1,900 research outputs found
Compositionality for Quantitative Specifications
We provide a framework for compositional and iterative design and
verification of systems with quantitative information, such as rewards, time or
energy. It is based on disjunctive modal transition systems where we allow
actions to bear various types of quantitative information. Throughout the
design process the actions can be further refined and the information made more
precise. We show how to compute the results of standard operations on the
systems, including the quotient (residual), which has not been previously
considered for quantitative non-deterministic systems. Our quantitative
framework has close connections to the modal nu-calculus and is compositional
with respect to general notions of distances between systems and the standard
operations
Compositionality for Quantitative Specifications
We provide a framework for compositional and iterative design and verification of systems with quantitative information, such as rewards, time or energy. It is based on disjunctive modal transition systems where we allow actions to bear various types of quantitative information. Throughout the design process the actions can be further refined and the information made more precise. We show how to compute the results of standard operations on the systems, including the quotient (residual), which has not been previously considered for quantitative non-deterministic systems. Our quantitative framework has close connections to the modal nu-calculus and is compositional with respect to general notions of distances between systems and the standard operations
Learning Task Specifications from Demonstrations
Real world applications often naturally decompose into several sub-tasks. In
many settings (e.g., robotics) demonstrations provide a natural way to specify
the sub-tasks. However, most methods for learning from demonstrations either do
not provide guarantees that the artifacts learned for the sub-tasks can be
safely recombined or limit the types of composition available. Motivated by
this deficit, we consider the problem of inferring Boolean non-Markovian
rewards (also known as logical trace properties or specifications) from
demonstrations provided by an agent operating in an uncertain, stochastic
environment. Crucially, specifications admit well-defined composition rules
that are typically easy to interpret. In this paper, we formulate the
specification inference task as a maximum a posteriori (MAP) probability
inference problem, apply the principle of maximum entropy to derive an analytic
demonstration likelihood model and give an efficient approach to search for the
most likely specification in a large candidate pool of specifications. In our
experiments, we demonstrate how learning specifications can help avoid common
problems that often arise due to ad-hoc reward composition.Comment: NIPS 201
Structural Refinement for the Modal nu-Calculus
We introduce a new notion of structural refinement, a sound abstraction of
logical implication, for the modal nu-calculus. Using new translations between
the modal nu-calculus and disjunctive modal transition systems, we show that
these two specification formalisms are structurally equivalent.
Using our translations, we also transfer the structural operations of
composition and quotient from disjunctive modal transition systems to the modal
nu-calculus. This shows that the modal nu-calculus supports composition and
decomposition of specifications.Comment: Accepted at ICTAC 201
Compositional bisimulation metric reasoning with Probabilistic Process Calculi
We study which standard operators of probabilistic process calculi allow for
compositional reasoning with respect to bisimulation metric semantics. We argue
that uniform continuity (generalizing the earlier proposed property of
non-expansiveness) captures the essential nature of compositional reasoning and
allows now also to reason compositionally about recursive processes. We
characterize the distance between probabilistic processes composed by standard
process algebra operators. Combining these results, we demonstrate how
compositional reasoning about systems specified by continuous process algebra
operators allows for metric assume-guarantee like performance validation
A temporal logic approach to modular design of synthetic biological circuits
We present a new approach for the design of a synthetic biological circuit
whose behaviour is specified in terms of signal temporal logic (STL) formulae.
We first show how to characterise with STL formulae the input/output behaviour
of biological modules miming the classical logical gates (AND, NOT, OR). Hence,
we provide the regions of the parameter space for which these specifications
are satisfied. Given a STL specification of the target circuit to be designed
and the networks of its constituent components, we propose a methodology to
constrain the behaviour of each module, then identifying the subset of the
parameter space in which those constraints are satisfied, providing also a
measure of the robustness for the target circuit design. This approach, which
leverages recent results on the quantitative semantics of Signal Temporal
Logic, is illustrated by synthesising a biological implementation of an
half-adder
Hidden-Markov Program Algebra with iteration
We use Hidden Markov Models to motivate a quantitative compositional
semantics for noninterference-based security with iteration, including a
refinement- or "implements" relation that compares two programs with respect to
their information leakage; and we propose a program algebra for source-level
reasoning about such programs, in particular as a means of establishing that an
"implementation" program leaks no more than its "specification" program.
This joins two themes: we extend our earlier work, having iteration but only
qualitative, by making it quantitative; and we extend our earlier quantitative
work by including iteration. We advocate stepwise refinement and
source-level program algebra, both as conceptual reasoning tools and as targets
for automated assistance. A selection of algebraic laws is given to support
this view in the case of quantitative noninterference; and it is demonstrated
on a simple iterated password-guessing attack
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