Philipp W. Keller

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The Supply Chain Management track of the international Trading Agents Competition (TAC SCM) was introduced in 2003 as a test-bed for researchers interested in building autonomous agents that act in dynamic supply chains. TAC SCM provides a challenging scenario for existing AI decision-making algorithms, due to the high dimensionality and the non-determinism(More)
We address the problem of automatically constructing basis functions for linear approximation of the value function of a Markov Decision Process (MDP). Our work builds on results by Bertsekas and Castañon (1989) who proposed a method for automatically aggregating states to speed up value iteration. We propose to use neighborhood component analysis(More)
The Supply Chain Management track of the international trading Agents Competition (TAC ACM) provides a challenging scenario for existing AI decision-making algorithms, due to the high dimensionality and the non-determinism of the environment. Our entry in this competition, RedAgent, is centered around the idea of using an internal market in order to(More)
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical applications (e.g. speech recognition, biological sequence alignment) it has two major limitations: it requires a known model topology, and learning is only locally optimal. We(More)
We propose a modeling and optimization framework to cast a broad range of fundamental multi-product pricing problems as tractable convex optimization problems. We consider the basic setting of a retailer offering an assortment of differentiated substitutable products to a population of customers that are price-sensitive. The retailer selects prices to(More)
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