Adaptive trading agent strategies using market experience
| dc.contributor.advisor | Stone, Peter, 1971- | en |
| dc.contributor.committeeMember | Miikkulainen, Risto | en |
| dc.contributor.committeeMember | Mooney, Raymond | en |
| dc.contributor.committeeMember | Saar-Tsechansky, Maytal | en |
| dc.contributor.committeeMember | Wellman, Michael | en |
| dc.creator | Pardoe, David Merrill | en |
| dc.date.accessioned | 2011-06-22T15:20:23Z | en |
| dc.date.available | 2011-06-22T15:20:23Z | en |
| dc.date.available | 2011-06-22T15:20:37Z | en |
| dc.date.issued | 2011-05 | en |
| dc.date.submitted | May 2011 | en |
| dc.date.updated | 2011-06-22T15:20:37Z | en |
| dc.description | text | en |
| dc.description.abstract | Along with the growth of electronic commerce has come an interest in developing autonomous trading agents. Often, such agents must interact directly with other market participants, and so the behavior of these participants must be taken into account when designing agent strategies. One common approach is to build a model of the market, but this approach requires the use of historical market data, which may not always be available. This dissertation addresses such a case: that of an agent entering a new market in which it has no previous experience. While the agent could adapt by learning about the behavior of other market participants, it would need to do so in an online fashion. The agent would not necessarily have to learn from scratch, however. If the agent had previous experience in similar markets, it could use this experience to tailor its learning approach to its particular situation. This dissertation explores methods that a trading agent could use to take advantage of previous market experience when adapting to a new market. Two distinct learning settings are considered. In the first, an agent acting as an auctioneer must adapt the parameters of an auction mechanism in response to bidder behavior, and a reinforcement learning approach is used. The second setting concerns agents that must adapt to the behavior of competitors in two scenarios from the Trading Agent Competition: supply chain management and ad auctions. Here, the agents use supervised learning to model the market. In both settings, methods of adaptation can be divided into four general categories: i) identifying the most similar previously encountered market, ii) learning from the current market only, iii) learning from the current market but using previous experience to tune the learning algorithm, and iv) learning from both the current and previous markets. The first contribution of this dissertation is the introduction and experimental validation of a number of novel algorithms for market adaptation fitting these categories. The second contribution is an exploration of the degree to which the quantity and nature of market experience impact the relative performance of methods from these categories. | en |
| dc.description.department | Computer Sciences | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/2152/ETD-UT-2011-05-2899 | en |
| dc.language.iso | eng | en |
| dc.subject | Trading agents | en |
| dc.subject | Multi-agent systems | en |
| dc.subject | Machine learning | en |
| dc.subject | Transfer learning | en |
| dc.subject | Supply chain management | en |
| dc.subject | Ad auctions | en |
| dc.subject | Trading Agent Competition | en |
| dc.title | Adaptive trading agent strategies using market experience | en |
| dc.type.genre | thesis | en |
| thesis.degree.department | Computer Sciences | en |
| thesis.degree.discipline | Computer Science | en |
| thesis.degree.grantor | University of Texas at Austin | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |