Agent negotiation based ontology refinement process and mechanisms for service applications

Nowadays organisations are willing to outsource their business processes as services and make them accessible via the Web. In doing so, they can dynamically combine individual services to their service applications. However, unless the data on the Web can be meaningfully shared and is interpretable, this objective cannot be realised. In this paper, a new agent-based approach for managing ontology evolution in a Web services environment is exploited. The proposed approach has several key characteristics such as flexibility and extensibility that differentiate this research from others. The refinement mechanisms which cope with an evolving ontology are carefully examined. The novelty of our work is that inter-processes between different ontologies are studied from the agent’s perspective. Based on this perspective, an agent negotiation model is applied to reach an agreement regarding ontology discrepancy in an application. The efficiency and effectiveness of reaching an agreement over an ontology dispute is leveraged by the private negotiation strategy applied in the argumentation approach. An extended negotiation strategy is discussed to enable sufficient information in decision making at each negotiation round. A case study is presented to demonstrate ontology refinement in a Web services environment.     

On the merging of Dung's argumentation systems

In this paper, the problem of deriving sensible information from a collection of argumentation systems coming from different agents is addressed. The underlying argumentation theory is Dung's one: each argumentation system gives both a set of arguments and the way they interact (i.e., attack or non-attack) according to the corresponding agent. The inadequacy of the simple, yet appealing, method which consists in voting on the agents' selected extensions calls for a new approach. To this purpose, a general framework for merging argumentation systems from Dung's theory of argumentation is presented. The objective is achieved through a three-step process: first, each argumentation system is expanded into a partial system over the set of all arguments considered by the group of agents (reflecting that some agents may easily ignore arguments pointed out by other agents, as well as how such arguments interact with her own ones); then, merging is used on the expanded systems as a way to solve the possible conflicts between them, and a set of argumentation systems which are as close as possible to the whole profile is generated; finally, voting is used on the selected extensions of the resulting systems so as to characterize the acceptable arguments at the group level.     

Handling threats,rewards, and explanatory arguments in a unified setting

Current logic‐based handling of arguments has mainly focused on explanation or justification‐oriented purposes in presence of inconsistency. So only one type of argument has been considered, and several argumentation frameworks have then been proposed for generating and evaluating such arguments. However, recent works on argumentation‐based negotiation have emphasized different other types of arguments such as threats, rewards, and appeals. The purpose of this article is to provide a logical setting that encompasses the classical argumentation‐based framework and handles the new types of arguments. More precisely, we give the logical definitions of these arguments and their weighting systems. These definitions take into account that negotiation dialogues involve not only agents' beliefs (of various strengths), but also their goals (having maybe different priorities), as well as the beliefs on the goals of other agents. In other words, from the different beliefs and goals bases maintained by agents, all the possible threats, rewards, explanations, and appeals that are associated with them can be generated. It may also happen that an intended threat, or reward, is not perceived as such by the addressee and thus misses its target because the addresser misrepresents the addressee's goals. The proposed approach accounts for that phenomenon. Finally, we show how to evaluate conflicting arguments of different types. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1195–1218, 2005.     

Argumentation-based negotiation planning for autonomous agents

When we negotiate, the arguments uttered to persuade the opponent are not the result of an isolated analysis, but of an integral view of the problem that we want to agree about. Before the negotiation starts, we have in mind what arguments we can utter, what opponent we can persuade, which negotiation can finish successfully and which cannot. Thus, we plan the negotiation, and in particular, the argumentation. This fact allows us to take decisions in advance and to start the negotiation more confidently. With this in mind, we claim that this planning can be exploited by an autonomous agent. Agents plan the actions that they should execute to achieve their goals. In these plans, some actions are under the agent's control, while some others are not. The latter must be negotiated with other agents. Negotiation is usually carried out during the plan execution. In our opinion, however, negotiation can be considered during the planning stage, as in real life. In this paper, we present a novel approach to integrate argumentation-based negotiation planning into the general planning process of an autonomous agent. This integration allows the agent to take key decisions in advance. We evaluated this proposal in a multiagent scenario by comparing the performance of agents that plan the argumentation and agents that do not. These evaluations demonstrated that performance improves when the argumentation is planned, specially, when the negotiation alternatives increase.     

