Automated Negotiation from Declarative Contract Descriptions

Our approach for automating the negotiation of business contracts proceeds in three broad steps. First, determine the structure of the negotiation process by applying general knowledge about auctions and domain–specific knowledge about the contract subject along with preferences from potential buyers and sellers. Second, translate the determined negotiation structure into an operational specification for an auction platform. Third, after the negotiation has completed, map the negotiation results to a final contract. We have implemented a prototype which supports these steps by employing a declarative specification (in courteous logic programs) of (1) high–level knowledge about alternative negotiation structures, (2) general–case rules about auction parameters, (3) rules to map the auction parameters to a specific auction platform, and (4) special–case rules for subject domains. We demonstrate the flexibility of this approach by automatically generating several alternative negotiation structures for the domain of travel shopping in a trading agent competition.     

Contracting With Uncertain Level Of Trust

The paper investigates the impact of trust on market efficiency and bilateral contracts. We prove that a market in which agents are trusted to the degree they deserve to be trusted is as efficient as a market with complete trustworthiness. In other words, complete trustworthiness is not a necessary condition for market efficiency. We prove that distrust could significantly reduce market efficiency, and we show how to solve the problem by using appropriately designed multiagent contracts. The problem of trust is studied in the context of a bilateral negotiation game between a buyer and a seller. It is shown that if the seller's trust equals the buyer's trustworthiness, then the social welfare, the amount of trade, and the agents' utility functions are maximized. The paper also studies the efficiency of advance payment contracts as a tool for improving trustworthiness. It is proved that advance payment contracts maximize the social welfare and the amount of trade. Finally, the paper studies the problem of how to make agents truthfully reveal their level of trustworthiness. An incentive–compatible contract is defined, in which agents do not benefit from lying about their trustworthiness. The analysis and the solutions proposed in this paper could help agent designers avoid many market failures and produce efficient interaction mechanisms.     

一种优化的基于增强学习协商策略

增强学习可以帮助协商Agent选择最优行动实现其最终目标。对基于增强学习的协商策略进行优化,在协商过程中充分利用对手的历史信息,加快协商解的收敛和提高协商解的质量。最后通过实验验证了算法的有效性和可用性。     

MTO型供应链的价格/交货期协商支持模型

针对MTO(make-to-order)供应链环境下制造商与供应商的多个订单的价格/交货期协商问题，基于供应链伙伴间关系是竞争性合作的特点，提出一种新的两阶段协商议程．在合作性协商阶段，中介者利用模拟退火算法帮助制造商和供应商寻找最小化供应链总成本的交货期预协议点；制造商和供应商在此基础上基于整合效用的思想调整价格议题的保留值和期望值．在竞争性协商阶段双方逐步让步，就价格达成协议．实验表明，该协商议程能够获得近似最优的社会福利，达成对协商人双赢的方案．该协商议程能够有效应用于供应链协调和B2B在线市场．     

Agent-based multimedia presentation and adaptation service

In this paper, we aim to provide adaptive multimedia services especially video ones to end-users in an efficient and secure manner. Users moving outside the office should be able to maintain an office-like environment at their current locations. First, the agents within our proposed architecture negotiate the different communication and interaction factors autonomously and dynamically. Moreover, we needed to develop a user agent in addition to service and system agents that could negotiate the requirements and capabilities at run time to furnish best possible service results. Thus we designed and integrated a video indexing and key framing service within our overall agent-based architecture. We integrated this video indexing and content-based analysis service to adapt the video content according to run time conditions. We designed a video XML schema to validate the media content out of this multimedia service according to specific requirements and features, as we will describe later.

A heuristic algorithm for distributed control in manufacturing systems

Distributed Problem Solving Networks (DPSN) provide a means for interconnecting intelligent problem solver nodes that can solve only a part of a problem depending on their ability in the problem domain. The decomposition of a problem into subproblems, and the selection of nodes to solve them can be regarded as the generation of an AND/OR tree, and the solution of the problem as a search for a solution tree. Introducing measurements for the cost of a solution tree, we present an algorithm to find one having minimal cost under certain conditions. A Flexible Manufacturing System consisting of a network of flexible workcells is used as an example.     

Managing commitments in multiple concurrent negotiations

Automated negotiation by software agents is a key enabling technology for agent mediated e-commerce. To this end, this paper considers an important class of such negotiations – namely those in which an agent engages in multiple concurrent bilateral negotiations for a good or service. In particular, we consider the situation in which a buyer agent is looking for a single service provider from a number of available ones in its environment. By bargaining simultaneously with these providers and interleaving partial agreements that it makes with them, a buyer can reach good deals in an efficient manner. However, a key problem in such encounters is managing commitments since an agent may want to make intermediate deals (so that it has a definite agreement) with other agents before it gets to finalize a deal at the end of the encounter. To do this effectively, however, the agents need to have a flexible model of commitments that they can reason about in order to determine when to commit and to decommit. This paper provides and evaluates such a commitment model and integrates it into a concurrent negotiation model.     

Collaborative assignment using belief-desire-intention agent modeling and negotiation with speedup strategies

In this paper, we propose a distributed agent model that applies belief-desire-intention (BDI) reasoning and negotiation for addressing the linear assignment problem (LAP) collaboratively. In resource allocation, LAP is viewed as seeking a concurrent allocation of one different resource for every task to optimize a linear sum objective function. The proposed model provides a basic agent-based foundation needed for efficient resource allocation in a distributed environment. A distributed agent algorithm that has been developed based on the BDI negotiation model is examined both analytically and experimentally. To improve performance in terms of average negotiation speed and solution quality, two initialization heuristics and two different reasoning control strategies are applied, with the latter yielding different variants of the basic algorithm. Extensive simulations suggest that all the heuristic-algorithm combinations can produce a near optimal solution soon enough in some specific sense. The significance and applicability of the research work are also discussed.     

属性群签名的可信服务选择方案

面向服务计算(service-oriented computing,SOC)的一个基本内容就是选择和组合服务以直接支持指定用户的需求或提供附加服务,这些需求往往是细粒度的.现有的服务选择方法要么没有考虑要么是从粗粒度上考虑服务间的信任关系.我们提出一个新的可信服务选择方法:基于属性群签名的可信服务选择方案(TSS-ABGS).该方案将功能属性相同的可选服务组成一个属性群,当请求者向该群发出包含属性集的选择请求时,满足属性集要求的群成员返回一个签名,请求者通过群管理员开放签名者并与之建立可信关系而达到可信服务选择的目的.该方法具有可跟踪性和防共谋.     

自动信任协商中一种最小信任披露策略

自动信任协商是通过逐渐请求和披露数字证书在两个陌生实体间建立相互信任的方法。当前对自动信任协商协商策略的研究,往往存在很多问题。文章提出了一种基于期望因子的自动信任协商模型,该模型采用MCD策略,通过分析各信任证书的期望因子,当可能的协商存在时,它能披露和请求最小的信任证找到一条成功的协商路径,当协商不可能成功时,尽快的发现并终止它。它能保证在协商的过程中没有不相关的信任证被披露并且不需要暴露双方的访问控制策略,同时通过实验证了明MCD策略是完备的。