Distributed Constraint Optimization Problems: Review and perspectives

Intelligent agents is a research area of the Artificial Intelligence intensely studied since the 1980s. Multi-agent systems represent a powerful paradigm of analyzing, projecting, and developing complex systems. One of the main difficulties in modeling a multi-agent system is defining the coordination model, due to the autonomous behavior of the agents. Distributed Constraint Optimization Problems (DCOP) have emerged as one of most important formalisms for coordination and distributed problem solving in multi-agent systems and are capable of modeling a large class of real world problems naturally. This work aims to provide an overview and critical review of DCOP, addressing the most popular methods and techniques, the evolution and comparison of algorithms, and future perspectives on this promising research area.     

Solution Techniques for Constraint Satisfaction Problems: Foundations

The Constraint Satisfaction Problem (CSP) is ubiquitous in artificialintelligence. It has a wide applicability, ranging from machine visionand temporal reasoning to planning and logic programming. This paperattempts a systematic and coherent review of the foundations ofthe techniques for constraint satisfaction. It discusses in detail thefundamental principles and approaches. This includes an initialdefinition of the constraint satisfaction problem, a graphical meansof problem representation, conventional tree search solutiontechniques, and pre-processing algorithms which are designed to makesubsequent tree search significantly easier.     

基于MAS的分布式约束满足求解在供应链研究中的应用

简要介绍了多智能体系统(MAS)在供应链研究中的应用,给出了约束满足问题(Constraint Satisfaction Problem,CSP)和分布式约束满足问题(Distributed CSP)的定义以及其应用现状,提出了一个利用基于MAS的分布式约束满足求解来研究供应链问题的基本框架,并给出了其求解过程。     

蚁群算法在资源受限项目调度问题中的应用

资源受限的项目调度问题(RCPSP,Resource-ConstrainedProjectSchedulingProblems)已经被证明是一种NP-hard的组合优化问题,随着问题规模的增大,使用经典的数学方法如数学规划等方法,已经很难解决问题。论文提出了一种用于求解资源受限的项目调度问题的蚁群算法。针对资源受限的项目调度问题的具体特点,提出了蚂蚁巡游网络图的动态生成方式,信息素的表示及更新方式,以及启发信息的计算方法。针对PSPLIB中的测试集对算法中的主要参数进行了优化,最后,使用PSPLIB中的四种测试集对算法进行了测试,计算结果表明了算法的有效性。     

Automatic Generation of Redundant Models for Permutation Constraint Satisfaction Problems

If we have two representations of a problem as constraint satisfaction problem (CSP) models, it has been shown that combining the models using channeling constraints can increase constraint propagation in tree search CSP solvers. Handcrafting two CSP models for a problem, however, is often time-consuming. In this paper, we propose model induction, a process which generates a second CSP model from an existing model using channeling constraints, and study its theoretical properties. The generated induced model is in a different viewpoint, i.e., set of variables. It is mutually redundant to and can be combined with the input model, so that the combined model contains more redundant information, which is useful to increase constraint propagation. We also propose two methods of combining CSP models, namely model intersection and model channeling. The two methods allow combining two mutually redundant models in the same and different viewpoints respectively. We exploit the applications of model induction, intersection, and channeling and identify three new classes of combined models, which contain different amounts of redundant information. We construct combined models of permutation CSPs and show in extensive benchmark results that the combined models are more robust and efficient to solve than the single models.     

Distributed artificial intelligence from a socio-cognitive standpoint: Looking at reasons for interaction

Distributed Artificial Intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence and help. Connections with human-computer interaction are also suggested.     

基于MAS的分布式群体决策支持系统框架体系结构的研究

论文分析了群体决策支持系统(GDSS)的现状,根据GDSS决策方式的两种分类,结合分布式人工智能(DAI)技术和多Agent系统(MAS)技术,提出了基于MAS的分布式群体决策支持系统的框架体系结构,并对群体决策支持系统中的协调作了初步研究。     

基于混沌蚂蚁的分布式系统协同优化方法

针对复杂分布式系统的优化问题,提出基于混沌蚂蚁的复杂分布式系统协同优化方法.在系统理论指导下,分析复杂分布式系统中自主Agent的基本动力学特征,进而提出复杂分布式系统协同优化模型.在此基础上,借助混沌蚂蚁群算法(CAS)的思想,建立基于混沌蚂蚁的复杂分布式系统协同优化算法(CAS-CO).通过对复杂多Agent网络中基于位置的任务分配问题进行仿真实验,同时与已有算法仿真结果对比,表明CAS-CO算法可行有效,反映文中模型的正确性和Agent的自主性在复杂分布式系统设计和构建中的重要性.     

Using Constraint Satisfaction Problem approach to solve human resource allocation problems in cooperative health services

In developing countries, the increasing utilization of health services, due to a great life expectancy, is followed by a reduction in incomes from the public health system and from private insurance companies, to the payment of medical procedures. Beyond this scenery, it is mandatory an effective hospital cost control though the utilization of planning tools.This work is intended to contribute to the reduction of hospital costs, proposing a new tool for planning human resources utilization in hospital plants. Specifically, it is proposed a new tool for human resources allocation in health units. The solution to the allocation problem uses the CSP technique (Constraint Satisfaction Problem) associated with the backtracking search algorithm. With the objective of enhancing the backtracking search algorithm performance a new heuristics is proposed. Through some simulations the performance of the proposed heuristics is compared to the other heuristics previously published in literature: remaining minimum values, forward checking and grade heuristics.Another important contribution of this work is the mathematical modeling of the constraints, that could be unary, multiple, numeric and implicit constraints. In the results it is presented a case study of a human resource allocation in a cooperative health service.Based on the results, it is proposed that for a real allocation problems solution, the best approach is to combine the remaining minimum values heuristics with the grade heuristics, to select the best unit allocation to be filled, and then use the proposed heuristic to select the best physician to the chosen unit allocation. This association shows a satisfactory result for the human resource allocation problem of the case study, with an algorithm convergence time of 46.7 min with no backtracks. The same problem when manually resolved took about more than 50 h.     

