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Collaborative expert portfolio management

Stern, David and Herbrich, Ralf and Graepel, Thore and Samulowitz, Horst and Pulina, Luca and Tacchella, Armando (2010) Collaborative expert portfolio management. In: AAAI 2010: 24th AAAI Conference on Artificial Intelligence, 11-15 July 2010, Atlanta (GA), USA., AAAI Press. p. 179-184. ISBN 978-1-57735-463-5. Conference or Workshop Item.

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We consider the task of assigning experts from a portfolio of specialists in order to solve a set of tasks. We apply a Bayesian model which combines collaborative filtering with a feature-based description of tasks and experts to yield a general framework for managing a portfolio of experts. The model learns an embedding of tasks and problems into a latent space in which affinity is measured by the inner product. The model can be trained incrementally and can track non-stationary data, tracking potentially changing expert and task characteristics. The approach allows us to use a principled decision theoretic framework for expert selection, allowing the user to choose a utility function that best suits their objectives. The model component for taking into account the performance feedback data is pluggable, allowing flexibility. We apply the model to manage a portfolio of algorithms to solve hard combinatorial problems. This is a well studied area and we demonstrate a large improvement on the state of the art in one domain (constraint solving) and in a second domain (combinatorial auctions) created a portfolio that performed significantly better than any single algorithm.

Item Type:Conference or Workshop Item (Contribute)
ID Code:7215
Uncontrolled Keywords:Algorithm portfolio, Bayesian model
Subjects:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Divisions:001 Università di Sassari > 01 Dipartimenti > Economia, istituzioni e società
Publisher:AAAI Press
Deposited On:14 Mar 2012 10:15

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