Probabilistic Completion of Nondeterministic Models
Author(s) -
Guy. Beaulieu
Publication year - 2007
Publication title -
electronic notes in theoretical computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.242
H-Index - 60
ISSN - 1571-0661
DOI - 10.1016/j.entcs.2007.02.028
Subject(s) - nondeterministic algorithm , probabilistic logic , semilattice , regular polygon , closure (psychology) , computer science , mathematics , distributive property , axiom , theoretical computer science , discrete mathematics , algebra over a field , artificial intelligence , pure mathematics , semigroup , geometry , economics , market economy
This work continues ongoing research in combining theories of nondeterminism and probabilistic choice. First, we adapt the above choice theories to allow for uncountably indexed nondeterministic operators, and countably indexed probabilistic operators. Classically, models for mixed choice were obtained by enhancing arbitrary models for probabilistic choice with appropriately distributive nondeterministic operations. In this paper, we focus on the dual approach: constructing mixed choice models by completing nondeterministic models with suitably behaved probabilistic operations. We introduce a functorial construction, called convex completion, which freely computes set-theoretical and posetal mixed choice models from the appropriate semilattices. The completion construction relies upon a new closure operation on convex sets, dependant on the given semilattice. Finally, we show that building a free mixed choice model is equivalent to applying the convex completion functor to its corresponding free nondeterministic model
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