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  • Modeling Synergistic Coordination in Multi-Agent Human and Human-Machine Systems (NSF #1513801):
     
     The success of many everyday activities depends on the effective behavioral coordination of cooperating individuals. These activities include moving materials through an assembly line, loading a dishwasher with a family member, shaking hands with a co-worker, and maneuvering a remotely operated vehicle to repair a satellite. Recent research within the fields of human movement science, psychology and complex dynamical systems has revealed new methods for modelling these interactions. The objective of this project is to examine whether these human inspired models can be implemented in robotic and artificial agents in order to develop highly robust and flexible human-machine systems. Accordingly, the project will result in important new advances in interactive human-robotic and human-artificial-agent based systems by grounding the behavior of these systems in the dynamics of natural human-human interaction. More generally, the proposed project has significant implications for the design and development of workplace machinery, autonomous vehicles, and medical technologies where high levels of precise human-machine cooperation are necessary. Understanding how to implement human inspired behavioral dynamic models in robotic and artificial agents will also help with the development of assistive technologies, including prosthetics, robotic aids, and therapeutic technologies, that are better tailored to the needs of the specific end user.

    Cooperating individuals behave as a single, synergistic unit, with their actions and behaviors often coordinated in a self-organized manner, requiring little or no explicit direction or a priori planning. The objective of this project is to develop and test dynamical (differential equation) models that capture the synergistic self-organization of human multi-agent coordination and deploy these models in artificial agents (virtual and robotic agents) to create highly robust and mutually responsive coupled human-machine systems. The project is designed to advance a new framework for the development of coordinated human-machine systems, one that is derived from empirical evidence of the physical, informational and biomechanical processes that shape and constrain the dynamics of successful and adaptive joint-action behavior in humans. Using a set of object moving and passing tasks, the specific aims of the project are to demonstrate (1) how dynamical models of the synergistic coordination that occurs between pairs of human agents performing a physical joint-action task can serve as the basis for artificial agent performance and (2) can be implemented in systems of interacting human and artificial agents to produce stable and adaptive patterns of robust behavioral coordination equivalent to that observed during human-human interaction. The proposed research integrates contemporary methods from behavioral, cognitive and social psychology, human movement science, computer-science, complexity science, engineering, and philosophy and will advance our fundamental understanding of the behavioral dynamics of human joint action. Moreover, the proposed project will have broad and transformative implications for the development and design of interactive robotic and artificial agent based assistive, diagnostic, and therapeutic sensorimotor technologies.
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  • Characteristics of Non-reductive Explanations in Complex Dynamical Systems Research:

    Dissertation (Philosophy) – I argue that philosophical accounts of scientific explanation appear to agree that identification of constraints is a significant feature of scientific explanation. Moreover, scientific explanations are evaluated according to how well they facilitate human prediction, manipulation and understanding of a given phenomena. The constraints identified in an explanation may be due to the physical states and structures as they are observed, as in the speed of light or the mass of the coffee mug on the table. These are physical constraints. Constraints also depend on the choices and perspectives of the individuals or communities producing and consuming the explanation. These latter constraints I refer to as framing constraints. Framing constraints include the choice to observe a biological organism within a particular ecosystem as well as the choice to explain in the context of the observable universe. Ultimately, both physical constraints and framing constraints are not distinctive categories but extremes on a continuum.

    Complex dynamical systems theory provides a framework for characterizing and understanding increases in system order in the context of certain constraints. Increases in system order entail increases in the observed correlations of spatial, temporal, or energetic features as represented by variations in a system’s degrees of freedom. I argue that these increases in correlation length provide a basis for identifying characteristic scales of a system of interest that are larger than the scale defined in terms of the system’s smallest components. In the context of scientific explanation, increases in order also result in the elimination of smallest scale degrees of freedom and their corresponding constraints. When the smallest scale degrees of freedom are eliminated from an explanation, the explanation is non-reductive. Given the proposed account of explanation in terms of constraints and insights regarding scale in terms of complex dynamical systems theory, I conclude that scientific explanations in the life and social sciences are sometimes non-reductive and multi-scale.