LEARNING DRIFTING NEGOTIATIONS

In this work, we propose the use of drift detection techniques for learning offer policies in multiissue, bilateral negotiation. Several works aiming to develop adaptive trading agents have been proposed. Such agents are capable of learning their competitors' utility values and functions, thereby obtaining better results in negotiation. However, the learning mechanisms generally used disregard possible changes in a competitor's offer/counter-offer policy. In that case, the agent's performance may decrease drastically. The agent then needs to restart the learning process, as the model previously learned is no longer valid. Drift detection techniques can be used to detect changes in the current offers model and quickly update it. In this work, we demonstrate with simulated data that drift detection algorithms can be used to build adaptive trading agents and offer a number of advantages over the techniques mostly used in this problem. The results obtained with the algorithm IB3 (instance-based) show that the agent's performance can be rapidly recovered even when changes interesting to the competitor are abrupt, moderate, or gradual.     

A supplier offer modification approach based on fuzzy systems for automated negotiation in e-commerce

In E-commerce, numbers of transactions are increasing day by day in B2B and B2C trade. Online negotiation is possible because of automated negotiation. Autonomous entities such as agents could help in these situations. Providing an offer which has maximum utilities for both trading parties into possible shortest time is the main aim of this work. Proposed model applies issue trade-offs strategy in which multiple issues are traded-offs against one another to maximize participant satisfaction. To make trade-offs the model applies a fuzzy system approach. The automated negotiation model in this article has a process without offer generating and exchanging between buyer and supplier agents to explore time-consuming negotiation process in earlier researches. Mediator component searches an optimal offer that satisfies buyer and supplier requirements. The system will utilize fuzzy inference systems to automate negotiation process and considers two effective factors in the negotiation process: requirements and preferences. Requirements are qualitative or quantitative values which the participants assign to issues for negotiation. Preferences of the participants are priorities assigned to the issues. These values express an importance measure of the issues from a participant perspective. Analytic hierarchy process (AHP) is used to get preferences of the issues. Proposed model applies different Fuzzy Inference System (FIS) schemes for qualitative and quantitative negotiation issues to enhance the satisfaction level of buyers and suppliers. Experimental results show that how the model fulfills the main aim of our work.     

Hybrid job offer recommender system in a social network

Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.     

Cooperative-Competitive Healthcare Service Negotiation

Service negotiation is a complex activity, especially in complex domains such as healthcare. The provision of healthcare services typically involves the coordination of several professionals with different skills and locations. There is usually negotiation between healthcare service providers as different services have specific constraints, variables, and features (scheduling, waiting lists, availability of resources, etc.), which may conflict with each other. While automating the negotiation processes by using software can improve the effciency and quality of healthcare services, most of the existing negotiation automations are positional bargaining in nature, and are not suitable for complex scenarios in healthcare services. This paper proposes a cooperative-competitive negotiation model that enables negotiating parties to share their knowledge and work toward optimal solutions. In this model, patients and healthcare providers work together to develop a patient-centered treatment plan. We further automate the new negotiation model with software agents.     

Compromising Adjustment Strategy Based on TKI Conflict Mode for Multi‐Times Bilateral Closed Negotiations

Bilateral multi‐issue closed negotiation is an important class for real‐life negotiations. Usually, negotiation problems have constraints such as a complex and unknown opponent's utility in real time, or time discounting. In the class of negotiation with some constraints, the effective automated negotiation agents can adjust their behavior depending on the characteristics of their opponents and negotiation scenarios. Recently, the attention of this study has focused on the interleaving learning with negotiation strategies from the past negotiation sessions. By analyzing the past negotiation sessions, agents can estimate the opponent's utility function based on exchanging bids. In this article, we propose a negotiation strategy that estimates the opponent's strategies based on the past negotiation sessions. Our agent tries to compromise to the estimated maximum utility of the opponent by the end of the negotiation. In addition, our agent can adjust the speed of compromise by judging the opponent's Thomas–Kilmann conflict mode and search for the Pareto frontier using past negotiation sessions. In the experiments, we demonstrate that the proposed agent has better outcomes and greater search technique for the Pareto frontier than existing agents in the linear and nonlinear utility functions.     

A Dialogue Game Protocol for Agent Purchase Negotiations

We propose a dialogue game protocol for purchase negotiation dialogues which identifies appropriate speech acts, defines constraints on their utterances, and specifies the different sub-tasks agents need to perform in order to engage in dialogues according to this protocol. Our formalism combines a dialogue game similar to those in the philosophy of argumentation with a model of rational consumer purchase decision behaviour adopted from marketing theory. In addition to the dialogue game protocol, we present a portfolio of decision mechanisms for the participating agents engaged in the dialogue and use these to provide our formalism with an operational semantics. We show that these decision mechanisms are sufficient to generate automated purchase decision dialogues between autonomous software agents interacting according to our proposed dialogue game protocol.     

