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Designing Adaptive Trading Agents.
David Pardoe and Peter
Stone.
ACM SIGecom Exchanges, 10(2):37–9, June 2011.
SIGecom
Exchanges
This extended abstract summarizes the research presented in Dr. Pardoe's recently-completed Ph.D. thesis. The thesis considers how adaptive trading agents can take advantage of previous experience (real or simulated) in other markets while remaining robust in the face of novel situations in a new market. Its contributions are at the intersection of machine learning and electronic commerce, with particular focus on transfer learning and fully autonomous trading agents.
@article{SIGecom11,
author="David Pardoe and Peter Stone",
title="Designing Adaptive Trading Agents",
month="June",year="2011",
volume="10",number="2",pages="37--9",
journal="{ACM} {SIG}ecom Exchanges",
abstract={This extended abstract summarizes the research presented in Dr. Pardoe's recently-completed Ph.D. thesis. The thesis considers how adaptive trading agents can take advantage of previous experience (real or simulated) in other markets while remaining robust in the face of novel situations in a new market. Its contributions are at the intersection of machine learning and electronic commerce, with particular focus on transfer learning and fully autonomous trading agents.},
wwwnote={<a href="http://www.acm.org/sigecom/exchanges/">SIGecom Exchanges</a>},
}
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