Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
We study finding similar or diverse solutions of a given computational problem, in answer set programming, and introduce offline methods and online methods to compute them using an...
Thomas Eiter, Esra Erdem, Halit Erdogan, Michael F...
Abstract. Most recent papers addressing the algorithmic problem of allocating advertisement space for keywords in sponsored search auctions assume that pricing is done via a first...
Yossi Azar, Benjamin E. Birnbaum, Anna R. Karlin, ...
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
In the era of Ubiquitous Computing and world–wide data transfer mobility, as an innovative aspect of professional activities, imposes new and complex problems of mobile and dist...