Relational Symbol Grounding through Affordance Learning: An Overview of the ReGround Project | Zendy

Laura Antanas | Zendy; Jesse Davis | Zendy; Luc De Raedt | Zendy; Amy Loutfi | Zendy; Andreas Persson | Zendy; Alessandro Saffiotti | Zendy; Deniz Yüret | Zendy; Ozan Arkan Can | Zendy; Emre Ünal | Zendy; Pedro Zuidberg Dos Martires | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Relational Symbol Grounding through Affordance Learning: An Overview of the ReGround Project

Author(s) -

Laura Antanas,

Jesse Davis,

Luc De Raedt,

Amy Loutfi,

Andreas Persson,

Alessandro Saffiotti,

Deniz Yüret,

Ozan Arkan Can,

Emre Ünal,

Pedro Zuidberg Dos Martires

Publication year - 2017

Publication title -

lirias (ku leuven)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.21437/glu.2017-3

Subject(s) - affordance , computer science , referent , context (archaeology) , ground , modal , symbol (formal) , process (computing) , human–computer interaction , artificial intelligence , engineering , programming language , linguistics , electrical engineering , paleontology , philosophy , chemistry , polymer chemistry , biology

Symbol grounding is the problem of associating symbols from language with a corresponding referent in the environment. Traditionally, research has focused on identifying single objects and their properties. The ReGround project hypothesizes that the grounding process must consider the full context of the environment, including multiple objects, their properties, and relationships among these objects. ReGround targets the development of a novel framework for “affordance grounding”, by which an agent placed in a new environment can adapt to its new setting and interpret possibly multi-modal input in order to correctly carry out the requested tasks.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research