In this work we present an empirical study where we demonstrate the possibility of developing an arti- ficial agent that is capable to autonomously explore an experimental scenario. During the exploration, the agent is able to discover and learn interesting options allowing to interact with the environment without any assigned task, and then abstract and re-use the acquired knowledge to solve the assigned tasks. We test the system in the so-called Treasure Game domain described in the recent literature and we empirically demonstrate that the discovered options can be abstracted in an probabilistic symbolic planning model (using the PPDDL language), which allowed the agent to generate symbolic plans to achieve extrinsic goals.

Sartor, G., Zollo, D., Cialdea, M., Oddi, A., Giuliano Santucci, V., Rasconi, R. (2021). Autonomous Generation of Symbolic Knowledge via Option Discovery. In Proceedings of the 9th Italian Workshop on Planning and Scheduling (IPS-2021). CEUR Workshop Proceedings series.

Autonomous Generation of Symbolic Knowledge via Option Discovery

Marta Cialdea;
2021-01-01

Abstract

In this work we present an empirical study where we demonstrate the possibility of developing an arti- ficial agent that is capable to autonomously explore an experimental scenario. During the exploration, the agent is able to discover and learn interesting options allowing to interact with the environment without any assigned task, and then abstract and re-use the acquired knowledge to solve the assigned tasks. We test the system in the so-called Treasure Game domain described in the recent literature and we empirically demonstrate that the discovered options can be abstracted in an probabilistic symbolic planning model (using the PPDDL language), which allowed the agent to generate symbolic plans to achieve extrinsic goals.
Sartor, G., Zollo, D., Cialdea, M., Oddi, A., Giuliano Santucci, V., Rasconi, R. (2021). Autonomous Generation of Symbolic Knowledge via Option Discovery. In Proceedings of the 9th Italian Workshop on Planning and Scheduling (IPS-2021). CEUR Workshop Proceedings series.
File in questo prodotto:
File Dimensione Formato  
2021- IPS -paper2_193.pdf

accesso aperto

Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 4.12 MB
Formato Adobe PDF
4.12 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/395039
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact