The Experts below are selected from a list of 201 Experts worldwide ranked by ideXlab platform

David R Jacques - One of the best experts on this subject based on the ideXlab platform.

  • applying Control Abstraction to the design of human agent teams
    System, 2020
    Co-Authors: Clifford D Johnson, Michael E Miller, Christina F Rusnock, David R Jacques
    Abstract:

    Levels of Automation (LOA) provide a method for describing authority granted to automated system elements to make individual decisions. However, these levels are technology-centric and provide little insight into overall system operation. The current research discusses an alternate classification scheme, referred to as the Level of Human Control Abstraction (LHCA). LHCA is an operator-centric framework that classifies a system’s state based on the required operator inputs. The framework consists of five levels, each requiring less granularity of human Control: Direct, Augmented, Parametric, Goal-Oriented, and Mission-Capable. An analysis was conducted of several existing systems. This analysis illustrates the presence of each of these levels of Control, and many existing systems support system states which facilitate multiple LHCAs. It is suggested that as the granularity of human Control is reduced, the level of required human attention and required cognitive resources decreases. Thus, it is suggested that designing systems that permit the user to select among LHCAs during system Control may facilitate human-machine teaming and improve the flexibility of the system.

  • a framework for understanding automation in terms of levels of human Control Abstraction
    Systems Man and Cybernetics, 2017
    Co-Authors: Clifford D Johnson, Michael E Miller, Christina F Rusnock, David R Jacques
    Abstract:

    Levels of Autonomy (LoA) provide a method for describing authority granted to operators and autonomous system elements. Unfortunately, LoA does not provide the user interface designer a clear method to distinguish interface concepts which impose varying levels of operator workload or result in human or system performance changes. The current research suggests an alternate classification framework for vehicle Control, referred to as the Level of Human Control Abstraction (LHCA). LHCA describes how an operator Controls a system based on the Control tasks performed and the level of detail of decisions made by the operator. The proposed framework consists of five levels: Direct Control, Augmented Control, Parametric Control, Goal-Oriented Control, and Mission-Capable Control. It is suggested that as the level of detail of Control is reduced through progression from Direct Control to Mission-Capable Control, the level of human attention, and workload will be reduced.

  • SMC - A framework for understanding automation in terms of levels of human Control Abstraction
    2017 IEEE International Conference on Systems Man and Cybernetics (SMC), 2017
    Co-Authors: Clifford D Johnson, Michael E Miller, Christina F Rusnock, David R Jacques
    Abstract:

    Levels of Autonomy (LoA) provide a method for describing authority granted to operators and autonomous system elements. Unfortunately, LoA does not provide the user interface designer a clear method to distinguish interface concepts which impose varying levels of operator workload or result in human or system performance changes. The current research suggests an alternate classification framework for vehicle Control, referred to as the Level of Human Control Abstraction (LHCA). LHCA describes how an operator Controls a system based on the Control tasks performed and the level of detail of decisions made by the operator. The proposed framework consists of five levels: Direct Control, Augmented Control, Parametric Control, Goal-Oriented Control, and Mission-Capable Control. It is suggested that as the level of detail of Control is reduced through progression from Direct Control to Mission-Capable Control, the level of human attention, and workload will be reduced.

Michael E Miller - One of the best experts on this subject based on the ideXlab platform.

  • SMC - Simulation-Based Evaluation of the Effects of Varying Degrees of Control Abstraction for Manned-Unmanned Teaming on Mental Workload of Pilots
    2020 IEEE International Conference on Systems Man and Cybernetics (SMC), 2020
    Co-Authors: Jinan M. Andrews, Michael E Miller, Chrstina F. Rusnock, Douglas P. Meador
    Abstract:

    The future of air combat is expected to evolve significantly to include new technologies and novel concepts of operation. The Manned-Unmanned Teaming concept involves low cost, attritable Unmanned Aerial Vehicles (UAVs) that could be deployed along with a manned aircraft. The UAVs act as a complementary asset and bolster offensive air operations. Given the complexity of future operating environments, the degree of autonomous Control required for pilots to concurrently operate multiple UA Vs and their own aircraft is one area of concern. To determine the amount of autonomous Control Abstraction that has the largest impact in reducing operator workload and increasing system performance, a predictive workload model was developed using the Improved Performance Research Integration Tool (IMPRINT). This research concluded that maned-unmanned teams can increase mission performance and maintain the pilot’s cognitive workload at a manageable level by utilizing higher levels of human Control Abstraction, where unmanned systems have greater degree of autonomy.

  • applying Control Abstraction to the design of human agent teams
    System, 2020
    Co-Authors: Clifford D Johnson, Michael E Miller, Christina F Rusnock, David R Jacques
    Abstract:

    Levels of Automation (LOA) provide a method for describing authority granted to automated system elements to make individual decisions. However, these levels are technology-centric and provide little insight into overall system operation. The current research discusses an alternate classification scheme, referred to as the Level of Human Control Abstraction (LHCA). LHCA is an operator-centric framework that classifies a system’s state based on the required operator inputs. The framework consists of five levels, each requiring less granularity of human Control: Direct, Augmented, Parametric, Goal-Oriented, and Mission-Capable. An analysis was conducted of several existing systems. This analysis illustrates the presence of each of these levels of Control, and many existing systems support system states which facilitate multiple LHCAs. It is suggested that as the granularity of human Control is reduced, the level of required human attention and required cognitive resources decreases. Thus, it is suggested that designing systems that permit the user to select among LHCAs during system Control may facilitate human-machine teaming and improve the flexibility of the system.

  • a framework for understanding automation in terms of levels of human Control Abstraction
    Systems Man and Cybernetics, 2017
    Co-Authors: Clifford D Johnson, Michael E Miller, Christina F Rusnock, David R Jacques
    Abstract:

    Levels of Autonomy (LoA) provide a method for describing authority granted to operators and autonomous system elements. Unfortunately, LoA does not provide the user interface designer a clear method to distinguish interface concepts which impose varying levels of operator workload or result in human or system performance changes. The current research suggests an alternate classification framework for vehicle Control, referred to as the Level of Human Control Abstraction (LHCA). LHCA describes how an operator Controls a system based on the Control tasks performed and the level of detail of decisions made by the operator. The proposed framework consists of five levels: Direct Control, Augmented Control, Parametric Control, Goal-Oriented Control, and Mission-Capable Control. It is suggested that as the level of detail of Control is reduced through progression from Direct Control to Mission-Capable Control, the level of human attention, and workload will be reduced.

  • SMC - A framework for understanding automation in terms of levels of human Control Abstraction
    2017 IEEE International Conference on Systems Man and Cybernetics (SMC), 2017
    Co-Authors: Clifford D Johnson, Michael E Miller, Christina F Rusnock, David R Jacques
    Abstract:

    Levels of Autonomy (LoA) provide a method for describing authority granted to operators and autonomous system elements. Unfortunately, LoA does not provide the user interface designer a clear method to distinguish interface concepts which impose varying levels of operator workload or result in human or system performance changes. The current research suggests an alternate classification framework for vehicle Control, referred to as the Level of Human Control Abstraction (LHCA). LHCA describes how an operator Controls a system based on the Control tasks performed and the level of detail of decisions made by the operator. The proposed framework consists of five levels: Direct Control, Augmented Control, Parametric Control, Goal-Oriented Control, and Mission-Capable Control. It is suggested that as the level of detail of Control is reduced through progression from Direct Control to Mission-Capable Control, the level of human attention, and workload will be reduced.

Jacques Garrigue - One of the best experts on this subject based on the ideXlab platform.

  • ICFP - Recursive modules for programming
    Proceedings of the eleventh ACM SIGPLAN international conference on Functional programming - ICFP '06, 2006
    Co-Authors: Keiko Nakata, Jacques Garrigue
    Abstract:

    TheML module system is useful for building large-scale programs. The programmer can factor programs into nested and parameterized modules, and can Control Abstraction with signatures. Yet ML prohibits recursion between modules. As a result of this constraint, the programmer may have to consolidate conceptually separate components into a single module, intruding on modular programming. Introducing recursive modules is a natural way out of this predicament. Existing proposals, however, vary in expressiveness and verbosity. In this paper, we propose a type system for recursive modules, which can infer their signatures. Opaque signatures can also be given explicitly, to provide type Abstraction either inside or outside the recursion. The type system is decidable, and is sound for a call-by-value semantics. We also present a solution to the expression problem, in support of our design choices.

