Fuzzy Logic Based Negotiation in E-Commerce

The evolution of multi-agent system (MAS) presents new challenges in computer science and software engineering. A particularly challenging problem is the design of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is dealing with negotiation in e-commerce: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, we present a fuzzy logic-based negotiation approach to automate multi-issue bilateral negotiation in e-marketplaces. In such frameworks issues to negotiate on can be multiple, interrelated, and may not be fixed in advance. Therefore, we use fuzzy inference system to model relations among issues and to allow agents express their preferences on them. We focus on settings where agents have limited or uncertain information, ruling them out from making optimal decisions. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs then applying logic (by using fuzzy logic) over these reasons can enable agents to refine their decisions and consequently reach better agreements. I refer to this form of negotiation as: Fuzzy logic based negotiation in e-commerce. The contributions of the thesis begin with the use of fuzzy logic to design a reasoning model through which negotiation tactics and strategy are expressed throughout the process of negotiation. Then, an exploration of the differences between this approach and the more traditional bargaining-based approaches is presented. Strategic issues are then explored and a methodology for designing negotiation strategies is developed. Finally, the applicability of the framework is simulated using MATLAB toolbox

Fuzzy Logic Based Negotiation in E-Commerce

The evolution of multi-agent system (MAS) presents new challenges in computer science and software engineering. A particularly challenging problem is the design of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is dealing with negotiation in e-commerce: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, we present a fuzzy logic-based negotiation approach to automate multi-issue bilateral negotiation in e-marketplaces. In such frameworks issues to negotiate on can be multiple, interrelated, and may not be fixed in advance. Therefore, we use fuzzy inference system to model relations among issues and to allow agents express their preferences on them. We focus on settings where agents have limited or uncertain information, ruling them out from making optimal decisions. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs then applying logic (by using fuzzy logic) over these reasons can enable agents to refine their decisions and consequently reach better agreements. I refer to this form of negotiation as: Fuzzy logic based negotiation in e-commerce. The contributions of the thesis begin with the use of fuzzy logic to design a reasoning model through which negotiation tactics and strategy are expressed throughout the process of negotiation. Then, an exploration of the differences between this approach and the more traditional bargaining-based approaches is presented. Strategic issues are then explored and a methodology for designing negotiation strategies is developed. Finally, the applicability of the framework is simulated using MATLAB toolbox

Development of a Fuzzy-based Multi-agent System for E-commerce Settings

AbstractIn this paper we present our experience in developing a fuzzy-logic based multi-agent e-commerce system capable of achieving a mutually beneficial deal for the seller and buyer using a negotiation process. We use fuzzy logic to assist users to express their preferences about a product in fuzzy terms such as low, medium and high. Our system evaluates offers based on a fuzzy utility function and feeds utility scores to a fuzzy inference system which then computes its next counter offer. Our paper presents issues involved in the development of a multi-agent system for e-commerce settings using the JADE platform - a modern agent development environment. In this paper our focus is on implementing agents of different types/roles engaged in activities usually encountered with buying and selling in an e-commerce environment. Our concluding remarks and future research are presented

Production/maintenance cooperative scheduling using multi-agents and fuzzy logic

Within companies, production is directly concerned with the manufacturing schedule, but other services like sales, maintenance, purchasing or workforce management should also have an influence on this schedule. These services often have together a hierarchical relationship, i.e. the leading function (most of the time sales or production) generates constraints defining the framework within which the other functions have to satisfy their own objectives. We show how the multi-agent paradigm, often used in scheduling for its ability to distribute decision-making, can also provide a framework for making several functions cooperate in the schedule performance. Production and maintenance have been chosen as an example: having common resources (the machines), their activities are actually often conflicting. We show how to use a fuzzy logic in order to model the temporal degrees of freedom of the two functions, and show that this approach may allow one to obtain a schedule that provides a better compromise between the satisfaction of the respective objectives of the two functions

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

This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.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.The Ministry of Education, Science and Technology (Korea

KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

Using Similarity Criteria to Make Negotiation Trade-Offs

This paper addresses the issues involved in software agents making trade-offs during automated negotiations in which they have information uncertainty and resource limitations. In particular, the importance of being able to make trade-offs in real-world applications is highlighted and a novel algorithm for performing trade-offs for multi-dimensional goods is developed. The algorithm uses the notion of fuzzy similarity in order to find negotiation solutions that are beneficial to both parties. Empirical results indicate the benefits and effectiveness of the trade-off algorithm in a range of negotiation situations

Automated Negotiation for Provisioning Virtual Private Networks Using FIPA-Compliant Agents

This paper describes the design and implementation of negotiating agents for the task of provisioning virtual private networks. The agents and their interactions comply with the FIPA specification and they are implemented using the FIPA-OS agent framework. Particular attention is focused on the design and implementation of the negotiation algorithms

Risk-bounded formation of fuzzy coalitions among service agents.

Cooperative autonomous agents form coalitions in order ro share and combine resources and services to efficiently respond to market demands. With the variety of resources and services provided online today, there is a need for stable and flexible techniques to support the automation of agent coalition formation in this context. This paper describes an approach to the problem based on fuzzy coalitions. Compared with a classic cooperative game with crisp coalitions (where each agent is a full member of exactly one coalition), an agent can participate in multiple coalitions with varying degrees of involvement. This gives the agent more freedom and flexibility, allowing them to make full use of their resources, thus maximising utility, even if only comparatively small coalitions are formed. An important aspect of our approach is that the agents can control and bound the risk caused by the possible failure or default of some partner agents by spreading their involvement in diverse coalitions

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications