Machine-based learning system
AssigneeAT&T Bell Laboratories
InventorsAllen L. Gorin, Stephen E. Levinson
Life of this patent
Estimating the term…
Abstract
A machine-based learning system receives inputs and proposes outputs using a network of connection weights to relate the inputs to the outputs. When a user affirms a proposed output, the system adjusts the connection weights to strengthen the relationship between elements of the inputs and that output. Learning occurs in the course of successive iterations as the connection weights adapt to relationships between semantically-significant input elements and related outputs. The system can be used to acquire language from inputs provided in the form of text or speech such that the connection weights adapt to semantically-significant words without need for defining word meanings. Such language acquisition contrasts with previous systems in which words are predefined.
Claims19
The method of conditioning a machine for selecting one of a plurality of outputs in response to applied inputs, each input containing one or more elements, comprising the steps of:
- establishing a recognition node for each distinct element in said inputs, said recognition node having activation states indicating the presence or absence of said element in an input;
- defining weighting relationships between each said recognition node and each said output;
- determining, in response to each input, a total activation for each said output from said weighting relationships and the activation states of said recognition nodes;
- selecting, in response to said determining step, the output having the most favorable total activation;
- proposing the output selected in said selecting step;
- adjusting said weighting relationships in response to an input received subsequent to said proposing step in accordance with whether or not said subsequent input indicates that said proposed output is correct;
- whereby, as successive inputs are received, said weighting relationships become adapted to represent associations between semantically significant ones of said elements and said outputs.
A machine-based system for selecting one of a plurality of outputs in response to inputs, each said input containing one or more elements, and for learning associations between said elements and said actions, which comprises:
- means for receiving said inputs;
- means responsive to said inputs for establishing a recognition node for each distinct element in said inputs, said recognition node having activation states indicating the presence or absence of said element in an input;
- means for defining weighting relationships between each said recognition node and each said output;
- means for determining, in response to receiving each input, a total activation for each said output from said weighting relationships and the activation states of said recognition nodes
- means responsive to said determining means for selecting the output having the most favorable total activation;
- means responsive to said selecting means for proposing the selected action;
- means for adjusting said weighting relationships in response to an input received subsequent to proposing said output in accordance with whether or not said subsequent input indicates that said proposed output is correct;
- whereby, as successive inputs are received, said weighting relationships become adapted to represent associations between semantically significant ones of said elements and said outputs.
Dependencies are inferred from the claim text.