Demetri Terzopoulos - One of the best experts on this subject based on the ideXlab platform.

  • automated learning of muscle actuated locomotion through Control Abstraction
    International Conference on Computer Graphics and Interactive Techniques, 1995
    Co-Authors: Radek Grzeszczuk, Demetri Terzopoulos
    Abstract:

    We present a learning technique that automatically syn- thesizes realistic locomotion for the animation of physics-based models of animals. The method is especially suitable for animals with highly flexible, many-degree-of-freedom bodies and a consid- erable number of internal muscle actuators, such as snakes and fish. The multilevel learning process first performs repeated loco- motion trials in search of actuator Control functions that produce efficient locomotion, presuming virtually nothing about the form of these functions. Applying a short-time Fourier analysis, the learn- ing process then abstracts Control functions that produce effective locomotion into a compact representation which makes explicit the natural quasi-periodicities and coordination of the muscle actions. The artificial animals can finally put into practice the compact, efficient Controllers that they have learned. Their locomotion learn- ing abilities enable them to accomplish higher-level tasks specified by the animator while guided by sensory perception of their vir- tual world; e.g., locomotion to a visible target. We demonstrate physics-based animation of learned locomotion in dynamic models of land snakes, fishes, and even marine mammals that have trained themselves to perform "SeaWorld" stunts.

  • SIGGRAPH - Automated learning of muscle-actuated locomotion through Control Abstraction
    Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH '95, 1995
    Co-Authors: Radek Grzeszczuk, Demetri Terzopoulos
    Abstract:

    We present a learning technique that automatically syn- thesizes realistic locomotion for the animation of physics-based models of animals. The method is especially suitable for animals with highly flexible, many-degree-of-freedom bodies and a consid- erable number of internal muscle actuators, such as snakes and fish. The multilevel learning process first performs repeated loco- motion trials in search of actuator Control functions that produce efficient locomotion, presuming virtually nothing about the form of these functions. Applying a short-time Fourier analysis, the learn- ing process then abstracts Control functions that produce effective locomotion into a compact representation which makes explicit the natural quasi-periodicities and coordination of the muscle actions. The artificial animals can finally put into practice the compact, efficient Controllers that they have learned. Their locomotion learn- ing abilities enable them to accomplish higher-level tasks specified by the animator while guided by sensory perception of their vir- tual world; e.g., locomotion to a visible target. We demonstrate physics-based animation of learned locomotion in dynamic models of land snakes, fishes, and even marine mammals that have trained themselves to perform "SeaWorld" stunts.

Douglas P. Meador - One of the best experts on this subject based on the ideXlab platform.

  • SMC - Simulation-Based Evaluation of the Effects of Varying Degrees of Control Abstraction for Manned-Unmanned Teaming on Mental Workload of Pilots
    2020 IEEE International Conference on Systems Man and Cybernetics (SMC), 2020
    Co-Authors: Jinan M. Andrews, Michael E Miller, Chrstina F. Rusnock, Douglas P. Meador
    Abstract:

    The future of air combat is expected to evolve significantly to include new technologies and novel concepts of operation. The Manned-Unmanned Teaming concept involves low cost, attritable Unmanned Aerial Vehicles (UAVs) that could be deployed along with a manned aircraft. The UAVs act as a complementary asset and bolster offensive air operations. Given the complexity of future operating environments, the degree of autonomous Control required for pilots to concurrently operate multiple UA Vs and their own aircraft is one area of concern. To determine the amount of autonomous Control Abstraction that has the largest impact in reducing operator workload and increasing system performance, a predictive workload model was developed using the Improved Performance Research Integration Tool (IMPRINT). This research concluded that maned-unmanned teams can increase mission performance and maintain the pilot’s cognitive workload at a manageable level by utilizing higher levels of human Control Abstraction, where unmanned systems have greater degree of autonomy.