Lambda the Ultimate

Abstract

The Interaction Nets (IN) of Lafont are a graphical formalism used to model parallel computation. Their genesis can be traced back to the Proof Nets of Linear Logic. They enjoy several nice theoretical properties, amongst them pure locality of interaction, strong confluence, computational completeness, syntactically-definable deadlock-free fragments, combinatorial completeness (existence of a Universal IN). They also have nice "pragmatic" properties: they are simple and elegant, intuitive, can capture aspects of computation at widely varying levels of abstraction. Compared to term and graph rewriting systems, INs are much simpler (a subset of such systems that imposes several constraints on the rewriting process), but are still computationally complete (can capture the lambda-calculus). INs are a refinement of graph rewriting which keeps only the essential features in the system.

Conventional INs are strongly confluent, and are therefore unsuitable for the modeling of non-deterministic systems such as process calculi and concurrent object-oriented programming. We study four diffrent ways of "breaking" the confluence of INs by introducing various extensions:

• IN with Multiple (reduction) Rules (INMR) Allow more than one reduction rule per redex.
• IN with Multiple Principal Ports (INMPP) Allow more than one active port per node.
• IN with MultiPorts (INMP) Allow more than one connection per port.
• IN with Multiple Connections (INMC) Allow hyper-edges (in the graph-theoretical sense), i.e. connections between more than two ports.

I have a few friends that have severe repetitive stress injury and can effectively no longer type for long periods of type.

I'm trying to consider an environment and a language which would be suited to speech-to-text input. My first thought on the base language is Standard ML since definitions are self contained and require no "punctuation" e.g.

let x=5
let y=7

is valid and complete without double-semicolons at the end. Thus, you could say "let x be five, let y be seven" and produce the above code without too much interpolation.

That said, there would have to be a grammar that translated a precise speech into ML and to be really effective, the ML generated would have to be constantly reparsed and kept in a symbol-table state so that the speech processing program could use the inherent structure of the underlying language to disambiguate slurred or otherwise ambiguous speech. Another good reason to use Standard ML is that parsed ML contains more information than many other languages due to type-safety putting restrictions on variable/function usage -- more disambiguation possible.

Does anyone have any thoughts on other requirements for such a beast or pointers to research that has already been done that I might not find through an old-fashined ACM/Citeseer/DBLP search?

An interesting CFP, that given the discussions here recently should interest at least a couple of readers.

Could I use the new VS XML Editor to edit XHTML and WordML? Absolutely.

Would I want to? Absolutely not.

The JoCaml system is an experimental extension of the Objective-Caml language with the distributed join-calculus programming model. This model includes high-level communication and synchronising channels,mobile agents, failure detection and automatic memory management. JoCaml enables programmers to rapidly develop distributed large-scale applications, using both Objective-Caml ease of programmation and extended libraries, and the join-calculus distributed and concurrent features.

Mentioned on LtU before, but never in a separate story.

Could become Erlang-killer, if were not stopped for some reason.

Definitely worth trying, if only for ideas on how to bring powerful concurrency/distribution to a mature language.

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