4,579 research outputs found
Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
This paper presents a novel approach for augmenting proof-based verification
with performance-style analysis of the kind employed in state-of-the-art model
checking tools for probabilistic systems. Quantitative safety properties
usually specified as probabilistic system invariants and modeled in proof-based
environments are evaluated using bounded model checking techniques.
Our specific contributions include the statement of a theorem that is central
to model checking safety properties of proof-based systems, the establishment
of a procedure; and its full implementation in a prototype system (YAGA) which
readily transforms a probabilistic model specified in a proof-based environment
to its equivalent verifiable PRISM model equipped with reward structures. The
reward structures capture the exact interpretation of the probabilistic
invariants and can reveal succinct information about the model during
experimental investigations. Finally, we demonstrate the novelty of the
technique on a probabilistic library case study
Quantitative Behavioural Reasoning for Higher-order Effectful Programs: Applicative Distances (Extended Version)
This paper studies the quantitative refinements of Abramsky's applicative
similarity and bisimilarity in the context of a generalisation of Fuzz, a
call-by-value -calculus with a linear type system that can express
programs sensitivity, enriched with algebraic operations \emph{\`a la} Plotkin
and Power. To do so a general, abstract framework for studying behavioural
relations taking values over quantales is defined according to Lawvere's
analysis of generalised metric spaces. Barr's notion of relator (or lax
extension) is then extended to quantale-valued relations adapting and extending
results from the field of monoidal topology. Abstract notions of
quantale-valued effectful applicative similarity and bisimilarity are then
defined and proved to be a compatible generalised metric (in the sense of
Lawvere) and pseudometric, respectively, under mild conditions
On the Implementation of the Probabilistic Logic Programming Language ProbLog
The past few years have seen a surge of interest in the field of
probabilistic logic learning and statistical relational learning. In this
endeavor, many probabilistic logics have been developed. ProbLog is a recent
probabilistic extension of Prolog motivated by the mining of large biological
networks. In ProbLog, facts can be labeled with probabilities. These facts are
treated as mutually independent random variables that indicate whether these
facts belong to a randomly sampled program. Different kinds of queries can be
posed to ProbLog programs. We introduce algorithms that allow the efficient
execution of these queries, discuss their implementation on top of the
YAP-Prolog system, and evaluate their performance in the context of large
networks of biological entities.Comment: 28 pages; To appear in Theory and Practice of Logic Programming
(TPLP
An Internal Language for Categories Enriched over Generalised Metric Spaces
Programs with a continuous state space or that interact with physical processes often require notions of equivalence going beyond the standard binary setting in which equivalence either holds or does not hold. In this paper we explore the idea of equivalence taking values in a quantale ?, which covers the cases of (in)equations and (ultra)metric equations among others.
Our main result is the introduction of a ?-equational deductive system for linear ?-calculus together with a proof that it is sound and complete (in fact, an internal language) for a class of enriched autonomous categories. In the case of inequations, we get an internal language for autonomous categories enriched over partial orders. In the case of (ultra)metric equations, we get an internal language for autonomous categories enriched over (ultra)metric spaces.
We use our results to obtain examples of inequational and metric equational systems for higher-order programs that contain real-time and probabilistic behaviour
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