In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use o...
Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csab...
Indicator-based algorithms have become a very popular approach to solve multi-objective optimization problems. In this paper, we contribute to the theoretical understanding of alg...
We present a new bit-parallel technique for approximate string matching. We build on two previous techniques. The first one, BPM [Myers, J. of the ACM, 1999], searches for a patte...
The schedulability analysis problem for many realistic task models is intractable. Therefore known algorithms either have exponential complexity or at best can be solved in pseudo...
We present an empirical evaluation of three contextsensitive slicing algorithms and five context-sensitive chopping algorithms, and compare them to context-insensitive methods. B...