Abstract. In the aftermath of a large-scale disaster, agents’ decisions derive from self-interested (e.g. survival), common-good (e.g. victims’ rescue) and teamwork (e.g. fire...
Abstract--Model checkers for concurrent probabilistic systems have become very popular within the last decade. The study of long-run average behavior has however received only scan...
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...
Relational Markov Decision Processes (MDP) are a useraction for stochastic planning problems since one can develop abstract solutions for them that are independent of domain size ...
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...