Appropriate automation: Human system dynamics for control systems


Hybrid for appropriate automation

Figure 3. This canonical hybrid intelligence processor depicts a model of a closed-loop control system. Courtesy: Rational LLC and Jenius LLCAn approach is needed to guide process control engineering in the quest for appropriate automation. Sometimes insights can come from unlikely places. Early research in psychology may have something to offer here.

Figure 3 depicts a model of a closed-loop control system that embodies the first principles discussed. The ideas in Figure 3 are not entirely new, nor purely theoretical. Although it incorporates ideas such as search, proposed by Newell and Simon (1975), as a requisite for intelligence, an earlier version of the figure has been used as a reference design for military and commercial sensor management systems, and similar models abound in the human factors and HSI research literature. (cf Harris, Ballard, Girard and Gluckman, 1993; Wickens and Carswell, 2004).

Note the words "human," "software," and "hardware" do not appear in the figure. The architecture can be applied to any intelligent control system, whether it be purely silicon-based, mixed silicon-organic based, or even purely organic. Systems that are organized as in Figure 3, and that comprise human and machine components, exhibit capabilities and limitations as a system that are not evident-and may not even exist in either component alone because they are emergent. Thus such systems are hybrid intelligence systems. Such systems can routinely "solve" induction problems.

This processor is canonical because it entails the essential components of a real-time, closed-loop control system capable of intelligent behavior; yet, it does not specify how the processes are implemented. As a simple rule of thumb, at least for the foreseeable future, the induction authority should be human, and the deductive engine should be a machine, but those allocations of function might change.

IBM's Watson system, for example, may be capable of induction (Fan, Ferrucci, Gondek and Kalyanpur, 2010). Watson is the first of an emerging generation of machine intelligence systems intended to operate in real-world environments to solve real-world problems. Yet, such intelligent machines can't operate in a completely automated fashion. Their recommendations for solution to induction problems are just one more source of information to the human control authority. Watson is designed to be a team member, not the final decision-maker. Such machines must be integrated with human cognition in a coherent fashion to augment and enhance human cognitive performance. Hence, with the advent of Watson, new principles for integrating human and machine intelligence into a cohesive whole are needed. We trust that improved understanding of the fundamentals of induction and human cognition will contribute measurably to the next generation of control engineering.

What cannot be automated? From the perspective of first principles in control engineering, at least one answer is: Induction cannot be automated, at least not yet. Failure to understand the implications of that answer can be disastrous. New technology—IBM's Watson being the progenitor—may force us to revisit this principle in the near future.

- Steven D. Harris is president of Rational, LLC. Jennifer McGovern Narkevicius, PhD, is the managing director of Jenius, LLC and co-chair of INCOSE's Human Systems Integration Working Group. Edited by Mark T. Hoske, content manager, CFE Media, Control Engineering,

Key concepts

  • Automation, in this context, concerns the implementation of the control process inside a machine.
  • Even with closed-loop control, humans are involved.
  • Inductive logic devices aren't available in process control, yet.

Consider this

What efficiencies will be available when computing power and programming allow machine-based inductive reasoning with closed loop control?


This is a full-length version of an article scheduled to appear under the same headline in the March 2014 Control Engineering print and digital edition. See related articles tagged below. 


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Fan, J., Ferrucci, D., Gondek, D., and Kalyanpur, A. PRISMATIC: Inducing knowledge from a large scale lexicalized relation resource. Proceedings of the NAACL HLT 1020 First International Workshop on Formalisms and Methodology for Learning by Reading. Los Angeles, CA: Association for Computational Linguistics, 2010, pp. 122-127.

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Vibha , AL, India, 03/02/14 11:17 AM:

I have been read control problems and human safety. But control problem with neural network is also required.
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