An adaptive approach for decision making tactics in automated negotiation

In this paper, an adaptive decision making approach of three families of tactics has been proposed for bilateral negotiation: the time dependent tactics, behavior dependent tactics, and time independent tactics. These tactics are more adaptive to the environment’s changes (reservation interval, time deadline, opponent behavior). The suggested time dependent tactics take advantage from round based time continuity and dynamics aspects (features) integrated in their modelling. For suggested behavior dependent tactics, a new formalization based on the percentage of change was introduced, which helps agents to be more prudent in the environments with incomplete information comparing to previous behavior dependent tactics suggested by Faratin et al. (Int. J. Robotics Auton. Syst. 24(3–4):159–182, 1998). Concerning the new family of tactics which are completely independent from time, the agents compute their offers based on their reservation interval. These tactics are useful when there is no time deadline and, in addition, when the behavior of opponent agents doesn’t follow any negotiation equilibrium. Moreover, new experimental measures are suggested which are more useful for final evaluation. The experiments conducted in this paper, prove the applicability of all three families of tactic.     

A TWO‐STAGE WIN–WIN MULTIATTRIBUTE NEGOTIATION MODEL: OPTIMIZATION AND THEN CONCESSION

Many automated negotiation models have been developed to solve the conflict in many distributed computational systems. However, the problem of finding win–win outcome in multiattribute negotiation has not been tackled well. To address this issue, based on an evolutionary method of multiobjective optimization, this paper presents a negotiation model that can find win–win solutions of multiple attributes, but needs not to reveal negotiating agents’ private utility functions to their opponents or a third‐party mediator. Moreover, we also equip our agents with a general type of utility functions of interdependent multiattributes, which captures human intuitions well. In addition, we also develop a novel time‐dependent concession strategy model, which can help both sides find a final agreement among a set of win–win ones. Finally, lots of experiments confirm that our negotiation model outperforms the existing models developed recently. And the experiments also show our model is stable and efficient in finding fair win–win outcomes, which is seldom solved in the existing models.     

An evolutionary compensatory negotiation model for distributed dynamic scheduling

Although a considerable amount of efforts has been devoted to developing optimum negotiation for dynamic scheduling, most of them are inappropriate for the non-cooperative, self-interested participants in a distributed project for practical purpose. In this paper, an agent-based approach with a mutual influencing, many-issue, one-to-many-party, compensatory negotiation model is proposed. In the model, the activity agents possess various negotiation tactics and strategies formed by respective self-interested owner's subjective preference, aim to find the contracts of schedule adjustment mutually acceptable to respective participant's acquaintance while encountering conflicts over rescheduling settlement. In order to find the fitting negotiation strategies that are optimally adapted for each activity agent, an evolutionary computation approach that encodes the parameters of tactics and strategies of an agent as genes in GAs is also addressed. In the final, a prototype with a case discussed in researches is evaluated to validate the feasibility and applicability of the model, and some characteristics and future works are also exhibited.     

基于时间约束的不完全信息的战略协商模型

在实证的一对一协商中，协商Agent不仅要面临自己的最后期限的压力，同时又要预测协商对手的最后期限和其类型，协商Agent的协商战略必须满足理性与均衡的要求。提出了通过形式化的方法建立轮流出价协商模型，给出了轮流出价协商战略均衡的条件定义，求出了基于时间限制的不完全信息环境下满足均衡组合的协商战略，建立了依据均衡战略的实用化协商算法，最后分析了该算法产生的实验数据，并在相同环境下与Zeus协商模型比较显示，依从本模型的均衡战略的协商Agent能根据对对手的不确定信息的信念动态地采取行动，以获得最大的期望收益。     

一种多Agent多议题的协商模型

协商是多Agent系统实现协作、协调和冲突消解的关键技术。本文分析了协商问题的实质和协商过程,提出了一种支持多轮协商的多Agent多议题协商模型。模型中引入了Agent类型的概念,在信息不完全的条件下,协商Agent通过推测协商对手的类型来指导自身的提议策略和协商战术,使提议更具针对性,避免了盲目性,从而节约了协商时间,提高了协
商质量。     

Efficient Management of Multi-Linked Negotiation Based on a Formalized Model

A Multi-linked negotiation problem occurs when an agent needs to negotiate with multiple other agents about different subjects (tasks, conflicts, or resource requirements), and the negotiation over one subject has influence on negotiations over other subjects. The solution of the multi-linked negotiations problem will become increasingly important for the next generation of advanced multi-agent systems. However, most current negotiation research looks only at a single negotiation and thus does not present techniques to manage and reason about multi-linked negotiations. In this paper, we first present a technique based on the use of a partial-order schedule and a measure of the schedule, called flexibility, which enables an agent to reason explicitly about the interactions among multiple negotiations. Next, we introduce a formalized model of the multi-linked negotiation problem. Based on this model, a heuristic search algorithm is developed for finding a near-optimal ordering of negotiation issues and their parameters. Using this algorithm, an agent can evaluate and compare different negotiation approaches and choose the best one. We show how an agent uses this technology to effectively manage interacting negotiation issues. Experimental work is presented which shows the efficiency of this approach.     

