Neural-symbolic computing
AssigneeSRI International
InventorsJohn Byrnes, Richard Rohwer, Andrew Silberfarb
Life of this patent
Estimating the term…
Abstract
A neural-symbolic computing engine can have two or more modules that are configured to cooperate with each other in order to create one or more gradient-based machine learning models that use machine learning on i) knowledge representations and ii) reasoning to solve an issue. A model representation module in the neural-symbolic computing engine is configured to apply one or more mathematical functions, at least including a logit transform, to truth values from first order logic elements supplied from a language module of the neural-symbolic computing engine.
Claims20
An apparatus, comprising:
- a neural-symbolic computing engine that has two or more modules that are configured to cooperate with each other in order to create one or more gradient-based machine learning models that use machine learning on i) knowledge representations and ii) reasoning, to solve an issue, where a model representation module in the neural-symbolic computing engine is configured to apply one or more mathematical functions, at least including a logit transform, to truth values from first order logic elements supplied from a language module of the neural-symbolic computing engine.
A non-transitory computer-readable medium including executable instructions that, when executed with one or more processors, cause a neural-symbolic computing system to perform operations as follows, comprising:
- creating one or more gradient-based machine learning models that use machine learning on i) knowledge representations and ii) reasoning to solve an issue;
- and
- applying one or more mathematical functions, at least including a logit transform, to truth values from first order logic elements.
A method for neural-symbolic computing, comprising:
- configuring a neural-symbolic computing engine to create one or more gradient-based machine learning models that use machine learning on i) knowledge representations and ii) reasoning to solve an issue, where the neural-symbolic computing engine applies one or more mathematical functions, at least including a logit transform, to truth values from first order logic elements.
Dependencies are inferred from the claim text.