Distributed robust consensus control of multi-agent systems with heterogeneous matching uncertainties

This paper considers the distributed consensus problem of linear multi-agent systems subject to different matching uncertainties for both the cases without and with a leader of bounded unknown control input. Due to the existence of nonidentical uncertainties, the multi-agent systems discussed in this paper are essentially heterogeneous. For the case where the communication graph is undirected and connected, based on the local state information of neighboring agents, a fully distributed continuous adaptive consensus protocol is designed, under which the consensus error is uniformly ultimately bounded and exponentially converges to a small adjustable bounded set. For the case where there exists a leader whose control input is unknown and bounded, a distributed adaptive consensus protocol is proposed to ensure the boundedness of the consensus error. A sufficient condition for the existence of the proposed protocols is that each agent is stabilizable.     

Distributed consensus control of unicycle agents in the presence of external disturbances

In this paper, a distributed consensus control strategy is presented for a team of unicycle agents subject to external disturbances. Bounded disturbances with unknown dynamics on both translational and angular velocities are applied to the system. The key idea is to design the control inputs of each agent in such a way that, after a finite time, agents move with an acute angle with respect to a reference vector typically used for the consensus control of disturbance-free single-integrator agents. Convergence to consensus is then proved using Lyapunov theory. Simulation results confirm the efficacy of the proposed controller.     

Distributed nonlinear control of mobile autonomous multi-agents

This paper studies the distributed nonlinear control of mobile autonomous agents with variable and directed topology. A new distributed nonlinear design scheme is presented for multi-agent systems modeled by double-integrators. With the new design, the outputs of the controlled agents asymptotically converge to each other, as long as a mild connectivity condition is satisfied. Moreover, the velocity (derivative of the output) of each agent can be restricted to be within any specified neighborhood of the origin, which is of practical interest for systems under such physical constraint. The new design is still valid if one of the agents is a leader and the control objective is to achieve leader-following. As an illustration of the generality and effectiveness of the presented methodology, the formation control of a group of unicycle mobile robots with nonholonomic constraints is revisited. Instead of assuming the point-robot model, the unicycle model is transformed into two double-integrators by dynamic feedback linearization, and the proposed distributed nonlinear design method is used to overcome the singularity problem caused by the nonholonomic constraint by properly restricting the velocities. Simulation results are included to illustrate the theoretical results.     

蚁群算法中参数设置对其性能影响的研究

蚁群算法是一种新型的仿生智能算法,但由于算法中参数众多,各种参数值的设置对蚁群算法的性能影响很大。因此。蚁群算法中各参数的合理设置是十分关键的,这也是蚁群算法研究的一个难点问题。对蚁群算法的基本原理进行详细介绍,对蚁群算法中各参数对其性能的影响以及各参数的合理设置进行分析研究。     

基于模糊C均值聚类及群体智能的WSN分层路由算法

为了提高无线传感器网络（WSN）的能量效率并延长其生命周期,提出了一种基于模糊C均值聚类（FCM）和群体智能的WSN分层路由算法（FCM-SI）。首先采用FCM聚类算法对网络进行分簇,优化普通节点与簇头（CH）间距离;然后采用三参数的人工蜂群（ABC）算法选取每个簇的最优簇头;最后采用蚁群优化（ACO）算法搜索簇头至基站（BS）的多跳路径,路径综合考虑了网络的能耗和负载均衡性能。仿真结果显示,与基于均匀分簇的改进的低功耗自适应分簇（I-LEACH）算法、基于ABC的低功耗自适应分簇（ABC-LEACH）算法和基于ACO的低功耗自适应分簇（ANT-LEACH）算法相比,FCM-SI在100 m×100 m,100个节点的初始网络条件下将网络生命周期分别提高了65.2%、49.6%和29.0%。FCM-SI能够有效地延长网络寿命,提高能量利用效率。     

蚁群算法在P2P网络架构下的Web服务中的应用

文章探讨了在P2P网络架构下怎样使用蚁群算法解决Web服务中的Peer间的通信和路由、服务注册和查找等问题。对比试验结果表明该文采用的蚁群算法在性能和收敛性速度上优于常规算法,可以有效利用P2P本身的优势高效地实现Web服务的集成及资源的自治问题。     

A comparison of two chromosome representation schemes used in solving a family-based scheduling problem

Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective—developing new chromosome (solution) representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two chromosome representation schemes (S_old and S_new) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X_Y, where X represents an algorithmic mechanism and Y represents a chromosome representation. Experiment results indicate that GA_ S_new outperforms GA_S_old, and ACO_S_new also outperforms ACO_S_old. These findings reveal the importance of developing new chromosome representations in the application of meta-heuristic algorithms.     

Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.     

Preprocessing of Spatial Query in Distributed GIS

This paper focuses on spatial query optimization in distributed GIS. A new qualitative spatial relation model and its consistency problem solution which is composed of topology, direction, distance and size, are proposed. Research integrating the four aspects has not appeared before. A new method to deduce the constraints of spatial query is given, so it saves the query process time in distributed GIS. Finally, the methods and theories are applied to a distributed GIS project, and the experiment result is satisfactory.