Negotiation schemes for multi-agent cooperative search

Because of limited sensor and communication ranges, designing efficient mechanisms for cooperative tasks is difficult. In this article, several negotiation schemes for multiple agents performing a cooperative task are presented. The negotiation schemes provide suboptimal solutions, but have attractive features of fast decision-making, and scalability to large number of agents without increasing the complexity of the algorithm. A software agent architecture of the decision-making process is also presented. The effect of the magnitude of information flow during the negotiation process is studied by using different models of the negotiation scheme. The performance of the various negotiation schemes, using different information structures, is studied based on the uncertainty reduction achieved for a specified number of search steps. The negotiation schemes perform comparable to that of optimal strategy in terms of uncertainty reduction and also require very low computational time, similar to 7 per cent to that of optimal strategy. Finally, analysis on computational and communication requirement for the negotiation schemes is carried out.

An agent based adaptive bandwidth allocation scheme for multimedia applications

Bandwidth allocation for multimedia applications in case of network congestion and failure poses technical challenges due to bursty and delay sensitive nature of the applications. The growth of multimedia services on Internet and the development of agent technology have made us to investigate new techniques for resolving the bandwidth issues in multimedia communications. Agent technology is emerging as a flexible promising solution for network resource management and QoS (Quality of Service) control in a distributed environment. In this paper, we propose an adaptive bandwidth allocation scheme for multimedia applications by deploying the static and mobile agents. It is a run-time allocation scheme that functions at the network nodes. This technique adaptively finds an alternate patchup route for every congested/failed link and reallocates the bandwidth for the affected multimedia applications. The designed method has been tested (analytical and simulation)with various network sizes and conditions. The results are presented to assess the performance and effectiveness of the approach. This work also demonstrates some of the benefits of the agent based schemes in providing flexibility, adaptability, software reusability, and maintainability. (C) 2004 Elsevier Inc. All rights reserved.

An agent based adaptive bandwidth allocation scheme for multimedia applications

Bandwidth allocation for multimedia applications in case of network congestion and failure poses technical challenges due to bursty and delay sensitive nature of the applications. The growth of multimedia services on Internet and the development of agent technology have made us to investigate new techniques for resolving the bandwidth issues in multimedia communications. Agent technology is emerging as a flexible promising solution for network resource management and QoS (Quality of Service) control in a distributed environment. In this paper, we propose an adaptive bandwidth allocation scheme for multimedia applications by deploying the static and mobile agents. It is a run-time allocation scheme that functions at the network nodes. This technique adaptively finds an alternate patchup route for every congested/failed link and reallocates the bandwidth for the affected multimedia applications. The designed method has been tested (analytical and simulation)with various network sizes and conditions. The results are presented to assess the performance and effectiveness of the approach. This work also demonstrates some of the benefits of the agent based schemes in providing flexibility, adaptability, software reusability, and maintainability. (C) 2004 Elsevier Inc. All rights reserved.

Theoretical foundations for multiple rendezvous of glowworm-inspired mobile agents with variable local-decision domains

We present the theoretical foundations for the multiple rendezvous problem involving design of local control strategies that enable groups of visibility-limited mobile agents to split into subgroups, exhibit simultaneous taxis behavior towards, and eventually rendezvous at, multiple unknown locations of interest. The theoretical results are proved under certain restricted set of assumptions. The algorithm used to solve the above problem is based on a glowworm swarm optimization (GSO) technique, developed earlier, that finds multiple optima of multimodal objective functions. The significant difference between our work and most earlier approaches to agreement problems is the use of a virtual local-decision domain by the agents in order to compute their movements. The range of the virtual domain is adaptive in nature and is bounded above by the maximum sensor/visibility range of the agent. We introduce a new decision domain update rule that enhances the rate of convergence by a factor of approximately two. We use some illustrative simulations to support the algorithmic correctness and theoretical findings of the paper.

Adaptive agent-based manufacturing control and its application to flow shop routing control

The manufacturing industry is currently facing unprecedented challenges from changes and disturbances. The sources of these changes and disturbances are of different scope and magnitude. They can be of a commercial nature, or linked to fast product development and design, or purely operational (e.g. rush order, machine breakdown, material shortage etc.). In order to meet these requirements it is increasingly important that a production operation be flexible and is able to adapt to new and more suitable ways of operating. This paper focuses on a new strategy for enabling manufacturing control systems to adapt to changing conditions both in terms of product variation and production system upgrades. The approach proposed is based on two key concepts: (1) An autonomous and distributed approach to manufacturing control based on multi-agent methods in which so called operational agents represent the key physical and logical elements in the production environment to be controlled - for example, products and machines and the control strategies that drive them and (2) An adaptation mechanism based around the evolutionary concept of replicator dynamics which updates the behaviour of newly formed operational agents based on historical performance records in order to be better suited to the production environment. An application of this approach for route selection of similar products in manufacturing flow shops is developed and is illustrated in this paper using an example based on the control of an automobile paint shop.

a flexible negotiation model for an agent-based software process modelling

Our Agent-based Software Process Modelling (ASPM) approach describes a software process as a set of cooperative agents. Negotiation is the way in which the agents construct their cooperative relations, and thus the software process. Currently, most negotiation models use a fixed negotiation protocol and fixed strategies. In order to achieve the flexibility that the negotiation of the agents in ASPM requires, we propose a negotiation model NM-PA. NM-PA mainly includes a generic negotiation protocol and some rules, which possibly change in different negotiation processes. By changing the rules, the model can support multi-protocols and multi-decision-making strategies at a lower cost.

