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05/18/2023
A method for the ethical analysis of brain-inspired AI
Despite its successes, to date Artificial Intelligence (AI) is still cha...
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05/16/2022
Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies
Autonomous open-ended learning is a relevant approach in machine learnin...
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06/07/2021
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning
A common view on the brain learning processes proposes that the three cl...
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11/27/2020
An open-ended learning architecture to face the REAL 2020 simulated robot competition
Open-ended learning is a core research field of machine learning and rob...
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11/27/2020
Autonomous learning of multiple, context-dependent tasks
When facing the problem of autonomously learning multiple tasks with rei...
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01/06/2020
Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints
Reinforcement learning can greatly benefit from the use of options as a ...
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12/31/2019
Representation Internal-Manipulation (RIM): A Neuro-Inspired Computational Theory of Consciousness
Many theories, based on neuroscientific and psychological empirical evid...
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12/31/2019
Intrinsic motivations and open-ended learning
There is a growing interest and literature on intrinsic motivations and ...
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07/18/2019
Learning High-Level Planning Symbols from Intrinsically Motivated Experience
In symbolic planning systems, the knowledge on the domain is commonly pr...
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06/04/2019
Autonomous Reinforcement Learning of Multiple Interrelated Tasks
Autonomous multiple tasks learning is a fundamental capability to develo...
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05/07/2019
Autonomous Open-Ended Learning of Interdependent Tasks
Autonomy is fundamental for artificial agents acting in complex real-wor...
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05/19/2018