Although inference and learning arise traditionally from different schools of thought, in the last few years they have been framed in nice unified frameworks, in the attempt to resemble clever human decision mechanisms. In this paper, however, we support the position that a true understanding of human-based inference and learning mechanisms might arise more naturally when replacing the focus on logic and probabilistic reasoning with that of cognitive laws, in the spirit of most variational laws of Nature. To this end, we propose a strong analogy between learning from constraints and analytic mechanics, which suggests us that agents living in their own environment obey laws exactly like those of particles subjected to a force field.

Frandina, S., Gori, M., Lippi, M., Maggini, M., Melacci, S. (2013). Inference, Learning, and Laws of Nature. In Proceedings of the 9th International Workshop on Neural-Symbolic Learning and Reasoning NeSy13 (pp.20-23).

Inference, Learning, and Laws of Nature

FRANDINA, SALVATORE;GORI, MARCO;LIPPI, MARCO;MAGGINI, MARCO;MELACCI, STEFANO
2013-01-01

Abstract

Although inference and learning arise traditionally from different schools of thought, in the last few years they have been framed in nice unified frameworks, in the attempt to resemble clever human decision mechanisms. In this paper, however, we support the position that a true understanding of human-based inference and learning mechanisms might arise more naturally when replacing the focus on logic and probabilistic reasoning with that of cognitive laws, in the spirit of most variational laws of Nature. To this end, we propose a strong analogy between learning from constraints and analytic mechanics, which suggests us that agents living in their own environment obey laws exactly like those of particles subjected to a force field.
2013
Frandina, S., Gori, M., Lippi, M., Maggini, M., Melacci, S. (2013). Inference, Learning, and Laws of Nature. In Proceedings of the 9th International Workshop on Neural-Symbolic Learning and Reasoning NeSy13 (pp.20-23).
File in questo prodotto:
File Dimensione Formato  
2013 - Nesy.pdf

non disponibili

Tipologia: Post-print
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 686.9 kB
Formato Adobe PDF
686.9 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

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/11365/47102
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo