The motive behind the report is to present the approach used to develop Post-Traumatic, an intelligent agent that competed in Trading Agent Competition (TAC)  at the University of Southampton.
The methods used are different according to the type of the auction the agent is competing in. For the hotel auction, we used 1st-order curve fitting to predict the highest price at the end of the auction window. Then we add a safety-margin to ensure the winning of the bid. For the flight auction, we used a varying n-order curve-fitting to predict the rise and decline of the prices over the remaining time. The order (n) varies according to the elapsed time of the auction. For the entertainment auction, we used an ever-increasing bid price and an ever-decreasing ask price. In spite of the simplicity of this strategy, we were able to secure some profit added to the total utility.
The key results are as the following. In predicting flight prices, the agent was able to make profit when comparing the buying-prices with the final closing prices. For the hotel auction, our strategy to compete fiercely on the hotel tickets succeeded in securing more than 82% of the allocations according to the client preferences. But the trade-off was more hurting than rewarding. The strategy led to significantly reducing the final utility, because of rapid increase in the prices at the end of the auction window.
This project was built as a coursework for the ‘Intelligent Agents’, 1st semester in MSc Artificial Intelligence at the University of Southampton.