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Robust Intelligence (RI) under uncertainty: Mathematical foundations of autonomous hybrid (human-machine-robot) teams, organizations and systems

  • Author(s): Lawless, William F.
  • et al.
Abstract

To develop a theory of Robust Intelligence (RI), we continue to advance our theory of interdependence on the efficient and effective control of systems of autonomous hybrid teams composed of robots, machines and humans working interchangeably. As is the case with humans, we believe that RI is less likely to be achieved by individual computational agents; instead, we propose that a better path to RI is with interdependent agents. However, unlike conventional computational models where agents act independently of neighbors, where, for example, a predator mathematically consumes its prey or not as a function of a random interaction process, dynamic interdependence means that agents dynamically respond to the bi-directional signals of actual or potential presence of other agents (e.g., in states poised to fight or flight), a significant increase over conventional modeling complexity.  That this problem is unsolved, mathematically and conceptually, precludes hybrid teams from processing information like human teams operating under challenges and perceived threats. To simplify this problem, we use bistable models for interdependence  with a focus on teams and firms as we increase complexity to the level of systems. As part of the problem, in this paper, and countering simplification, sentient multi-agent systems require an aggregation process like data fusion. But the conventional use of fusion for the control of mobile systems hinges on mathematical convergence into patterns, increasing uncertainty whenever divergent information has the potential to process information into knowledge. The goals of our research are: First, to analyze why valid models of interdependence are difficult to build. Second, to reduce uncertainty in decision-making by moderating convergence processes in data aggregation (e.g., fusion) with differential clustering between alternative (orthogonal) views that check convergence processes and promote information processing (e.g., second opinions from independent physicians; prosecutor-defense attorneys; Republicans-Democrats in Congress; opposed scientists, like Bohr-Einstein). Third, in line with our theoretical expectations, we plan to lay the groundwork for agent-based systems to model the stability from the cooperative contexts associated with teams, and the instability from the competitive contexts associated with multiple teams or firms that constitute systems. Our result will be a new theory of interdependence; a new model of data aggregation; and new agent-based models of interdependence.

 

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