4 citations · 6 across the 11 of their papers we have counts for
13 papers
Acceptable Misalignment
Maud van Lier, Ludovica Marinucci, Vieri Giuliano Santucci +4
There is a specific challenge designers face in developing autonomous robots capable of open-ended learning. This challenge is how one can develop such systems in an ethically soun…
Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agents
Marica Notte, Ludovica Marinucci, Vieri Giuliano Santucci
In recent years, artificial intelligence has made extraordinary progress thanks to large-scale models capable of generalization and the generation of complex outputs. However, tran…
A Motivational Architecture for Open-Ended Learning Challenges in Robots
Alejandro Romero, Gianluca Baldassarre, Richard J. Duro +1
Developing agents capable of autonomously interacting with complex and dynamic environments, where task structures may change over time and prior knowledge cannot be relied upon, i…
Autonomous state-space segmentation for Deep-RL sparse reward scenarios
Gianluca Maselli, Vieri Giuliano Santucci
Dealing with environments with sparse rewards has always been crucial for systems developed to operate in autonomous open-ended learning settings. Intrinsic Motivations could be an…
Synthesizing Evolving Symbolic Representations for Autonomous Systems
Gabriele Sartor, Angelo Oddi, Riccardo Rasconi +2
Recently, AI systems have made remarkable progress in various tasks. Deep Reinforcement Learning(DRL) is an effective tool for agents to learn policies in low-level state spaces to…
The Autonomy-Alignment Problem in Open-Ended Learning Robots: Formalising the Purpose Framework
Gianluca Baldassarre, Richard J. Duro, Emilio Cartoni +3
The rapid advancement of artificial intelligence is enabling the development of increasingly autonomous robots capable of operating beyond engineered factory settings and into the…
A Definition of Open-Ended Learning Problems for Goal-Conditioned Agents
Olivier Sigaud, Gianluca Baldassarre, Cedric Colas +5
A lot of recent machine learning research papers have ``open-ended learning'' in their title. But very few of them attempt to define what they mean when using the term. Even worse,…
Option Discovery for Autonomous Generation of Symbolic Knowledge
Gabriele Sartor, Davide Zollo, Marta Cialdea Mayer +3
In this work we present an empirical study where we demonstrate the possibility of developing an artificial agent that is capable to autonomously explore an experimental scenario.…
Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies
Alejandro Romero, Gianluca Baldassarre, Richard J. Duro +1
Autonomous open-ended learning is a relevant approach in machine learning and robotics, allowing the design of artificial agents able to acquire goals and motor skills without the…
Autonomous learning of multiple, context-dependent tasks
Vieri Giuliano Santucci, Davide Montella, Bruno Castro da Silva +1
When facing the problem of autonomously learning multiple tasks with reinforcement learning systems, researchers typically focus on solutions where just one parametrised policy per…
Learning High-Level Planning Symbols from Intrinsically Motivated Experience
Angelo Oddi, Riccardo Rasconi, Emilio Cartoni +3
In symbolic planning systems, the knowledge on the domain is commonly provided by an expert. Recently, an automatic abstraction procedure has been proposed in the literature to cre…
Autonomous Reinforcement Learning of Multiple Interrelated Tasks
Vieri Giuliano Santucci, Gianluca Baldassarre, Emilio Cartoni
Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be in…
Autonomous Open-Ended Learning of Interdependent Tasks
Vieri Giuliano Santucci, Emilio Cartoni, Bruno Castro da Silva +1
Autonomy is fundamental for artificial agents acting in complex real-world scenarios. The acquisition of many different skills is pivotal to foster versatile autonomous behaviour a…