One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (...
Lihong Li, Michael L. Littman, Christopher R. Mans...
We present GridSAT, a parallel and complete satisfiability solver designed to solve non-trivial SAT problem instances using a large number of widely distributed and heterogeneous...
Distributing scarce resources among agents in a way that maximizes the social welfare of the group is a computationally hard problem when the value of a resource bundle is not lin...
The purpose of this work is to propose an immune-inspired setup to use a self-organizing map as a computational model for the interaction of antigens and antibodies. The proposed ...
Quantitative modeling plays a key role in the natural sciences, and systems that address the task of inductive process modeling can assist researchers in explaining their data. In...