基于多Agent协商的虚拟企业伙伴选择方法

伙伴选择是虚拟企业建立过程中的核心问题,分析了虚拟企业的特点、虚拟企业环境下协商问题的特点,提出了一个适合于虚拟企业环境的多Agent协商模型。该模型支持多Agent多议题的多轮谈判,并将Agent类型引入到协商中来,作为指导协商Agent提议的一个重要因素。在不完全信息的条件下,应用贝叶斯学习的方法,更新既有信息,并通过分析对方Agent的历史提议序列,推测其类型,来指导自身的提议策略和战术,使自己的提议更具有针对性,避免了盲目性,从而节约协商时间,提高了协商的效率,使得盟主企业能在尽短的时间里寻找到理想的合作伙伴。     

“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.     

一个基于多阶段的多Agent多问题协商框架

多问题协商是电子交易中的关键问题．多Agent技术的不断成熟为这个问题的解决提供了有效的途径．提出了一个以理性Agent为基础的基于多阶段的多问题协商框架,该框架在时间约束下适用于信息不完全的场景,它描述了多问题的价格协商．为了降低多问题协商的复杂性,它将多问题协商分解为多阶段协商,每个阶段的大小（问题数）相同．阶段数和顺序在协商前确定,每个阶段中的问题顺序在协商中确定．在阶段大小相同的情况下,对给定协商问题的分解,框架能给出优化协商议程（agenda）．尤其是框架能为参与协商的Agent建立学习系统（LS）,以增强Agent的学习能力．最后基于这个框架实现了一个原型系统,原型系统证明这个框架是有效的．     

Modeling driver behavior in a cognitive architecture

OBJECTIVE: This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system. BACKGROUND: Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. METHOD: An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment. RESULTS: This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. CONCLUSION: The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks. APPLICATION: The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.     

Agent based mobile negotiation for personalized pricing of last minute theatre tickets

This paper proposes an agent based mobile negotiation framework for personalized pricing of last minutes theatre tickets whose values are dependent on the time remaining to the performance and the locations of potential customers. In particular, case based reasoning and fuzzy cognitive map techniques are adopted in the negotiation framework to identify the best initial offer zone and adopt multi criteria decision in the scoring function to evaluate offers. The proposed framework is tested via a computer simulation in which personalized pricing policy shows higher market performance than other policies therefore the validity of the proposed negotiation framework.     

基于模糊认知图的动态网格分布式资源调度

论文利用多代理的理论,通过网格节点自主选择任务来实现网格系统中的资源优化调度。由于各节点的自主性,对于任务分配方案将存在不同的支持度。论文采用模糊认知图,建立了对任务分配方案的支持度的协调控制策略,并在此基础上给出了一种智能的网格资源调度策略。考虑到各个网格节点的资源配置各不相同,提出了标准支持度的概念,保证了支持度协调策略的可行性和有效性。该调度策略无需存在处于上层的资源调度单元,各节点通过协调对任务分配方案的标准支持度即可实现网格资源的优化调度。该策略适用于分布式计算,并支持网格节点的动态变化,具有较好的实时性和鲁棒性。     

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.     

Adaptive Probabilistic Behavioural Learning System for the effective behavioural decision in cloud trading negotiation market