一种适应性多边软件过程Agent协商模型研究

软件过程建模是指对软件过程建立模型，并且对模型建立和模型执行提供自动化支持。软件过程建模技术为软件过程的描述、表示、执行、分析、跟踪、变更以及改进提供了方法和工具，从而为软件组织实现软件开发过程的管理和改进提供了有力的支持，对于保证软件组织的软件产品质量，提高开发效率具有重要的理论和实践价值。 软件过程的一个重要特征是极大地依赖于软件开发人员，当软件过程面临各种变化时，正是作为软件过程的主体——软件开发人员的及时反应和应对，使得软件过程能够灵活适应各种软件开发的实际和变化状况；忽视软件开发人员在软件过程中的核心作用，是目前大多软件过程建模方法不适用于软件企业实际应用的原因之一。Agent技术主要基于对人的观察，其已被认识到是软件过程建模领域的重要研究方向之一。然而，相关研究工作并没有很好地体现人在软件过程中的核心作用，其根本原因在于其所采用或关注的是单Agent技术，而非多Agent系统技术。以互联为背景的多Agent系统能够很好地刻画人类的社会性，因此，对于当前以全球性、分布式多点等为主要特点的软件过程而言，采用多Agent系统技术，关注软件过程中所涉及的实体之间的协商、协作、竞争、承诺等特性是将Agent技术应用于软件过程的研究重点。 本文提出一种适应性多边软件过程Agent协商模型AMNM-PA。AMNM-PA基于软件开发者为软件过程中的核心要素并且软件过程为这些核心要素之间的相互协同关系的观点，将软件开发建模描述为代表软件开发者的自治的软件过程Agent（简称过程Agent）之间的协商；在协商中，过程Agent能够在变化的环境下针对各种不同实际应用，以及根据对环境的感知和所拥有的知识，适应、灵活地确立彼此间的协同关系，即建立软件过程。 本文在组织上采用了从模型要素及其相互关系定义、模型要素具体描述到模型实现及验证的逻辑结构。首先，本文给出AMNM-PA的模型定义，对过程Agent的协商所涉及的协商要素及其相互之间的关系进行描述。其次，本文详细描述协商要素中的决策过程，即过程Agent在协商中进行决策所依据的协商策略选择方法及各种可选协商策略；并以决策过程为基础，提出一种多边适应性协商算法，以此给出基于AMNM-PA的适应性多边协商过程的基本算法。随后，本文详细描述了另一协商要素——协商规则，其解决了协商过程的离线和在线灵活性问题，从而为软件过程建模的灵活性提供了解决方案。最后，本文实现了基于AMNM-PA的协商系统，并在该系统的支持下进行了实验研究，验证了AMNM-PA能够在变化的环境中适应性地建立面向实际软件项目的软件过程，并且该软件过程所包含的个体利益能够得到均衡、整体利益较高；同时很好地支持了软件过程建立和执行的灵活性。

The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents

N.J. Lacey and M.H. Lee, ?The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents?, Springer-Verlag Lecture Notes on Artificial Intelligence, Vol 2636 on Adaptive Agents and Multi-Agent Systems, 2002.

The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents

Lacey N and Lee M.H., The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents, in Proc. AISB?01 Symposium on Adaptive Agents and Multi-agent Systems, York, March 2001, pp13-24.

CloudNeg: An autonomous multi issue negotiation system, with preference elicitation component, for trading cloud services

Cloud services provide its users with flexible resource provisioning. But in the current market, a user has to choose from a limited set of configurations at a fixed price. This paper presents an autonomous negotiation system termed CloudNeg for negotiating cloud services. CloudNeg provides buyers and sellers of cloud services with autonomous agents to negotiate on the specifications of a cloud instance, including price, on their behalf. These agents elicit their buyers’ time preferences and use them in negotiations. Further, this paper presents two artifacts: a negotiation algorithm and a prototype which together form CloudNeg.

A flexible bandwidth resource provisioning system with agent-enhanced SLA negotiation

Paper describes an effcicient approach for provisioning of network resources based on SLAs and a range of negotiating agents. The work arose from direct collboration with Fujitsu research and invlolved a worldwide press reslease of their agent brokering system which was based on this; also, a plenary address: A.Marshall (QUB) & A.Campbell (Columbia, USA) at 4th IFIP/IEEE International conference on Management of Multimedia Networks and Services' 2001 (MMNS'01). ISSN: 0926-6801

An architecture for agent-enhanced network service provisioning through SLA negotiation

This paper focuses on two main areas. We first investigate various aspects of subscription and session Service Level Agreement (SLA) issues such as negotiating and setting up network services with Quality of Service (QoS) and pricing preferences. We then introduce an agent-enhanced service architecture that facilitates these services. A prototype system consisting of real-time agents that represent various network stakeholders was developed. A novel approach is presented where the agent system is allowed to communicate with a simulated network. This allows functional and dynamic behaviour of the network to be investigated under various agent-supported scenarios. This paper also highlights the effects of SLA negotiation and dynamic pricing in a competitive multi-operator networks environment.

SLA Brokering and Bandwidth Reservation Negotiation Schemes for QoS-aware Internet

We present a novel Service Level Agreement (SLA)-driven service provisioning architecture, which enables dynamic and flexible bandwidth reservation schemes on a per-user or per-application basis. Various session level SLA negotiation schemes involving bandwidth allocation, service start time and service duration parameters are introduced and analyzed. The results show that these negotiation schemes can be utilized for the benefit of both end users and network providers in achieving the highest individual SLA optimization in terms of key Quality of Service (QoS) metrics and price. The inherent characteristics of software agents such as autonomy, adaptability and social abilities offer many advantages in this dynamic, complex, and distributed network environment especially when performing Service Level Agreements (SLA) definition negotiations and brokering tasks. This article also presents a service broker prototype based on Fujitsu's Phoenix Open Agent Mediator (OAM) agent technology, which was used to demonstrate a range of SLA brokering scenarios.

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.