651 research outputs found
On the Computation Power of Name Parameterization in Higher-order Processes
Parameterization extends higher-order processes with the capability of
abstraction (akin to that in lambda-calculus), and is known to be able to
enhance the expressiveness. This paper focuses on the parameterization of
names, i.e. a construct that maps a name to a process, in the higher-order
setting. We provide two results concerning its computation capacity. First,
name parameterization brings up a complete model, in the sense that it can
express an elementary interactive model with built-in recursive functions.
Second, we compare name parameterization with the well-known pi-calculus, and
provide two encodings between them.Comment: In Proceedings ICE 2015, arXiv:1508.0459
Foundational Extensible Corecursion
This paper presents a formalized framework for defining corecursive functions
safely in a total setting, based on corecursion up-to and relational
parametricity. The end product is a general corecursor that allows corecursive
(and even recursive) calls under well-behaved operations, including
constructors. Corecursive functions that are well behaved can be registered as
such, thereby increasing the corecursor's expressiveness. The metatheory is
formalized in the Isabelle proof assistant and forms the core of a prototype
tool. The corecursor is derived from first principles, without requiring new
axioms or extensions of the logic
Symblicit algorithms for optimal strategy synthesis in monotonic Markov decision processes
When treating Markov decision processes (MDPs) with large state spaces, using
explicit representations quickly becomes unfeasible. Lately, Wimmer et al. have
proposed a so-called symblicit algorithm for the synthesis of optimal
strategies in MDPs, in the quantitative setting of expected mean-payoff. This
algorithm, based on the strategy iteration algorithm of Howard and Veinott,
efficiently combines symbolic and explicit data structures, and uses binary
decision diagrams as symbolic representation. The aim of this paper is to show
that the new data structure of pseudo-antichains (an extension of antichains)
provides another interesting alternative, especially for the class of monotonic
MDPs. We design efficient pseudo-antichain based symblicit algorithms (with
open source implementations) for two quantitative settings: the expected
mean-payoff and the stochastic shortest path. For two practical applications
coming from automated planning and LTL synthesis, we report promising
experimental results w.r.t. both the run time and the memory consumption.Comment: In Proceedings SYNT 2014, arXiv:1407.493
Formalising the pi-calculus using nominal logic
We formalise the pi-calculus using the nominal datatype package, based on
ideas from the nominal logic by Pitts et al., and demonstrate an implementation
in Isabelle/HOL. The purpose is to derive powerful induction rules for the
semantics in order to conduct machine checkable proofs, closely following the
intuitive arguments found in manual proofs. In this way we have covered many of
the standard theorems of bisimulation equivalence and congruence, both late and
early, and both strong and weak in a uniform manner. We thus provide one of the
most extensive formalisations of a process calculus ever done inside a theorem
prover.
A significant gain in our formulation is that agents are identified up to
alpha-equivalence, thereby greatly reducing the arguments about bound names.
This is a normal strategy for manual proofs about the pi-calculus, but that
kind of hand waving has previously been difficult to incorporate smoothly in an
interactive theorem prover. We show how the nominal logic formalism and its
support in Isabelle accomplishes this and thus significantly reduces the tedium
of conducting completely formal proofs. This improves on previous work using
weak higher order abstract syntax since we do not need extra assumptions to
filter out exotic terms and can keep all arguments within a familiar
first-order logic.Comment: 36 pages, 3 figure
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