STRATEGIC NEGOTIATION FOR SHARING A RESOURCE BETWEEN TWO AGENTS*

In this article, we propose a strategic negotiation model that enables self‐motivated rational agents to share resources. The strategic negotiation model takes the passage of time during the negotiation process itself into account. The model considers bilateral negotiations in situations characterized by complete information, in which one agent loses over time whereas the other gains over time. Using this negotiation mechanism, autonomous agents apply simple and stable negotiation strategies that result in efficient agreements without delay, even when there are dynamic changes in the environment. Simulation results show that our mechanism performs as well as a centralized scheduler and also has the property of balancing the resources' usage.     

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。     

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.     

A price- and-time-slot-negotiation mechanism for Cloud service reservations

When making reservations for Cloud services, consumers and providers need to establish service-level agreements through negotiation. Whereas it is essential for both a consumer and a provider to reach an agreement on the price of a service and when to use the service, to date, there is little or no negotiation support for both price and time-slot negotiations (PTNs) for Cloud service reservations. This paper presents a multi-issue negotiation mechanism to facilitate the following: 1) PTNs between Cloud agents and 2) tradeoff between price and time-slot utilities. Unlike many existing negotiation mechanisms in which a negotiation agent can only make one proposal at a time, agents in this work are designed to concurrently make multiple proposals in a negotiation round that generate the same aggregated utility, differing only in terms of individual price and time-slot utilities. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Using the testbed, experiments were carried out to compare this work with related approaches. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches. A case study was carried out to demonstrate the application of the PTN mechanism for pricing Cloud resources.     

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。     

Negotiation in a multi-agent system for construction claims negotiation

Negotiation is an important approach for agents to co-operate and reach agreement in multiagent systems (MAS). Different negotiation theories and models have been deployed in a variety of applications. This paper is concerned with the applicability of these theories to the domain of agent-based construction claims negotiation. The peculiarities of this domain are highlighted and the approach adopted in the development of a multi-agent system for construction claims negotiation (MASCOT) described. Of particular interest is the integration of Zeuthen's bargaining model with a Bayesian learning mechanism, which addresses the characeristics of the construction claims negotiation. Examples are presented to demonstrate the impact of various negotiation approaches on the conduct and outcome of construction claims negotiations.     

Equilibria, prudent compromises, and the "waiting" game.

While evaluation of many e-negotiation agents are carried out through empirical studies, this work supplements and complements existing literature by analyzing the problem of designing market-driven agents (MDAs) in terms of equilibrium points and stable strategies. MDAs are negotiation agents designed to make prudent compromises taking into account factors such as time preference, outside option, and rivalry. This work shows that 1) in a given market situation, an MDA negotiates optimally because it makes minimally sufficient concession, and 2) by modeling negotiation of MDAs as a game gamma of incomplete information, it is shown that the strategies adopted by MDAs are stable. In a bilateral negotiation, it is proven that the strategy pair of two MDAs forms a sequential equilibrium for gamma. In a multilateral negotiation, it is shown that the strategy profile of MDAs forms a market equilibrium for gamma.     

A two-step approach to building bilateral consensus between agents based on relationship learning theory

Researchers are increasingly focusing on the agent based approach to transaction support in ubiquitous commerce. These agents work autonomously to maximize utility on the user’s behalf. In the case of a cooperative game, rather than a win–lose zero-sum game, agents may negotiate with each other or have a negotiating agent provide a suggestion that can be reasonably accepted by the dyad to build a consensus. In this paper we propose a novel methodology that increases agent performance in terms of costs associated with building consensus and successful negotiation rates. To do so, we develop a two-step approach: joint learning and negotiation to consensus building. We also conduct an experimental study to show the feasibility of the methodology.     

基于Agent辩论谈判的奖励模型研究

电子商务谈判过程中,由于能使接受辩论的Agent根据它所接受到的辩论改变其偏好或目标,从而使谈判双方更快更好地达成一致,因而基于辩论的Agent谈判方式受到了广泛关注。目前已经有文献对此作了一定程度的研究,然而较少有对其中客观存在的威胁、奖励和申辩进行有效地形式化建模和描述。目标主要在于研究其中的奖励模型和其评价模型,然后在此基础上对奖励的评价模型进行模拟和分析,以评价其辩论力度的强弱,使被奖励对象最终做出正确选择。     

A taxonomy of argumentation models used for knowledge representation

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.