On Fri, Sep 2, 2011 at 1:06 PM, David Barbour <[email protected]> wrote:
> > > My Reactive Demand Programming (RDP) model is, of course, designed for live > programming. Development this June and July has led to me re-envisioning how > various state models might be exposed to an RDP application, to improve live > programming. One interesting possibility I've been developing since last > weekend is to use TRS as a basis for state: state is a 'term', and the set > of rewrite rules is a time-varying quantity. > > Thanks for your list. For your own goals, check out Montanari's work on representing concurrent computations as contextual nets. Contextual nets are used to close off feedback loops so that hard problems like "weakly" and "meagerly" specified computations do not interfere with the overall goal of a program. One example problem to battle is the Brock-Ackerman Anomaly. Also, Jose Mesegeur has several papers on implementing temporal logic in a term rewriting system. And Dave MacQueen has a very good tutorial titled "Kahn Networks at the Dawn of Functional Programming" that loosely traces some history in the development of reasoning about dataflow programs. I generally like the idea of representing state as a database of values. Round 2 of my question would be, What can we learn from the problem solving approaches used by each of these domains? Can the tools and techniques be combined to solve design contradictions well-known in one domain but totally shadowed by the taking the perspective from another problem domain?
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