We propose an opponent modeling approach for no-limit Texas hold-em poker that starts from a (learned) prior, i.e., general expectations about opponent behavior and learns a relat...
Marc J. V. Ponsen, Jan Ramon, Tom Croonenborghs, K...
: We describe the participation of the University of Amsterdam's ILPS group in the relevance feedback track at TREC 2008. We introduce a new model which incorporates informati...
Edgar Meij, Wouter Weerkamp, Jiyin He, Maarten de ...
We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a s...
Abstract: A vision of tools to support software development organizations in the process of development, maintenance and evolution of systems is presented. The envisioned tools see...
Daniel C. M. May, Bent Bruun Kristensen, Palle Now...
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...