Prediction methods that anticipate the outcome of a detailed optimization step are used in lieu of or in conjunction with actual optimization to improve response time and reduce required computational resources for optimization problems having a hierarchical structure. Decomposition of the optimization problem into sub-problems and sub-sub-problems is, itself, an optimization process which is iteratively performed while preferably guided by prediction of the quality of solutions to the problems into which the "master" optimization problem may be decomposed. Prediction also reduces the requirements for computational resources and allows more decompositions to be examined within the available time in order to arrive at a more nearly optimal decomposition as well as a more nearly optimal solution. Prediction is selectively used when it is determined that such a benefit is probable.

An autonomous adaptive agent which can learn verbal as well as nonverbal behavior. The primary object of the system is to optimize a primary value function over time through continuously learning how to behave in an environment (which may be physical or electronic). Inputs may include verbal advice or information from sources of varying reliability as well as direct or preprocessed environmental inputs. Desired agent behavior may include motor actions and verbal behavior which may constitute a system output (and which may also function "internally" to guide external actions. A further aspect involves an efficient "training" process by which the agent can be taught to utilize verbal advice and information along with environmental inputs.

A current-type fuzzy processor is disclosed. The fuzzy processor comprises a characteristic decoder for decoding a to-be-identified pattern, a plurality of membership function generators for generating a plurality of membership degrees for the to-be-identified pattern, a weighting decoder for generating a weighting value, a plurality of weighting adjustment circuits for performing weighting operations on the plurality of membership degrees according to the weighting value to generate a plurality of weighted membership degrees, a plurality of accumulators for performing accumulation operations on the plurality of weighted membership degrees to generate a plurality of synthesis membership degrees, a WTA circuit for outputting the plurality of synthesis membership degrees in an order of magnitude; and a timing and control logic for generating timing and control signals for the fuzzy processor.

A method for action selection based upon an objective of an outcome relative to a subject. In one embodiment, a training set is obtained that contains attributes of a subject. In the present embodiment, a best behavioral model for predicting an outcome when a subject has an action applied is calculated. The training set is mapped to the best behavioral model. The mapping provides a base from which a random sub-sample is acquired. In the present embodiment, a random sub-sample of the training set and the best behavioral model is then selected. This random sub-sample reduces the computational requirements when determining an optimized strategy. The optimized strategy provides an optimal action relative to the subject for the objective of the outcome. In one embodiment, the subject is a customer of a business entity, enabled to interact with the customer, and an action is a promotion offered by the business entity.

A system for retrieving multimedia information is provided using a computer coupled to a computer-based network, such as the Internet, and particularly the World Wide Web (WWW). The system includes a web browser, a graphic user interface enabled through the web browser to allow a user to input a query representing the information the user wishes to retrieve, and an agent server for producing, training, and evolving first agents and second agents. Each of the first agents retrieves documents (Web page) from the network at a different first network address and at other addresses linked from the document at the first network address. Each of the second agents executes a search on different search engines on the network in accordance with the query to retrieve documents at network addresses provided by the search engine. The system includes a natural language processor which determines the subject categories and important terms of the query, and of the text of each agent retrieved document. The agent server generates and trains an artificial neural network in accordance with the natural language processed query, and embeds the trained artificial neural network in each of the first and second agents. During the search, the first and second agents process through their artificial neural network the subject categories and important terms of each document they retrieve to determine a retrieval value for the document. The graphic user interface displays to the user the addresses of the retrieved documents which are above a threshold retrieval value. The user manually, or the agent server automatically, selects which of the retrieved documents are relevant. Periodically, the artificial neural network of the first and second agents is expanded and retrained by the agent server in accordance with the selected relevant documents to improve their ability to retrieve documents which may be relevant to the query. Further, the agent server can evolve an artificial neural network based on the current artificial neural network, the retrieved documents, and their selected relevancy, by iteratively producing, training, and testing several generations of neural networks to produce an evolved agent. The artificial neural network of the evolved agent then replaces the current artificial neural network used by the agents to search the Internet. One or more concurrent search of the Internet may be provided.