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

Design patterns for multi-agent simulations

The advent of mobile agent technology has brought along a few difficulties in designing a stable, efficient and scalable system for a certain problem. Agent-based simulations prove to be powerful tools for economic analyses. In this paper we aim at describing a set of design patterns which were specifically built for agents and multi-agent systems. The details of each design pattern discussed are presented and the possible applications and known issues are noted. In order to aid the software designers, we provide some examples of the basic implementation of these patterns using the JADE multi-agent framework.intelligent agent, multi-agent design, multi-agent simulation.

STRATEGY MANAGEMENT IN A MULTI-AGENT SYSTEM USING NEURAL NETWORKS FOR INDUCTIVE AND EXPERIENCE-BASED LEARNING

Intelligent agents and multi-agent systems prove to be a promising paradigm for solving problems in a distributed, cooperative way. Neural networks are a classical solution for ensuring the learning ability of agents. In this paper, we analyse a multi-agent system where agents use different training algorithms and different topologies for their neural networks, which they use to solve classification and regression problems provided by a user. Out of the three training algorithms under investigation, Backpropagation, Quickprop and Rprop, the first demonstrates inferior performance to the other two when considered in isolation. However, by optimizing the strategy of accepting or rejecting tasks, Backpropagation agents succeed in outperforming the other types of agents in terms of the total utility gained. This strategy is learned also with a neural network, by processing the results of past experiences. Therefore, we show a way in which agents can use neural network models for both external purposes and internal ones.agents, learning, neural networks, strategy management multi-agent system.

Reputation Model with Forgiveness Factor for Semi-Competitive E-Business Agent Societies

In this paper we introduce a new reputation model for agents engaged in e-business transactions. Our model enhances classic reputation models by adding forgiveness factor and new sources of reputation information based on agents groups. The model was implemented using JADE multi-agent platform and initially evaluated for e-business scenarios comprising societies of buyer and seller agents.