This version is much shorter, but has increased information about predictability of timeliness for scheduling under epistemic uncertainties using mathematical theories of evidence, as covered in the body of the site and the book.
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This site is a discourse about some of my novel research on real-time (including, but not limited to, computing) systems. It is a highly condensed and simplified work-in-progress preview of a work-in-progress book about that research: An Introduction to
Fundamental Principles of Timeliness in Dynamically Real-Time Systems [Jensen 2018].

This research is based on my career’s extensive experience performing research and development on real-time systems which have dynamic timeliness and predictability of timeliness resulting from intrinsic epistemic uncertainties–e.g., ignorance, non-
determinism–in the system and its application environment.

(My consulting practice web site is time-critical-technologies.com but I have retired from consulting except in certain cases of classified national security.)

The book and preview show that most real-time systems are beneficially recognized to be in that category, contrary to the traditional real-time computing systems orthodoxy. Traditional real-time computing system concepts and techniques are for a narrow (
albeit important) special case, based on presumed a’ priori omniscience of a predominately static periodic-based system model. They are not applicable to the general case of real-time systems which dominate the real world.

The benefits of quantifying and exploiting epistemic uncertainties are very widely recognized in a great many fields of endeavor. Examples familiar to everyone include autonomous systems such as robots, and semi-autonomous systems such as augmented
driving of automobiles.

This site provides a principled perspective for general real-time systems which has been proven uniquely effective for reasoning about timeliness and the predictability of timeliness, despite uncertainties—and yet which scales down to not just
encompass but improve the traditional static special case.

Reasoning about dynamic real-time actions and systems requires formalization of “timeliness” and “predictability.”

This book and site employ the time/utility (sometimes called time/value) function paradigm [Jensen 77, Jensen 85] as the basis for formalizing timeliness. Although that paradigm has been discussed in many papers and dissertations, much more detail about
it is provided here. Predictability of actions’ completion times and thus accrued utility remains promising for continued development of theory and engineering.

Formalizing predictability of timeliness in dynamic real-time systems requires use of an appropriate mathematical approach to dealing with uncertainties.

There exists a variety of widely used candidate approaches, each having specific conceptual and practical properties for different applications. The book describes advantages and disadvantages of some in the context of predictability of timeliness under
uncertainties–specifically, for predicting the completion times and consequent semantics (i.e., utilities) of scheduled actions (e.g., computational tasks).

Probability theory typically comes first to mind when considering predictability. However, there are a number of different interpretations of “probability” (i.e., probability theories). The best known to non-specialists is the frequentist
interpretation (cf. tossing dice), which is shown herein to be the least appropriate for predictability in dynamic real-time systems.

Also well known and widely used is the Bayesian probability theory, but it has several drawbacks. The strongest criticism of the Bayesian theory is its inability to distinguish between ignorance and randomness, which is overcome in other theories for
dealing with uncertainty.

Several of those, particularly belief function and evidence theories, (e.g., Dempster-Shafer, Transferable Belief Model, Dezert-Smranche) are candidates for being employed by action schedulers in certain dynamic real-time systems. These predict
timeliness and thus utilities by combining (even conflicting) beliefs or evidence from the system and application context.

Unsurprisingly, greater epistemic uncertainty (e.g., ignorance) leads to greater computation costs for resolving it. Fortunately, there is a rich body of literature on efficient algorithms for using belief function and evidence theory, which trade off
different aspects of the solution space to accelerate computations.

Examples of using the articulated fundamental principles in dynamic real-time systems are provided in the book.

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