Scalable fault tolerant agent grooming environment-SAGE

Multi-agent systems (MAS) advocate an agent-based approach to software engineering based on decomposing problems in terms of decentralized, autonomous agents that can engage in flexible, high-level interactions. This chapter introduces scalable fault tolerant agent grooming environment (SAGE), a second-generation Foundation for Intelligent Physical Agents (FIPA)-compliant multi-agent system developed at NIIT-Comtec, which provides an environment for creating distributed, intelligent, and autonomous entities that are encapsulated as agents. The chapter focuses on the highlight of SAGE, which is its decentralized fault-tolerant architecture that can be used to develop applications in a number of areas such as e-health, e-government, and e-science. In addition, SAGE architecture provides tools for runtime agent management, directory facilitation, monitoring, and editing messages exchange between agents. SAGE also provides a built-in mechanism to program agent behavior and their capabilities with the help of its autonomous agent architecture, which is the other major highlight of this chapter. The authors believe that the market for agent-based applications is growing rapidly, and SAGE can play a crucial role for future intelligent applications development. © 2007, IGI Global.

Self assessment schemes for multi-agent cooperative search

In this paper, we present self assessment schemes (SAS) for multiple agents performing a search mission on an unknown terrain. The agents are subjected to limited communication and sensor ranges. The agents communicate and coordinate with their neighbours to arrive at route decisions. The self assessment schemes proposed here have very low communication and computational overhead. The SAS also has attractive features like scalability to large number of agents and fast decision-making capability. SAS can be used with partial or complete information sharing schemes during the search mission. We validate the performance of SAS using simulation on a large search space consisting of 100 agents with different information structures and self assessment schemes. We also compare the results obtained using SAS with that of a previously proposed negotiation scheme. The simulation results show that the SAS is scalable to large number of agents and can perform as good as the negotiation schemes with reduced communication requirement (almost 20% of that required for negotiation).

Rollover-Preventive Force Synthesis at Active Suspensions in a Vehicle Performing a Severe Maneuver With Wheels Lifted Off

Among the intelligent safety technologies for road vehicles, active suspensions controlled by embedded computing elements for preventing rollover have received a lot of attention. The existing models for synthesizing and allocating forces in such suspensions are conservatively based on the constraints that are valid until no wheels lift off the ground. However, the fault tolerance of the rollover-preventive systems can be enhanced if the smart/active suspensions can intervene in the more severe situation in which the wheels have just lifted off the ground. The difficulty in computing control in the last situation is that the vehicle dynamics then passes into the regime that yields a model involving disjunctive constraints on the dynamics. Simulation of dynamics with disjunctive constraints in this context becomes necessary to estimate, synthesize, and allocate the intended hardware realizable forces in an active suspension. In this paper, we give an algorithm for the previously mentioned problem by solving it as a disjunctive dynamic optimization problem. Based on this, we synthesize and allocate the roll-stabilizing time-dependent active suspension forces in terms of sensor output data. We show that the forces obtained from disjunctive dynamics are comparable with existing force allocations and, hence, are possibly realizable in the existing hardware framework toward enhancing the safety and fault tolerance.

Multi-agent Reinforcement Learning for Traffic Signal Control

Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users amidst the rapid increase in the usage of vehicles. In this paper, we formulate the TSC problem as a discounted cost Markov decision process (MDP) and apply multi-agent reinforcement learning (MARL) algorithms to obtain dynamic TSC policies. We model each traffic signal junction as an independent agent. An agent decides the signal duration of its phases in a round-robin (RR) manner using multi-agent Q-learning with either is an element of-greedy or UCB 3] based exploration strategies. It updates its Q-factors based on the cost feedback signal received from its neighbouring agents. This feedback signal can be easily constructed and is shown to be effective in minimizing the average delay of the vehicles in the network. We show through simulations over VISSIM that our algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm 15] over two real road networks.

Modeling smart grids as complex systems through the implementation of intelligent hubs

ICINCO 2010

Selecting a multi-agent system development tool for industrial applications: A case study of self-serving aircraft assets

Industrialists have few example processes they can benchmark against in order to choose a multi-agent development kit. In this paper we present a review of commercial and academic agent tools with the aim of selecting one for developing an intelligent, self-serving asset architecture. In doing so, we map and enhance relevant assessment criteria found in literature. After a preliminary review of 20 multiagent platforms, we examine in further detail those of JADE, JACK and Cougaar. Our findings indicate that Cougaar is well suited for our requirements, showing excellent support for criteria such as scalability, persistence, mobility and lightweightness. © 2010 IEEE.

