ISEM: a multiagent Simulator for testing agent market strategies

Abstract

With the increasing importance of e-commerce across the Internet, the need for software agents to support customers and suppliers in buying and selling goods/services is growing rapidly. It is becoming increasingly evident that in a few years the Internet will host a large number of interacting software agents. Most of them will be economically motivated, and will negotiate a variety of goods and services. It is therefore important to consider the economic incentives and behaviors of economic software agents, and to use all available means to anticipate their collective interactions. This paper addresses this concern by presenting a multiagent market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, and considering risk preferences. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions. The results of the negotiations between agents are analyzed by data mining algorithms to extract rules that give agents feedback to improve their strategies.Index Terms-Agent-based simulation, agent risk preferences, data mining, dynamic agent strategic behavior, electronic markets.

Discovering Strategic Behaviors in Multi-Agent Scenarios by Ontology-Driven Mining

“…This is the case of several agent based models with application to information retrieval, computational economics and robotics. In (Viamonte et al, 2006), for instance, is presented a Multi-Agent Market Simulator where the agents behavior follows the customers and seller profiles extracted by mining information from the market segment under analysis. (Bezek et al, 2006) presents an algorithm for extracting strategies from a RoboCup soccer scenario comprising several interacting agents with different roles and behaviors.…”

“…On the one hand, it allows users a deeper insight into the multi-agent system, permitting to discover interesting patterns of actions that can be identified as interesting behaviors in the particular problem at hand. Consider, for instance, a multi-agent system simulating negotiation/selling phases in a particular market (Viamonte et al, 2006): the detection of interesting patterns of actions can bring to light specific market strategies that can be exploited to optimize revenues. On the other hand, the identified strategic knowledge can be exploited by the agents themselves to improve their performance, for instance by updating their internal representation of the world and their behavioral policies.…”

Research on Supply Chain Management Using Multi-Agent System Based on Ant Colony Algorithm

“…However, for the MAS directly used to solve TSP problems or scheduling problems, the algorithm's time and space complexity is high [3,4], the effect is not satisfactory. For the supply-chain management issue is a combinatorial optimization problem more complex than the TSP, ordinary MAS technology becomes even more difficult to deal with them [6].…”

Learning Business Strategies for Competitive Electronic Marketplaces

“…We intend to apply this platform to different market types, taking into account some previous work of our research group, where two different simulation platforms have already been developed, namely ISEM -Intelligent System for Electronic MarketPlaces [5], and MASCEM -Multi-Agent Simulator for Competitive Electricity Markets [6].…”