Today AI systems are rarely made without Machine Learning (ML) and this inspires us to explore what aptly called composite argumentation with ML components. Concretely, against two theoretical backdrops of PABA (Probabilistic Assumption-based Argumentation) DST (Dempster-Shafer Theory), we present a framework for such c-PABA. It is argued that c-PABA lends itself development tool as well demons...

We present a typed calculus IL (“intermediate language”) which supports the embedding of ML-like (strict, eager) and Haskell-like (non-strict, lazy) languages, without favoring either. IL’s type system includes negation (continuations), but not implication (function arrow). Within IL we find that lifted sums and products can be represented as the double negation of their unlifted counterparts. ...

This paper presents a compilation scheme from natural semantics speciications to ML. Permutations of premises allow, for a class of speciications, a purely functional execution and can reduce non-determinism. This method uses some speciic features of ML, like exceptions , closures and pattern-matching.