Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Decision making based on the comparison of multiple criteria of two or more alternatives, is the subject of intensive research. In many decision making situations, a single criter...
Alfons Schuster, Werner Dubitzky, Philippe Lopes, ...
Abstract--Incremental-redundancy hybrid automatic repeatrequest (IR-HARQ) schemes are proposed in several wireless standards for increased throughput-efficiency and greater reliabi...
Ashok K. Karmokar, Dejan V. Djonin, Vijay K. Bharg...
Motivated by the interest in relational reinforcement learning, we introduce a novel relational Bellman update operator called ReBel. It employs a constraint logic programming lan...
Kristian Kersting, Martijn Van Otterlo, Luc De Rae...
Probabilistic models are useful for analyzing systems which operate under the presence of uncertainty. In this paper, we present a technique for verifying safety and liveness prop...