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Register a new userTowards Probabilistic Formal Modeling of Robotic Cell Injection Systems
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Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Cell injection is a technique in the domain of biological cell micro-manipulation for the delivery of small volumes of samples into the suspended or adherent cells. It has been widely applied in various areas, such as gene injection, in-vitro fertilization (IVF), intracytoplasmic sperm injection (ISCI) and drug development. However, the existing manual and semi-automated cell injection systems require lengthy training and suffer from high probability of contamination and low success rate. In the recently introduced fully automated cell injection systems, the injection force plays a vital role in the success of the process since even a tiny excessive force can destroy the membrane or tissue of the biological cell. Traditionally, the force control algorithms are analyzed using simulation, which is inherently non-exhaustive and incomplete in terms of detecting system failures. Moreover, the uncertainties in the system are generally ignored in the analysis. To overcome these limitations, we present a formal analysis methodology based on probabilistic model checking to analyze a robotic cell injection system utilizing the impedance force control algorithm. The proposed methodology, developed using the PRISM model checker, allowed to find a discrepancy in the algorithm, which was not found by any of the previous analysis using the traditional methods.
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This volume contains the proceedings of MARS 2017, the second workshop on Models for Formal Analysis of Real Systems, held on April 29, 2017 in Uppala, Sweden, as an affiliated workshop of ETAPS 2017, the European Joint Conferences on Theory and Practice of Software. The workshop emphasises modelling over verification. It aims at discussing the lessons learned from making formal methods for the verification and analysis of realistic systems. Examples are: (1) Which formalism is chosen, and why? (2) Which abstractions have to be made and why? (3) How are important characteristics of the system modelled? (4) Were there any complications while modelling the system? (5) Which measures were taken to guarantee the accuracy of the model? We invited papers that present full models of real systems, which may lay the basis for future comparison and analysis. An aim of the workshop is to present different modelling approaches and discuss pros and cons for each of them. Alternative formal descriptions of the systems presented at this workshop are encouraged, which should foster the development of improved specification formalisms.
In modern cloud-based architectures, containers play a central role: they provide powerful isolation mechanisms such that developers can focus on the logic and dependencies of applications while system administrators can focus on deployment and management issue. In this work, we propose a formal model for container-based systems, using the framework of Bigraphical Reactive Systems (BRSs). We first introduce local directed bigraphs, a graph-based formalism which allows us to deal with localized resources. Then, we define a signature for modelling containers and provide some examples of bigraphs modelling containers. These graphs can be analysed and manipulated using techniques from graph theory: properties about containers can be formalized as properties of the corresponding bigraphic representations. Moreover, it turns out that the composition of containers as performed by e.g. docker-compose, corresponds precisely to the composition of the corresponding bigraphs inside an ``environment bigraph which in turn is obtained directly from the YAML file used to define the composition of containers.
Software engineering of modular robotic systems is a challenging task, however, verifying that the developed components all behave as they should individually and as a whole presents its own unique set of challenges. In particular, distinct components in a modular robotic system often require different verification techniques to ensure that they behave as expected. Ensuring whole system consistency when individual components are verified using a variety of techniques and formalisms is difficult. This paper discusses how to use compositional verification to integrate the various verification techniques that are applied to modular robotic software, using a First-Order Logic (FOL) contract that captures each components assumptions and guarantees. These contracts can then be used to guide the verification of the individual components, be it by testing or the use of a formal method. We provide an illustrative example of an autonomous robot used in remote inspection. We also discuss a way of defining confidence for the verification associated with each component.
Trust and reputation models for distributed, collaborative systems have been studied and applied in several domains, in order to stimulate cooperation while preventing selfish and malicious behaviors. Nonetheless, such models have received less attention in the process of specifying and analyzing formally the functionalities of the systems mentioned above. The objective of this paper is to define a process algebraic framework for the modeling of systems that use (i) trust and reputation to govern the interactions among nodes, and (ii) communication models characterized by a high level of adaptiveness and flexibility. Hence, we propose a formalism for verifying, through model checking techniques, the robustness of these systems with respect to the typical attacks conducted against webs of trust.
Many systems are naturally modeled as Markov Decision Processes (MDPs), combining probabilities and strategic actions. Given a model of a system as an MDP and some logical specification of system behavior, the goal of synthesis is to find a policy that maximizes the probability of achieving this behavior. A popular choice for defining behaviors is Linear Temporal Logic (LTL). Policy synthesis on MDPs for properties specified in LTL has been well studied. LTL, however, is defined over infinite traces, while many properties of interest are inherently finite. Linear Temporal Logic over finite traces (LTLf) has been used to express such properties, but no tools exist to solve policy synthesis for MDP behaviors given finite-trace properties. We present two algorithms for solving this synthesis problem: the first via reduction of LTLf to LTL and the second using native tools for LTLf. We compare the scalability of these two approaches for synthesis and show that the native approach offers better scalability compared to existing automaton generation tools for LTL.
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