An Auction-Based Semantic Service Discovery Model for E-Commerce Applications

摘要

Mediation between buyers (service requester's agents) and sellers (service provider's agents) is one of the most difficult problems in real electronic markets. In this paper, we propose an economic approach to solving this problem combined with AI (Artificial Intelligence) concepts. Firstly, we enable provider agents to dynamically and autonomously advertise semantic descriptions of available services by proposing a new auction model based on Pay-Per-Click advertising auctions. We call it the Semantic Pay-Per-Click Agent (SPPCA) auction. Requester agents then use two-level filtration of the advertised services to efficiently discover eligible services. In the first level of filtration, a semantic-based mechanism for matchmaking between services requested by buyers and those advertised by sellers is applied. Services which pass the first level of filtration are then considered on the second level. Here information regarding the actual performance of service providers is considered in conjunction with the prices bid by service provider's agents in the SPPCA auction. A final set of advertised services is then chosen and proposed to the buyer agent as an answer to its request.