System for retrieving multimedia information from the internet using multiple evolving intelligent agents

A method for retrieving information on a computer coupled to a computer-based network, such as the Internet, in accordance with a query representing the information a user wishes to retrieve, said method comprising the steps of:

• producing a plurality of first agents and second agents in which said first agents each retrieve documents at a different first network address and at other addresses linked from the document at the first network address, and said second agents each execute a search on a different search engines via the network in accordance with said query and retrieves documents at network addresses provided by the executed search;
• generating a trained artificial neural network common to each of said first and second agents in accordance with said query for determining for each of the retrieved documents by said agents a retrieval value representing the proximity of the content of the retrieved documents to said query;
• and
• displaying to the user the addresses of the retrieved documents above a threshold retrieval value.

A system for retrieving information from the Internet utilizing multiple intelligent agents comprising:

• a computer system having a graphical user interface to input a query, means for accessing the Internet, means for producing a plurality of agents in which each of said agents retrieves documents at a first address on the Internet and at other addresses linked to the document at the first address, and means for determining the subject and important terms of the text of the query and of each the documents retrieved;
• each of said agents having a common neural network for determining the relevancy of each of the document retrieved by the agent, said neural network having a plurality of inputs and an output in which said inputs are based upon the subject and important terms of the query and said output representing a relevance value of each of the documents applied to the neural network;
• said computer system having means for training the neural network in accordance with the query, means for selecting which of said retrieved documents are relevant, means for periodically updating the neural network with additional inputs based on the subject and important terms of the selected retrieved documents and training the updated neural network using said selected retrieved documents to provide a retrained neural network for each agent, and means for evolving and training a plurality of different neural networks in which each is based on subset of the inputs of the retrained neural network, and iteritively evolving and training a new set of different neural networks having a subset of the inputs of such evolved trained different neural networks which best classifies documents as relevant until one of the evolved neural networks is the best classifier of documents as relevant to provide said one neural network for each agent;
• and
• said graphic user interface displaying to a user the results of the documents retrieved by said first and second agents.

A method for neurogenically evolving a parent artificial neural network having a plurality of inputs each characteristic of a different feature using multiple sets of one or more of said training features in which each set has a classification of a plurality of known classes, said method comprising the steps of:

• generating a plurality of agents each having an artificial neural network with a different subset of said features of said parent artificial neural network;
• dividing said multiple sets into a training group and a test group;
• training the artificial neural network of each of said agents with said multiple sets of said training group;
• testing the artificial neural network of each of said agents using said multiple sets of said test group to determine the number of sets correctly classified;
• determining a fitness function for each of said agents the number of sets correctly classified by the total number of sets in said test group;
• rank the agents by their fitness function;
• generating a plurality of next generation agents each having an artificial neural network with a different subset of said features of said parent artificial neural network in which said artificial neural network of the next generation agents has a greater chance of including said features of the artificial neural network of said higher ranked agents of the prior generation;
• repeating said training step, testing step, determining step, identifying step and said step of generating a plurality of next generation agents in accordance with said next generation of agents until one of a maximum number of generation of agents have been produced, and two successive generations of agents each having identical features are produced, in which the agent having the highest fitness function represents an evolved artificial neural network.