Abstract: A method of operating a fuzzy inference system to simplify a mesh capable of producing a high-quality approximate mesh and retaining good characteristics and appearance so that a good visual effect emerges. The invention utilizes a fuzzy inference system to integrate mesh attributes and estimate the cost in removing certain data, which serve as a criteria for mesh simplification. Hence, the invention is suitable for progressive meshes, multiresolution modeling rendering and progressive transmission on a network.

Abstract: The invention consists of a learning system (108) based on the Dynamical System Architecture (DSA). Every implementation of the DSA is composed of a dynamic system (112) and a static system (116). The dynamic system (112) generates an intermediate output (114), which together with an external input (100) is fed into the static system (116), which produces a generated output (106). Every time the dynamic system (112) is reset it produces the same trajectory: an intermediate output (114) that does not cross over itself during the temporal span of each of the events generated by a reference system (102) whose behavior has to be duplicated. The static system (116) can be anything that behaves like a trainable universal function approximator. Training uses the intermediate output (114), the external input (100), the observed output (104) produced by the reference system (102) whose behavior is going to be mimicked, and a self-organizing procedure that only modifies the parameters of the static system (116).

Abstract: An integrated cellular network structure that is programmable to solve partial derivative differential equations in order to control a phenomenon of diffusion or a propagation of electric drive pulses for robot actuators. Such structure includes analog and digital portions interconnected with each other; the analog portion having a matrix array of analog cells arranged to receive data from an I/O interface, and the digital portion having first and second memory arrays for storing a desired configuration and the initial state of such analog matrix array, respectively.

Abstract: A system is disclosed that provides a goal based learning system utilizing a rule based expert training system to provide a cognitive educational experience. The system provides the user with a simulated environment that presents a business opportunity to understand and solve optimally. Mistakes are noted and remedial educational material presented dynamically to build the necessary skills that a user requires for success in the business endeavor. The system utilizes an artificial intelligence engine driving individualized and dynamic feedback with synchronized video and graphics used to simulate real-world environment and interactions. Multiple “correct” answers are integrated into the learning system to allow individualized learning experiences in which navigation through the system is at a pace controlled by the learner.

Abstract: A learning process for a neural network for open-loop or closed-loop control of an industrial process with time-variable parameters. The neural network is configured either as an open-loop or closed-loop-control network with which the process is controlled. The neural network is trained with the current process data so that it builds a model of the current process. The neural network can also be configured as a background network which is trained during operation with representative process data so that it builds an averaged model of the process over a longer period of time. After a certain learning time or upon the occurrence of an external event, the open-loop or closed-control network is replaced by the background network.

Abstract: An expert system adapted data network guidance engine. The invention allows an agent to interact with a customer and to provide selection and recommendation of data network products and/or services for the customer. The invention allows for the use of agents of varying skill levels, including relatively low skill level, without suffering deleterious performance. The data network guidance engine is operable to perform generation and selection of configurations that are generated using various heuristics. If desired, numerous iterations are performed within each of the heuristic operations. The data network guidance engine is operable to select recommended configurations from among a number of potential options. In addition, compatible configurations may also be identified. The data network guidance engine is one of the underlying engines within the expert system that allows the agent to provide real time interaction with a customer and to provide a real time recommended solution to that customer.

Abstract: In a facilitating agent apparatus, a message processing section and an element processing section replace knowledge transmitted from another agent apparatus by a superior concept, combine the knowledge with another knowledge, transform ontology, or the like, based on meta-knowledge accumulated in a meta-knowledge storing section, and transmit the resulting knowledge to still another agent apparatus. Furthermore, if transmitted knowledge is denied, the message processing section and the element processing section create a message for denying their own knowledge transmitted to another agent apparatus, based on the denied knowledge.

Abstract: A system and a method enabling a software agent to learn about its environment, and learn how to perform jobs and tasks. Novel information is assimilated and used in an adaptive manner by the genetically evolving rule set of the agent. The agent learning system communicates with the external environment in an artificial economy through on-line auctions. Internal information also passes through message auctions, that provide chaining of rule execution. The agent continually attempts to automatically improve its performance with respect to fitness for a job.

Abstract: An expert system and a method of providing automated advice are described. The system is regularly updated by advice (or diagnosis, recommendation etc.) given by practitioners in the relevant field. The combination of the underlying facts and the consequent (human) advice is used to update a ruleset that is then used to provide automated advice. An example of financial advice is given. A database contains the details for the customers of a financial institution together with advice and recommendations given previously by the institution's human advisors. This database is used to derive a ruleset which is then applied to a subsequent customer's details in an automated manner, possibly at the user's own PC via the World Wide Web. Frequent updating using data from the human advisors' work means that the database (and hence the ruleset) are kept up to date. Consistent advice can thus be provided with minimum human interaction.

Abstract: A model of consumer behavior in a transaction environment such as customers moving around a bank branch, is generated from an artificial life algorithm to create a number of agents. In each agent, a genetically encoded drive, equivalent for example to hunger, is defined so as to correspond to a transaction need such as the need for cash. Interaction rules, such as navigation rules, are set for interaction between the agents and a first representation of an environment, and the program is run and the agents observed, then compared with real human behavior. The best matched agents are selected and the program run again, the steps being repeated until a required level of comparison with real behavior is reached. The model can then be used with different transaction environments to study customer behavior and to select the best branch layout or the like.

