Search Results for "reasoning models"

...Data in the fields of text, speech, and images is easier to process into a grid-like type of Euclidean space, which is suitable for processing by existing deep learning models. Graph is a data type in non-Euclidean space and cannot be directly applied to existing methods, requiring a specially designed graph neural network system. Graph-based learning methods such as graph neural networks combine end-to-end learning with inductive reasoning, and are expected to solve a series of problems such as relational reasoning and interpretability that deep learning cannot handle.

TinyCog is a cognitive architecture (perception, action, reasoning, planning, and language) with a focus on representing "self-models" (physical, capabilities, intentions, social, historical, "cognitive stream") in order to implement social reasoning and collaborative planning in a "hunter domain" test bed. We use Prolog, RDF Triples, Description Logic and a HTN-Planner as base technologies.