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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…
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.…
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…