RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
— Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently...
Matthew Howard, Stefan Klanke, Michael Gienger, Ch...
A recurrent question in the design of intelligent agents is how to assign degrees of beliefs, or subjective probabilities, to various events in a relational environment. In the sta...
RoboCup is an increasingly successful attempt to promote the full integration of AI and robotics research. The most prominent feature of RoboCup is that it provides the researcher...
This paper presents the concepts of our MoRob (Modular Educational Robotic Toolbox) project, which aims to provide a robot platform for university teaching and research. Character...