In cloud e-commerce application, building an automated negotiation strategy by understanding the uncertain information of the opponent preferences, utilities, and tactics is highly challenging. The key issue is to analyse and predict the uncertain behaviour of the opponent tactics to suggest the appropriate counter tactics that can reach maximum consensus. To handle such uncertain information, negotiation strategies follow several tactics with and without learning ability. Strategies without learning ability are restricted to negotiate with the opponent having only deterministic behaviour. To overcome this problem most researchers exploited the negotiation strategies with fixed learning ability using Bayesian learning, neural network learning, and genetic tactics. These tactics can learn the opponent’s behaviour and cannot guarantee to generate suitable counter-offer for all offers submitted by the opponent cloud service provider. This limitation motivates to propose a novel Adaptive Probabilistic Behavioural Learning System for managing the opponent having unpredictable random behaviours. The proposed Adaptive Probabilistic Behavioural Learning System contains a Behavioural Inference Engine to analyse the sequence of negotiation offer received by the broker for effectively learning the opponent’s behaviour over several stages of negotiation process. It also formulates the multi-stage Markov decision problem to suggest the broker with appropriate counter-offer behavioural tactics generation based on the adaptive probabilistic decision taken over the corresponding negotiation stage. Therefore, this research work can outperform the existing fixed behavioural learning tactics and hence maximize the utility value and success rate of negotiating parties without any break-off.     

A Negotiation Meta Strategy Combining Trade-off and Concession Moves

In this paper we present a meta strategy that combines two negotiation tactics. The first one based on concessions, and the second one, a trade-off tactic. The goal of this work is to demonstrate by experimental analysis that the combination of different negotiation tactics allows agents to improve the negotiation process and as a result, to obtain more satisfactory agreements. The scenario proposed is based on two agents, a buyer and a seller, which negotiate over four issues. The paper presents the results and analysis of the meta strategy’s behaviour.     

多智能体系统中的协商模型

目前，对多智能体系统的应用多是以开放的、复杂的、变化的网络为平台，为了更好地与环境相匹配，同对提高智能体之间协商的效率，从全局与单个智能体两方面着手，提出了一种新的MAS全局模型，并相应地提出了全局协调Agent的三维模型以及智能体之间的协商模型。通过这一系列模型的模拟实现，证明了此协商模型是正确的与接近自然的。从理论上基本上解决了与当前流行的基于网络的各种先进的智能系统，分布式问题求解等领域的衔接，因而有一定的指导意义并具有广阔的发展前景。     

多Agen t 协商行为的效用分析

给出多Agent协商行为的一种统计模型以及效用函数的表达式，从统计的角度分析了多Agent协商行为的行为效用，并给出了相关参数的定性分析，从而为更好地设计多Agent系统的协商组织规则和协商策略提供了效用依据．     

Autonomous negotiation for resource allocation of multiple sectors in the TFT-LCD manufacturing industry

In a global TFT-LCD manufacturing enterprise, a central planning sector promises orders to maximize profit, while the production sector is responsible for minimizing costs and meeting delivery deadline. The two sectors frequently struggle with different preferences. The contradictory objectives raise conflict between the two sectors when developing an entire resource allocation plan for the enterprise. This study presents a novel negotiation framework and develops a mutually acceptable resource allocation plan via autonomous negotiation between sectors with different preferences. A mathematical model considering the major characters of the TFT-LCD industry is formulated. Individual sectors can employ preferred negotiation tactics to achieve their objectives with an acceptable level of trade-off. This study examines various negotiation tactics and compares the proposed decentralized model with a centralized solution. Negotiation experiments demonstrate a good resource allocation plan over a short time and the conflict between sectors can be resolved efficiently. The proposed autonomous negotiation facilitates smart operations management.     

Negotiation decision functions for autonomous agents

We present a formal model of negotiation between autonomous agents. The purpose of the negotiation is to reach an agreement about the provision of a service by one agent for another. The model defines a range of strategies and tactics that agents can employ to generate initial offers, evaluate proposals and offer counter proposals. The model is based on computationally tractable assumptions, demonstrated in the domain of business process management and empirically evaluated.     

A Market–Driven Model for Designing Negotiation Agents

Although there are many extant agent–based systems for negotiation in e–commerce, the negotiation strategies of agents in these systems are mostly static. This article presents a model for designing negotiation agents that make adjustable rates of concession by reacting to changing market situations. To determine the amount of concession for each trading cycle, these market–driven agents are guided by four mathematical functions of eagerness, trading time, trading opportunity , and competition . Trading opportunity is determined by considering: (i) number of trading partners, (ii) spreads —differences in utilities between an agent and its trading partners, and (iii) probability of completing a deal. Competition is determined by the probability that an agent is not considered the most preferred trader by other negotiating parties. Motivated by factors such as corporate policies and resource needs, eagerness represents an agent's desire to complete a deal. Agents with different time sensitivity to deadlines employ different trading strategies by making different rates of concession at different stages of negotiation. In this article, three classes of strategies with respect to remaining trading time are discussed. Theoretical analyses show that market–driven agents are designed to make prudent and appropriate amounts of concession for a given market situation.