Abstract. This paper develops a highly expressive semantic framework for program refinement that supports both temporal reasoning and reasoning about the knowledge of a single agen...
The Quantified Constraint Satisfaction Problem (QCSP) has been introduced to express situations in which we are not able to control the value of some of the variables (the universa...
In this paper, we investigate motor primitive learning with the Natural Actor-Critic approach. The Natural Actor-Critic consists out of actor updates which are achieved using natur...
Using multilayer perceptrons (MLPs) to approximate the state-action value function in reinforcement learning (RL) algorithms could become a nightmare due to the constant possibilit...
This paper defends the use of evolutionary algorithms to generate (and evolve) strategies that manage the behavior of a team in simulated football videogames. The chosen framework...