Abstract: Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

Abstract: Systems and methods to define a scenario of conditions comprising the steps of defining at least one condition for at least one educational objective, the at least one condition being represented by a constraint and scheduling the conditions into a scenario of conditions. In some embodiments, the scheduling is performed by analyzing the constraints using constraint programming. In some embodiments, the constraints comprise mathematical or computational constraints representing a range of variables. Also disclosed are systems and methods to monitor a scenario of conditions.

Abstract: Systems and methods to define a scenario of conditions comprising the steps of defining at least one condition for at least one educational objective, the at least one condition being represented by a constraint and scheduling the conditions into a scenario of conditions. In some embodiments, the scheduling is performed by analyzing the constraints using constraint programming. In some embodiments, the constraints comprise mathematical or computational constraints representing a range of variables. Also disclosed are systems and methods to monitor a scenario of conditions.

Abstract: Processor based systems and methods of defining a scenario event comprising the steps of identifying an event having an event attribute and generalizing the event attribute to define a generalized event whereby the generalized event is the scenario event. In some embodiments, the steps further comprise identifying a first and second event, generalizing a first and second event attribute to define a first and second generalized event and connecting the first and second generalized event in a continuous envelope to create a scenario envelope. Processor based systems and methods of monitoring an activity comprising the steps of monitoring an activity having an activity attribute and comparing the activity attribute to an event envelope to determine a status of the activity relative to the event envelope.

Abstract: Processor based systems and methods of defining a scenario event comprising the steps of identifying an event having an event attribute and generalizing the event attribute to define a generalized event whereby the generalized event is the scenario event. In some embodiments, the steps further comprise identifying a first and second event, generalizing a first and second event attribute to define a first and second generalized event and connecting the first and second generalized event in a continuous envelope to create a scenario envelope. Processor based systems and methods of monitoring an activity comprising the steps of monitoring an activity having an activity attribute and comparing the activity attribute to an event envelope to determine a status of the activity relative to the event envelope.

Abstract: Processor based systems and methods of defining a scenario event comprising the steps of identifying an event having an event attribute and generalizing the event attribute to define a generalized event whereby the generalized event is the scenario event. In some embodiments, the steps further comprise identifying a first and second event, generalizing a first and second event attribute to define a first and second generalized event and connecting the first and second generalized event in a continuous envelope to create a scenario envelope. Processor based systems and methods of monitoring an activity comprising the steps of monitoring an activity having an activity attribute and comparing the activity attribute to an event envelope to determine a status of the activity relative to the event envelope.

Abstract: A computer implemented systems and methods for determining an action for a user within a learning domain are disclosed, some embodiments of the methods comprise defining an initial learning model of a learning domain, determining an initial user state of the user, determining an initial user action from at least one learning domain action with the initial learning model, receiving a user observation of the user after the user executes the initial user action, determining an updated user state with the initial learning model given the updated user observation and determining a subsequent user action from the at least one learning domain action. Some embodiments utilize a Partially Observable Markov Model (POMDP) as the learning model.

Abstract: A processor based system and method of generating new pattern knowledge of a sensory input is disclosed. The system comprises a memory to store abstract patterns as pattern knowledge, a sensor configured to receive a concrete pattern representing a sensory input, an adaption module configured to horizontally blended abstract patterns to create a horizontally blended abstract pattern as the new pattern knowledge if a degree of blending the abstract and concrete patterns exceeds a threshold. In some embodiments, the horizontally blended abstract pattern comprises a selective projection of parameters of two abstract patterns. In some embodiments, the systems and methods further comprise vertically blending the abstract pattern and the concrete pattern by selectively projecting parameters of an abstract pattern with the parameters of the concrete pattern.

Abstract: Processor based systems and methods of defining a scenario event comprising the steps of identifying an event having an event attribute and generalizing the event attribute to define a generalized event whereby the generalized event is the scenario event. In some embodiments, the steps further comprise identifying a first and second event, generalizing a first and second event attribute to define a first and second generalized event and connecting the first and second generalized event in a continuous envelope to create a scenario envelope. Processor based systems and methods of monitoring an activity comprising the steps of monitoring an activity having an activity attribute and comparing the activity attribute to an event envelope to determine a status of the activity relative to the event envelope.

