11 citations · 16 across the 11 of their papers we have counts for
12 papers
A method for the ethical analysis of brain-inspired AI
Michele Farisco, Gianluca Baldassarre, Emilio Cartoni +6
Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limita…
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…
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning
Giovanni Granato, Emilio Cartoni, Federico Da Rold +2
A common view on the brain learning processes proposes that the three classic learning paradigms -- unsupervised, reinforcement, and supervised -- take place in respectively the co…
Learning Abstract Representations through Lossy Compression of Multi-Modal Signals
Charles Wilmot, Gianluca Baldassarre, Jochen Triesch
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific det…
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…
Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints
Manuel Del Verme, Bruno Castro da Silva, Gianluca Baldassarre
Reinforcement learning can greatly benefit from the use of options as a way of encoding recurring behaviours and to foster exploration. An important open problem is how can an agen…
Intrinsic motivations and open-ended learning
Gianluca Baldassarre
There is a growing interest and literature on intrinsic motivations and open-ended learning in both cognitive robotics and machine learning on one side, and in psychology and neuro…
A Neurocomputational Account of Flexible Goal-directed Cognition and Consciousness: The Goal-Aligning Representation Internal Manipulation Theory (GARIM)
Giovanni Granato, Gianluca Baldassarre
Goal-directed manipulation of representations is a key element of human flexible behaviour, while consciousness is often related to several aspects of higher-order cognition and hu…
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…
Autonomous discovery of the goal space to learn a parameterized skill
Emilio Cartoni, Gianluca Baldassarre
A parameterized skill is a mapping from multiple goals/task parameters to the policy parameters to accomplish them. Existing works in the literature show how a parameterized skill…