Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (“ADAS”), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: Mobile agents automatically manipulate components such as blocks on a working surface, to perform operations such as construction of generalized structures. The working surface and/or the components can have machine-readable codes to assist the agents in maintaining current knowledge of their respective locations. Agents identify components by type and location, and can move components according to directions; such directions can be provided by a user, or can be based on a pre-programmed directive, or can be determined dynamically based on current conditions or in response to actions of other agents. Agents may cooperate with one another. Agents can also respond to changes in the environment, alterations in works in progress, and/or other conditions, and may be configured to exhibit responses simulating emotional reactions. Different mobile agents can be associated with different character traits, which may be configured to change based on environmental conditions and/or the behavior of other mobile agents.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: Mobile agents automatically manipulate components such as blocks on a working surface, to perform operations such as construction of generalized structures. The working surface and/or the components can have machine-readable codes to assist the agents in maintaining current knowledge of their respective locations. Agents identify components by type and location, and can move components according to directions; such directions can be provided by a user, or can be based on a pre-programmed directive, or can be determined dynamically based on current conditions or in response to actions of other agents. Agents may cooperate with one another. Agents can also respond to changes in the environment, alterations in works in progress, and/or other conditions, and may be configured to exhibit responses simulating emotional reactions. Different mobile agents can be associated with different character traits, which may be configured to change based on environmental conditions and/or the behavior of other mobile agents.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: Mobile agents automatically manipulate components such as blocks on a working surface, to perform operations such as construction of generalized structures. The working surface and/or the components can have machine-readable codes to assist the agents in maintaining current knowledge of their respective locations. Agents identify components by type and location, and can move components according to directions; such directions can be provided by a user, or can be based on a pre-programmed directive, or can be determined dynamically based on current conditions or in response to actions of other agents. Agents may cooperate with one another. Agents can also respond to changes in the environment, alterations in works in progress, and/or other conditions, and may be configured to exhibit responses simulating emotional reactions. Different mobile agents can be associated with different character traits, which may be configured to change based on environmental conditions and/or the behavior of other mobile agents.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: Mobile agents automatically manipulate components such as blocks on a working surface, to perform operations such as construction of generalized structures. The working surface and/or the components can have machine-readable codes to assist the agents in maintaining current knowledge of their respective locations. Agents identify components by type and location, and can move components according to directions; such directions can be provided by a user, or can be based on a pre-programmed directive, or can be determined dynamically based on current conditions or in response to actions of other agents. Agents may cooperate with one another. Agents can also respond to changes in the environment, alterations in works in progress, and/or other conditions, and may be configured to exhibit responses simulating emotional reactions. Different mobile agents can be associated with different character traits, which may be configured to change based on environmental conditions and/or the behavior of other mobile agents.

Abstract: Mobile agents automatically manipulate components such as blocks on a working surface, to perform operations such as construction of generalized structures. The working surface and/or the components can have machine-readable codes to assist the agents in maintaining current knowledge of their respective locations. Agents identify components by type and location, and can move components according to directions; such directions can be provided by a user, or can be based on a pre-programmed directive, or can be determined dynamically based on current conditions or in response to actions of other agents. Agents may cooperate with one another. Agents can also respond to changes in the environment, alterations in works in progress, and/or other conditions, and may be configured to exhibit responses simulating emotional reactions. Different mobile agents can be associated with different character traits, which may be configured to change based on environmental conditions and/or the behavior of other mobile agents.

Abstract: Mobile agents automatically manipulate components such as blocks on a working surface, to perform operations such as construction of generalized structures. The working surface and/or the components can have machine-readable codes to assist the agents in maintaining current knowledge of their respective locations. Agents identify components by type and location, and can move components according to directions; such directions can be provided by a user, or can be based on a pre-programmed directive, or can be determined dynamically based on current conditions or in response to actions of other agents. Agents may cooperate with one another. Agents can also respond to changes in the environment, alterations in works in progress, and/or other conditions, and may be configured to exhibit responses simulating emotional reactions. Different mobile agents can be associated with different character traits, which may be configured to change based on environmental conditions and/or the behavior of other mobile agents.