Abstract: A processor based system and method of generating cognitive pattern knowledge of a sensory input is disclosed. The method comprising the steps of receiving sensory input to create at least one concrete pattern, receiving at least one abstract pattern comprising abstract segments and vertically blending the concrete pattern with the abstract pattern by selectively projecting abstract segments to create a vertically blended pattern whereby the vertically blended pattern represents cognitive pattern knowledge of the sensory input. In some embodiments, the systems and methods further comprise creating a measure of a degree of vertical blending and when the measure of the degree of vertical blending exceeds a threshold, horizontally blending at least two abstract patterns to create a horizontally blended abstract pattern.

Abstract: Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

Abstract: Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic multi-attribute graph matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

Abstract: A processor based system and method of generating cognitive pattern knowledge of a sensory input is disclosed. The method comprising the steps of receiving sensory input to create at least one concrete pattern, receiving at least one abstract pattern comprising abstract segments and vertically blending the concrete pattern with the abstract pattern by selectively projecting abstract segments to create a vertically blended pattern whereby the vertically blended pattern represents cognitive pattern knowledge of the sensory input. In some embodiments, the systems and methods further comprise creating a measure of a degree of vertical blending and when the measure of the degree of vertical blending exceeds a threshold, horizontally blending at least two abstract patterns to create a horizontally blended abstract pattern.

Abstract: A processor based system and method of generating cognitive pattern knowledge of a sensory input is disclosed. The method comprising the steps of receiving sensory input to create at least one concrete pattern, receiving at least one abstract pattern comprising abstract segments and vertically blending the concrete pattern with the abstract pattern by selectively projecting abstract segments to create a vertically blended pattern whereby the vertically blended pattern represents cognitive pattern knowledge of the sensory input. In some embodiments, the systems and methods further comprise creating a measure of a degree of vertical blending and when the measure of the degree of vertical blending exceeds a threshold, horizontally blending at least two abstract patterns to create a horizontally blended abstract pattern.

Abstract: Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic multi-attribute graph matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

Abstract: Processor based systems and methods of defining a scenario event comprising the steps of identifying an event having an event attribute and generalizing the event attribute to define a generalized event whereby the generalized event is the scenario event. In some embodiments, the steps further comprise identifying a first and second event, generalizing a first and second event attribute to define a first and second generalized event and connecting the first and second generalized event in a continuous envelope to create a scenario envelope. Also disclosed are processor based systems and methods of monitoring an activity comprising the steps of monitoring an activity having an activity attribute and comparing the activity attribute to an event envelope to determine a status of the activity relative to the event envelope.

Abstract: A computer implemented systems and methods for determining an action for a user within a learning domain are disclosed, some embodiments of the methods comprise defining an initial learning model of a learning domain, determining an initial user state of the user, determining an initial user action from at least one learning domain action with the initial learning model, receiving a user observation of the user after the user executes the initial user action, determining an updated user state with the initial learning model given the updated user observation and determining a subsequent user action from the at least one learning domain action. Some embodiments utilize a Partially Observable Markov Model (POMDP) as the learning model.

Abstract: Systems and methods to define a scenario of conditions comprising the steps of defining at least one condition for at least one educational objective, the at least one condition being represented by a constraint and scheduling the conditions into a scenario of conditions. In some embodiments, the scheduling is performed by analyzing the constraints using constraint programming. In some embodiments, the constraints comprise mathematical or computational constraints representing a range of variables. Also disclosed are systems and methods to monitor a scenario of conditions.

Abstract: A processor based system and method of generating cognitive pattern knowledge of a sensory input is disclosed. The method comprising the steps of receiving sensory input to create at least one concrete pattern, receiving at least one abstract pattern comprising abstract segments and vertically blending the concrete pattern with the abstract pattern by selectively projecting abstract segments to create a vertically blended pattern whereby the vertically blended pattern represents cognitive pattern knowledge of the sensory input. In some embodiments, the systems and methods further comprise creating a measure of a degree of vertical blending and when the measure of the degree of vertical blending exceeds a threshold, horizontally blending at least two abstract patterns to create a horizontally blended abstract pattern.

Abstract: A workstation that employs methods to construct computer programs through use of visual graphical representations. Computer programs are illustrated as visual road maps of the intended sequence of actions. Each operational entity in a program graph on the screen is represented as an elemental "atomic" unit, called a "Softron". The Softron is a multidimensional, graphical "atom" of programming information which has four modes of operation, termed "layers". The four layers are Normal, where the basic functionally of the application resides; Initialization/Reset, responsible both for the startup values of important variables and for their values at strategic checkpoints; Error, which handles conditions outside design limits; and Input/Output, which performs human input/output and other I/O tasks. Softrons reside in very general form in the workstation's library, and are optimized by the process of specialization. Softrons may be grouped to form new Softrons by a process called Logical Zoom (TM).