Many learning algorithms form concept descriptions composed of clauses, each of which covers some proportion of the positive training data and a small to zero proportion of the ne...
The notion of fixed-parameter approximation is introduced to investigate the approximability of optimization problems within the framework of fixed-parameter computation. This work...
By learning a range of possible times over which the effect of an action can take place, a robot can reason more effectively about causal and contingent relationships in the world...
We introduce the probabilistic class SBP which is defined in a BPP-like manner. This class emerges from BPP by keeping the promise of a probability gap but decreasing the probabil...
In this paper we address the problem of how decision-theoretic policies can be repaired. This work is motivated by observations made in robotic soccer where decisiontheoretic polic...
Christoph Mies, Alexander Ferrein, Gerhard Lakemey...