Abstract
In this paper, we sketch a framework for integration between subsymbolic and symbolic representations,
consisting of a series of layers and mappings between elements across the layers. Each layer corresponds to
a particular level of abstraction about phenomena in the environment being observed in the layers below.
Through an iterative process, the differences between the elements in successive iterations within a given
layer are captured as transformations between the elements and used for identification and recognition of
objects as well as prediction and verification of the environment in future iterations. A bridge between the
subsymbolic and symbolic levels can be built by successively adding layers at ever more sophisticated levels
of abstraction. This approach aims to benefit from subsymbolic learning, while harnessing the abstraction
and reasoning powers of classical symbolic AI techniques.
consisting of a series of layers and mappings between elements across the layers. Each layer corresponds to
a particular level of abstraction about phenomena in the environment being observed in the layers below.
Through an iterative process, the differences between the elements in successive iterations within a given
layer are captured as transformations between the elements and used for identification and recognition of
objects as well as prediction and verification of the environment in future iterations. A bridge between the
subsymbolic and symbolic levels can be built by successively adding layers at ever more sophisticated levels
of abstraction. This approach aims to benefit from subsymbolic learning, while harnessing the abstraction
and reasoning powers of classical symbolic AI techniques.
| Original language | English |
|---|---|
| Title of host publication | CEUR Workshop Proceedings |
| Subtitle of host publication | NESY 2022: 16th International Workshop on Neural-Symbolic Learning and Reasoning |
| Publication status | Accepted/In press - 29 Jul 2022 |