Meeting Minutes - 25 March 2009

In the presentation, Romke and Otto presented the outcomes from the master seminar in computational economics. The research was focused on improving the price prediction mechanism of the MinneTAC agent. The MinneTAC agent is an agent-based computer model that is developed by the university of Minnesota in cooperation with the Erasmus University.

The MinneTAC agent competes with other Agent based computer models in the Trading Agent Competition for Supply Chain Management (TAC SCM). The TAC SCM game was designed to come to the best solution for an Agent based computer model that is capable of dealing with the problems of a dynamic supply chain.

In the MinneTAC agent, there is an ensemble, consisting of multiple price predictors used to predict the future market prices. The function of the model selection mechanism is to determine the most accurate price based on the predictions from all the individual predictors making up the ensemble. The advantage of using multiple predictors is the ability to capture more features in the data then a single predictor. The disadvantage of using multiple predictors is that different features are captured that causes different predictions. A second disadvantage is that not every price predictor is performing optimal for every time horizon and quantity of training data. To overcome these disadvantages, there is a dynamic weighting mechanism with adaptive weights developed for the MinneTAC agent. This weighting mechanism has to find to the optimal weights for every price predictor for every time horizon. The weights are learned during the game, while the agent is competing with its competitors for customer orders. When the agent starts, every price predictor has an equal weight. During the game, the MinneTAC agent starts using the optimal weights. This means that the price prediction mechanism is not working with the optimal weights during the first phase of the game.

In our seminar research, we found the optimal weights for every price predictor during the game. This data is used to bootstrap the agent to increase the performance in the first phase of the game.

R.J. Romke de Vries
O.B. ter Haar

Meeting Minutes - 18 February 2009

Today I discussed the main results of my master's thesis, in which I try to incorporate procurement information into an economic regime model based on sales information. This model is used in the MinneTAC agent, which is an artifical trading agent that competes in the TAC SCM game.

First, I gave a brief description of the TAC SCM game, after which I introduced the regime model as it is currently used in the MinneTAC agent. Then, I introduced a new procurement variable, i.e., offer prices, after which I elaborated on both regime identification and regime prediction. Finally, I discussed some experimental results.

During the last part of the presentation where I introduced some experimental results, we had a discussion on the causes of these results. As it seems, implementing procurement information into the regime model does not lead to better performance of the MinneTAC agent in TAC SCM games. In fact, the agent gets more orders from customers, but generates lower profits. Other competitors seem to take advantage of the situation, since their profits increase when MinneTAC uses the new regime model.

A suggested cause is the fact that there could be a delay regarding the procurement information. Procurement information might be a leading indicator for regimes, so perhaps creating a regime model based on the sales price of yesterday and the procurement offer price of for instance three days earlier could improve the performance of the agent. However, this is dependent on the cost allocation of the agent. Furthermore, there is a lack of adaptivity in the regime model and therefore, the model cannot adjust properly during a game. Finally, regime information is not used for price setting in the experiments discussed today. Establishing this connection between regimes and price setting could improve the overall results as well.

Frederik

Meeting Minutes - 12 November 2008

Today Uzay Kaymak gave a presentation on the Ming Hua and Nicholas Jenning's paper of "Designing a Successful Trading Agent: A Fuzzy Set Aproach".

The paper tells us about the writers experience in implementing fuzzy logics in their SouthamptonTAC agent.

SouthhamptonTAC agent participated successfully in the first and second (TAC) Trading Agent Competition, a competition which facilitates the competition of the participant's "travel" agents in fulfilling their customer demand of travel package (flight ticket, hotel, and extra entertainment ticket). Those participating agents should compete with each other in different (flight ticket, hotel, and extra entertainment ticket) auctions provided by the TAC platform to statisfy the demand of the agent's costumer.

Uzay briefly explains how the SouthamptonTAC implements the fuzzy logics in the hotel, ticket, and entertaiment bidding actions. He (Uzay) found it quiet surprising how the performance of the fuzzy rules implementation can work well on the competition.

The presentation, is wrapped up by a small discussion about the competition conditions. Wolf gives the audience some description about the condition of the TAC competition (The rounds, the finalists, etc.).