QoS Negotiation and Renegotiation Based on Mobile Agents

As the Quality of Service （QoS） becomes increasingly important in the Internet development, there are more and more researches focusing on the issues of QoS negotiation. Mobile software agents represent a valid alternative to the implementation of strategies for the negotiation. In this paper, a QoS negotiation and renegotiation system architecture based on mobile agents is proposed. These agents perform the task in the whole process and therefore reduce the network load, overcome latency, and avoid frequent exchange information between clients and server. The simulation results show that it can improve the network resource utility about 10%.     

自治Agents的面向服务的协商模型

提出了一个基于自治Agents的面向服务的协商模型。模型主要包括两大模块：首先定义了一系列的战略和战术过程，使得Agents可以产生初始建议，评价对方的建议，以及在此基础上做出反建议；其次，定义了两个层次的Agent通信机制。二者相辅相成，实现了Agents间合作的动态形成和随机协调，使得Agents可以承担因环境的不确定性而需动态协作的复杂任务。     

Impact of Argumentative Negotiation on Collaborative Engineering

Engineering of complex systems involves multiple disciplinary design teams with diversified skills. The team members must work together to make joint decisions, but are often faced with difficulties when trying to reach agreements. Negotiation has been studied as a method for facilitating information exchange, mutual understanding, and joint decision-making. In our previous work, we introduced an argumentative negotiation model to support collaborative engineering. In this paper, we present an experiment study that was conducted to assess the impact of this negotiation support system on the process and the outcome of collaborative design. The results of the experiment have demonstrated the positive effects of the approach.     

An intelligent negotiator agent design for bilateral contracts of electrical energy

In this paper, an intelligent agent (using the Fuzzy SARSA learning approach) is proposed to negotiate for bilateral contracts (BC) of electrical energy in Block Forward Markets (BFM or similar market environments). In the BFM energy markets, the buyers (or loads) and the sellers (or generators) submit their bids and offers on a daily basis. The loads and generators could employ intelligent software agents to trade energy in BC markets on their behalves. Since each agent attempts to choose the best bid/offer in the market, conflict of interests might happen. In this work, the trading of energy in BC markets is modeled and solved using Game Theory and Reinforcement Learning (RL) approaches. The Stackelberg equation concept is used for the match making among load and generator agents. Then to overcome the negotiation limited time problems (it is assumed that a limited time is given to each generator–load pairs to negotiate and make an agreement), a Fuzzy SARSA Learning (FSL) method is used. The fuzzy feature of FSL helps the agent cope with continuous characteristics of the environment and also prevents it from the curse of dimensionality. The performance of the FSL (compared to other well-known traditional negotiation techniques, such as time-dependent and imitative techniques) is illustrated through simulation studies. The case study simulation results show that the FSL based agent could achieve more profits compared to the agents using other reviewed techniques in the BC energy market.     

Enacting agent-based services for automated procurement

Negotiation events in industrial procurement involving multiple, highly customisable goods pose serious challenges to buying agents when trying to determine the best set of providing agents’ offers. Typically, a buying agent's decision involves a large variety of constraints that may involve attributes of a very same item as well as attributes of different, multiple items. In this paper we present iBundler, an agent-aware service offered to buying agents to help them determine the optimal bundle of received offers based on their business rules. In this way, buying agents are relieved with the burden of solving too hard a problem and concentrate on strategic issues. iBundler is intended as a negotiation service for buying agents and as a winner determination service for reverse combinatorial auctions with side constraints. Furthermore, we assess the computational cost added by employing agent technology in the development of iBundler to characterise the type of negotiation scenarios that it can acceptably handle.     

基于移动Agent的自动协商应用

主要研究电子商务中的协商问题,为实现自动协商提供了一个可行的方向。通过分析目前电子商务的协商现状及所存在的问题,提出将移动Agent技术引入电子商务,为B2C电子商务中一对多的协商问题自动化指出了一个实现方向,并得出基于移动Agent的自动协商系统的建设目标及特点。这将对于实现电子商务协商的自动化,提供有力的理论支持及发展方向。     

Negotiating SLAs-An Approach for a Generic Negotiation Framework for WS-Agreement

The current Web Services Agreement specification draft proposes a simple request-response protocol for agreement creation only addressing bilateral offer exchanges. This paper proposes a framework augmenting this WS-Agreement to enable negotiations according to a variety of bilateral and multilateral negotiation protocols. The framework design is based on a thorough analysis of taxonomies for negotiations from the literature in order to allow for capturing a variety of different negotiation models within a single, WS-Agreement compatible, framework. In order to provide for the intended flexibility, the proposed protocol takes a two-stage approach: a meta-protocol is conducted among interested parties to agree on a common negotiation protocol first before the real negotiation is carried out in the second step due to the protocol established in the first step.     

Agent decision-making in open mixed networks

Computer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups vary in their capabilities, goals, and strategies; they may cooperate, collaborate, or compete. The presence of people in mixed networks raises challenges for the design and the evaluation of decision-making strategies for computer agents. This paper describes several new decision-making models that represent, learn and adapt to various social attributes that influence people's decision-making and presents a novel approach to evaluating such models. It identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies. The settings vary in the capabilities, goals, and strategies that people bring into their interactions. The studies deploy a configurable system called Colored Trails (CT) that generates a family of games. CT is an abstract, conceptually simple but highly versatile game in which players negotiate and exchange resources to enable them to achieve their individual or group goals. It provides a realistic analogue to multi-agent task domains, while not requiring extensive domain modeling. It is less abstract than payoff matrices, and people exhibit less strategic and more helpful behavior in CT than in the identical payoff matrix decision-making context. By not requiring extensive domain modeling, CT enables agent researchers to focus their attention on strategy design, and it provides an environment in which the influence of social factors can be better isolated and studied.     

“免疫软件人”概念及其协商控制模型

在研究生物免疫机理和软件人(SM)技术的基础上,将生物免疫特性融入SM技术中,提出了免疫软件人(ISM)的概念,设计了一种能够对动态网络环境进行实时监控和故障预警的多免疫软件人(MISM)联盟的协商控制模型;在对模型进行形式化描述的基础上,构造出一种新颖的ISM协商控制算法.以此构建的MISM联盟系统具有更强的灵活性、鲁棒性和局部更新能力,是一个适用于动态网络环境的自组织系统.     

DipGame: A challenging negotiation testbed

There is a chronic lack of shared application domains to test advanced research models and agent negotiation architectures in Multiagent Systems. In this paper we introduce a friendly testbed for that purpose. The testbed is based on The Diplomacy Game where negotiation and the relationships between players play an essential role. The testbed profits from the existence of a large community of human players that know the game and can easily provide data for experiments. We explain the infrastructure in the paper and make it freely available to the AI community.