exploration (MATE-n _k ) task under severe communication constraints. In MATE-n _k task agents have to explore their environment to find and visit n checkpoints, which only count as “visited” when k agents are present at the same time. In its simplest form, the architecture consists of two layers: an “Exploration layer” consisting of a selection of future locations for the team for further exploring the environment, and “Exploration and CheckpointVisit layer”, consisting of visiting the detected checkpoints while continuing the exploration task. The connectivity maintenance objective is achieved via two ways: (1) The first layer employs a leader-follower concept, where a communication zone is constructed by the leader using a distance transforms method, and (2) In the second layer we make use of a graph theory for characterizing the communication, which employs the adjacency and Laplacian matrices of the graph and their spectral properties. The proposed approach has been implemented and evaluated in several simulated environments and with varying team sizes and communication ranges. Throughout the paper, our conclusions are corroborated by the results from extensive simulations.     

Effective reformulations for task allocation in distributed systems with a large number of communicating tasks

In any distributed processing environment, decisions need to be made concerning the assignment of computational task modules to various processors. Many versions of the task allocation problem have appeared in the literature. Intertask communication makes the assignment decision difficult; capacity limitations at the processors increase the difficulty. This problem is naturally formulated as a nonlinear integer program, but can be linearized to take advantage of commercial integer programming solvers. While traditional approaches to linearizing the problem perform well when only a few tasks communicate, they have considerable difficulty solving problems involving a large number of intercommunicating tasks. This paper introduces new mixed integer formulations for three variations of the task allocation problem. Results from extensive computational tests conducted over real and generated data indicate that the reformulations are particularly efficient when a large number of tasks communicate, solving reasonablylarge problems faster than other exact approaches available.     

Branch-and-bound task allocation with task clustering-based pruning

We propose a task allocation algorithm that aims at finding an optimal task assignment for any parallel programs on a given machine configuration. The theme of the approach is to traverse a state–space tree that enumerates all possible task assignments. The efficiency of the task allocation algorithm comes from that we apply a pruning rule on each traversed state to check whether traversal of a given sub-tree is required by taking advantage of dominance relation and task clustering heuristics. The pruning rules try to eliminate partial assignments that violate the clustering of tasks, but still keeping some optimal assignments in the future search space. In contrast to previous state–space searching methods for task allocation, the proposed pruning rules significantly reduce the time and space required to obtain an optimal assignment and lead the traversal to a near optimal assignment in a small number of states. Experimental evaluation shows that the pruning rules make the state–space searching approach feasible for practical use.     

Subset selection from large datasets for Kriging modeling

When building a Kriging model, the general intuition is that using more data will always result in a better model. However, we show that when we have a large non-uniform dataset, using a uniform subset can have several advantages. Reducing the time necessary to fit the model, avoiding numerical inaccuracies and improving the robustness with respect to errors in the output data are some aspects which can be improved by using a uniform subset. We furthermore describe several new and current methods for selecting a uniform subset. These methods are tested and compared on several artificial datasets and one real life dataset. The comparison shows how the selected subsets affect different aspects of the resulting Kriging model. As none of the subset selection methods performs best on all criteria, the best method to choose depends on how the different aspects are valued. The comparison made in this paper can be used to facilitate the user in making a good choice.     

Evaluating holistic aggregators efficiently for very large datasets

In data warehousing applications, numerous OLAP queries involve the processing of holistic aggregators such as computing the top n, median, quantiles, etc. In this paper, we present a novel approach called dynamic bucketing to efficiently evaluate these aggregators. We partition data into equiwidth buckets and further partition dense buckets into subbuckets as needed by allocating and reclaiming memory space. The bucketing process dynamically adapts to the input order and distribution of input datasets. The histograms of the buckets and subbuckets are stored in our new data structure called structure trees. A recent selection algorithm based on regular sampling is generalized and its analysis extended. We have also compared our new algorithms with this generalized algorithm and several other recent algorithms. Experimental results show that our new algorithms significantly outperform prior ones not only in the runtime but also in accuracy.Received: 20 December 2000, Published online: 4 March 2004Edited by: P. Scheuermann.Sanguthevar Rajasekaran: This authors work is supported by NSF Grant 9912395.     

多Agent层次任务分配方法

提出了一种层次任务分配方法,用于解决动态环境中的任务分配问题.利用全局分配方法为Agent分配合适的任务,当环境发生变换时,通过局部调整来解决任务和Agent之间的匹配问题,使得每个Agent能够根据局部信息选择理想的任务来执行,提高了分配算法的鲁棒性和多Agent整体效用.仿真实验结果表明,该方法是可行且有效的,能够解决动态环境中的任务分配问题.     

A task allocation optimizer for software construction

Task allocation during the construction stage of software engineering is complex and challenging. First, engineers must chart a path between the often conflicting objectives of time and quality. Second, a huge productivity variance exists across the spectrum of practicing software developers. Properly handling this variance amid those time and quality pressures is a tricky management problem. Multiobjective optimization might provide the answer. This emerging research area generates optimal solutions for projects with many objectives. An experienced decision-maker analyzes these solutions and selects the best one. Here, we describe such an approach and demonstrate it with a problem involving the allocation of software construction tasks among a team of software developers with varying degrees of skill.     

Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints

The Journal of Supercomputing - Cooperative multiple task assignment problem is an essential issue in the collaboration of multiple unmanned aerial vehicles (UAVs). Consensus-based bundle algorithm...