Autonomous seller agent for multiple simultaneous English auctions

The growth of online auction is due to the flexibility and convenience that it offers to consumers. In the context of online auction, deriving the best reserve price can be associated to the seller's optimization problem. Determining this reserve price is not straightforward due to the dynamic and unpredictable nature of the auction environment. Setting the price too high will lead to the possibility of no sale outcome. Putting the price too low may produce a sale with less profit due to its lower selling price. We propose a strategy to derive the best reserve price based on several selling constraints such as the number of competitors (sellers), the number of bidders, the auction duration, and the profit the seller desired when offering an item to be auctioned. However, to obtain the best performance, the strategy must be tuned to the prevailing auction environment where the agent is situated. In this paper, we describe our seller agent's performance under varying auction environments. The purpose of the experimental evaluation is to assess the ability of the agent to identify its environments accurately to enable it to come up with the best reserve price