Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized ...

In this paper we present the CARPooL demonstrator, an implementation of a Collective Adaptation Engine (CAE) that addresses the challenge of collective adaptation in the smart mobility domain. CARPooL resolves adaptation ...

In this paper we address the challenges that impede collective adaptation in smart mobility systems by proposing a notion of ensembles. Ensembles enable systems with collective adaptability to be built as emergent aggregations ...

Hierarchical Task Networks (HTNs) are a common model for encoding knowledge about planning domains in the form of task decompositions. We present a novel algorithm that uses invariant analysis to construct an HTN from the ...

Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. In large part, this success has been made possible by powerful function approximation methods ...

Lots of hopes have been placed on machine learning (ML) as a key enabler of future wireless networks. By taking advantage of large volumes of data, ML is expected to deal with the ever-increasing complexity of networking ...

Lifelong Learning in the context of Artificial Intelligence is a new paradigm that is still in its infancy. It refers to agents that are able to learn continuously, accumulating the knowledge learned in previous tasks and ...

This paper presents a novel approach for generating Context-Free Grammars (CFGs) from small sets of input strings (a single input string in some cases). Our approach is to compile this task into a classical planning problem ...

Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari games. These methods scale independently of ...

In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induction of a subgoal automaton, an automaton ...