New robotics: design principles for intelligent systems.

New robotics is an approach to robotics that, in contrast to traditional robotics, employs ideas and principles from biology. While in the traditional approach there are generally accepted methods (e. g., from control theory), designing agents in the new robotics approach is still largely considered an art. In recent years, we have been developing a set of heuristics, or design principles, that on the one hand capture theoretical insights about intelligent (adaptive) behavior, and on the other provide guidance in actually designing and building systems. In this article we provide an overview of all the principles but focus on the principles of ecological balance, which concerns the relation between environment, morphology, materials, and control, and sensory-motor coordination, which concerns self-generated sensory stimulation as the agent interacts with the environment and which is a key to the development of high-level intelligence. As we argue, artificial evolution together with morphogenesis is not only "nice to have" but is in fact a necessary tool for designing embodied agents.

Performing Fast Addition and Multiplication by Transferring Single Electrons

This paper proposes novel fast addition and multiplication circuits that are based on non-binary redundant number systems and single electron (SE) devices. The circuits consist of MOSFET-based single-electron (SE) turnstiles. We use the number of electrons to represent discrete multiple-valued logic states and we finish arithmetic operations by controlling the number of electrons transferred. We construct a compact PD2,3 adder and a 12x12bit multiplier using the PD2,3 adder. The speed of the adder can be as high as 600MHz with 400nW power dissipation. The speed of the adder is regardless of its operand length. The proposed circuits have much smaller transistors than conventional circuits.

基于Agent的智能交通体系及仿真

交通问题已成为全世界所共同关注的主要问题 ,如何运用现代的科技手段来缓解日益严峻的交通压力 ,是目前研究的重点。该文基于目前交通问题及交通系统发展的现状 ,结合计算机软件技术的最新成果———Agent技术 ,提出了基于Agent技术的智能交通控制的体系结构 ,论述了该结构的优点 ;并根据Agent的特点 ,介绍了运用Agent技术进行交通仿真的优势 ,探讨了具体采用Agent技术进行交通仿真的方法。

智能交通仿真与车辆Agent决策策略的研究

文章介绍了Agent技术及特点,根据Agent的原理及其仿真的优势,着重叙述了Agent技术在交通仿真中的应用,详细探讨了基于Agent的智能仿真系统中车辆Agent的决策特点及模糊决策方法,同时,还分析了驾驶员因素对驾驶行为的影响。

BDI Agent的人机智能结合方法

在网络遥操作中存在着由于操作者的操作负担过重或长时间操作而引起的误操作问题,提出以著名的BDIAgent模型为基础,通过引入中断机制,将反映操作者智能决策的操作目标、操作意图与BDIAgent推理产生的目标、意图进行结合的方法,为解决Agent智能的局限性和操作者的误操作问题,提供了一种人机接口方法,实现了人机智能结合.给出了中断的引入方法和操作者操作目标、操作意图中断响应原理,用形式化模型描述了人机智能决策选取目标和意图的过程.该人机智能结合方法在网络遥操作RoboCup中型足球机器人系统中得到了应用.

基于Agent的网络遥操作机器人系统控制研究

为了充分发挥智能机器人的自主性和人的主观能动性,实现通过网络遥操作非结构化环境下的智能机器人,提出了一种基于Agent的网络遥操作机器人控制系统结构,分析了控制系统中各部分的功能,给出了系统中各个Agent的实现方法。该控制方法已通过仿人形机器人的遥操作控制实验得到了验证。

基于agent及holon的可重构生产线实现技术

以大批量制造的生产线制造系统为背景,研究基于agent与holon智能制造思想体系的可重构生产线制造系统实现技术,以提高生产线柔性与可重构能力为目标·深入分析了多agent制造及holonic制造模式体系下智能单元自主决策、协商合作在制造系统环境中的实现与运行机制,在以机械加工线和装配线为代表的不同的工程实际背景与应用环境下该思想体系目前的具体应用情况与实现技术·指出该思想体系面向具体工程背景的有效实施应用是理论研究的关键·最后以某汽车变速器装配生产线制造系统为应用实例,从其规划设计与运行角度,分析了可重构生产线系统的框架设计及具体实现技术与方法·

An application of a multiple agent system for flexible assemble tasks

C.H. Orgill, N.W. Hardy, M.H. Lee, and K.A.I. Sharpe. An application of a multiple agent system for flexible assemble tasks. In Knowledge based envirnments for industrial applications including cooperating expert systems in control. IEE London, 1989.