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 ...
Abstract. Deploying process-driven information systems is a time-consuming and error-prone task. Process mining attempts to improve this by automatically generating a process model...
In the past, environmental restrictions on size, weight, and power consumption have severely limited both the processing and storage capacity of embedded signal processing systems...
In this paper, we particularly focused our attention on how to enhance expressivity of ontologies when used as organized space values in a catalogue request process. Using the Wis...
We study a dynamically evolving random graph which adds vertices and edges using preferential attachment and is “attacked by an adversary”. At time t, we add a new vertex xt a...