Abstract: An apparatus is configured to automatically search for relevant picture data sets from a quantity of n (n≧2) picture data sets electronically stored in a memory device. Picture attributes for each of the n picture data sets is stored electronically in the memory device, and the n picture data sets as well as the stored picture attributes are adapted to be processed electronically by processor device. A training quantity including the electronically stored picture attributes of picture data sets which were selected by a user is utilized in the context of a machine learning process carried out automatically with the aid of the processor device to determine a decision function f. Subsequently, system relevancies of the picture data sets are calculated, based on the decision function f. Selected picture data sets then are provided in the context of the search result to be output in response to the respective system relevance.

Abstract: A system and a method for an automated intelligent information mining includes receiving product-related queries and respective product-related information from various text sources; extracting multiple key-phrases from the product-related information and received queries; generating two or more layers of contextual relation maps by mapping the extracted key-phrases to two-dimensional maps using a self organizing map, and a technique including a combination of Hessian matrix and Perturbation technique to enhance the learning process and to categorize the extracted key-phrases based on a contextual meaning. Further, the technique includes forming word clusters and constructing corresponding key phrase frequency histograms for each of the generated contextual relation maps.

Abstract: Computation elements are connected to one another with a first subsystem having a first input computation element, to which time series values, which each describe one state of a system in a first state space at a time, can be supplied. The first input computation element is connected to a first intermediate computation element, by which a state of the system can be described in a second state space at a time. In a second subsystem a second intermediate computation element, by which a state of the system can be described in the second state space at a time, is connected to a first output computation element, on which a first output signal can be tapped off. In a third subsystem a third intermediate computation element, by which a state of the system can be described in the second state space at a time, is connected to a second output computation element, on which a second output signal can be tapped off.

Abstract: A system and method for providing computerized, knowledge-based medical diagnostic and treatment advice. The medical advice is provided to the general public over a telephone network. Two new authoring languages, interactive voice response and speech recognition are used to enable expert and general practitioner knowledge to be encoded for access by the public. “Meta” functions for time-density analysis of a number of factors regarding the number of medical complaints per unit of time are an integral part of the system. A semantic discrepancy evaluator routine along with a mental status examination are used to detect the consciousness level of a user of the system. A re-enter feature monitors the user's changing condition over time. A symptom severity analysis helps to respond to the changing conditions. System sensitivity factors may be changed at a global level or other levels to adjust the system advice as necessary.

Abstract: A reduced control system suitable for control of a nonlinear or unstable plant is described. The reduced control system is configured to use a reduced sensor set for controlling the plant without significant loss of control quality (accuracy) as compared to an optimal control system with an optimum sensor set. The control system calculates the information content provided by the reduced sensor set as compared to the information content provided by the optimum set. The control system also calculates the difference between the entropy production rate of the plant and the entropy production rate of the controller. A genetic optimizer is used to tune a fuzzy neural network in the reduced controller. A fitness function for the genetic optimizer provides optimum control accuracy in the reduced control system by minimizing the difference in entropy production while maximizing the sensor information content.

Abstract: Disclosed is a system for approximating conditional probabilities using an annotated decision tree where predictor values that did not exist in training data for the system are tracked, stored, and referenced to determine if statistical aggregation should be invoked. Further disclosed is a system for storing statistics for deriving a non-leaf probability corresponding to predictor values, and a system for aggregating such statistics to approximate conditional probabilities.

Abstract: A medical system that is coupled to an endoscope which provides a video image to a monitor. The system includes an electrical circuit to overlay a graphic image onto the video image provided by the endoscope. The endoscope is moved by a robotic arm.

Abstract: System for enhancing knowledge discovery from data using a learning machine in general and a support vector machine in particular in a distributed network environment. A customer transmits training data, test data and live data to a vendor's server from a remote source, via a distributed network. The training biological data, test biological data and live biological data is stored in a storage device. Training biological data is then pre-processed in order to add meaning thereto. Pre-processing data involves transforming the biological data points and/or expanding the biological data points. Live biological data is pre-processed and input into the trained and tested learning machine. The live output from the learning machine is then post-processed into a computationally derived alphanumerical classifier for interpretation by a human or computer automated process.

Abstract: A system and method which allow genetic algorithms to more accurately parallel a biological genetic paradigm by employing synthetic genes. Genotypical synthetic chromosomes are recursively expanded to produce a population of phenotypical state models, bit strings or other forms of encoding that suitably represent a particular problem set. Sexual-like recombination takes place at the synthetic chromosome level, using synthetic genes, whereas fitness evaluation and selection take place at the level of the fully developed, i.e. expanded, and encoded structure. Additionally, a system and method are also taught for encoding complex data structures into synthetic genes as well as a system and method for non-replicative information data set encryption utilizing synthetic genes wherein information extraneous to the data set, such as encryption keys, may be embedded within the synthetic gene structure.

Abstract: A method for encoding the complete volumetric information of a virtual object and for retrieval of the same, using synthetic genes. The method efficiently describes the internal volumetric detail of a virtual object by encoding the object's outermost surface into a synthetic gene sequence. A set of rules is defined, by which a given synthetic gene sequence is expanded so as to produce an exact replica of a virtual object's outermost surface.