820 research outputs found
Abstractions and sensor design in partial-information, reactive controller synthesis
Automated synthesis of reactive control protocols from temporal logic
specifications has recently attracted considerable attention in various
applications in, for example, robotic motion planning, network management, and
hardware design. An implicit and often unrealistic assumption in this past work
is the availability of complete and precise sensing information during the
execution of the controllers. In this paper, we use an abstraction procedure
for systems with partial observation and propose a formalism to investigate
effects of limitations in sensing. The abstraction procedure enables the
existing synthesis methods with partial observation to be applicable and
efficient for systems with infinite (or finite but large number of) states.
This formalism enables us to systematically discover sensing modalities
necessary in order to render the underlying synthesis problems feasible. We use
counterexamples, which witness unrealizability potentially due to the
limitations in sensing and the coarseness in the abstract system, and
interpolation-based techniques to refine the model and the sensing modalities,
i.e., to identify new sensors to be included, in such synthesis problems. We
demonstrate the method on examples from robotic motion planning.Comment: 9 pages, 4 figures, Accepted at American Control Conference 201
Sound and Automated Synthesis of Digital Stabilizing Controllers for Continuous Plants
Modern control is implemented with digital microcontrollers, embedded within
a dynamical plant that represents physical components. We present a new
algorithm based on counter-example guided inductive synthesis that automates
the design of digital controllers that are correct by construction. The
synthesis result is sound with respect to the complete range of approximations,
including time discretization, quantization effects, and finite-precision
arithmetic and its rounding errors. We have implemented our new algorithm in a
tool called DSSynth, and are able to automatically generate stable controllers
for a set of intricate plant models taken from the literature within minutes.Comment: 10 page
Automatic verification of multi-threaded programs by inference of rely-guarantee specifications
Ministry of Education, Singapore under its Academic Research Funding Tier 2; National Research Foundation (NRF) Singapor
Abstracting Asynchronous Multi-Valued Networks: An Initial Investigation
Multi-valued networks provide a simple yet expressive qualitative state based
modelling approach for biological systems. In this paper we develop an
abstraction theory for asynchronous multi-valued network models that allows the
state space of a model to be reduced while preserving key properties of the
model. The abstraction theory therefore provides a mechanism for coping with
the state space explosion problem and supports the analysis and comparison of
multi-valued networks. We take as our starting point the abstraction theory for
synchronous multi-valued networks which is based on the finite set of traces
that represent the behaviour of such a model. The problem with extending this
approach to the asynchronous case is that we can now have an infinite set of
traces associated with a model making a simple trace inclusion test infeasible.
To address this we develop a decision procedure for checking asynchronous
abstractions based on using the finite state graph of an asynchronous
multi-valued network to reason about its trace semantics. We illustrate the
abstraction techniques developed by considering a detailed case study based on
a multi-valued network model of the regulation of tryptophan biosynthesis in
Escherichia coli.Comment: Presented at MeCBIC 201
On the Trade-off Between Efficiency and Precision of Neural Abstraction
Neural abstractions have been recently introduced as formal approximations of
complex, nonlinear dynamical models. They comprise a neural ODE and a certified
upper bound on the error between the abstract neural network and the concrete
dynamical model. So far neural abstractions have exclusively been obtained as
neural networks consisting entirely of activation functions, resulting
in neural ODE models that have piecewise affine dynamics, and which can be
equivalently interpreted as linear hybrid automata. In this work, we observe
that the utility of an abstraction depends on its use: some scenarios might
require coarse abstractions that are easier to analyse, whereas others might
require more complex, refined abstractions. We therefore consider neural
abstractions of alternative shapes, namely either piecewise constant or
nonlinear non-polynomial (specifically, obtained via sigmoidal activations). We
employ formal inductive synthesis procedures to generate neural abstractions
that result in dynamical models with these semantics. Empirically, we
demonstrate the trade-off that these different neural abstraction templates
have vis-a-vis their precision and synthesis time, as well as the time required
for their safety verification (done via reachability computation). We improve
existing synthesis techniques to enable abstraction of higher-dimensional
models, and additionally discuss the abstraction of complex neural ODEs to
improve the efficiency of reachability analysis for these models.Comment: To appear at QEST 202
Computer Aided Verification
This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book
An abstraction-refinement methodology for reasoning about network games
Network games (NGs) are played on directed graphs and are extensively used in network design and analysis. Search problems for NGs include finding special strategy profiles such as a Nash equilibrium and a globally-optimal solution. The networks modeled by NGs may be huge. In formal verification, abstraction has proven to be an extremely effective technique for reasoning about systems with big and even infinite state spaces. We describe an abstraction-refinement methodology for reasoning about NGs. Our methodology is based on an abstraction function that maps the state space of an NG to a much smaller state space. We search for a global optimum and a Nash equilibrium by reasoning on an under- and an over-approximation defined on top of this smaller state space. When the approximations are too coarse to find such profiles, we refine the abstraction function. We extend the abstraction-refinement methodology to labeled networks, where the objectives of the players are regular languages. Our experimental results demonstrate the effectiveness of the methodology
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