Giuseppe Della Penna, Benedetto Intrigila, Igor Melatti, Michele Minichino, Ester Ciancamerla, Andrea Parisse, Enrico Tronci, and Marisa Venturini Zilli. "Automatic Verification of a Turbogas Control System with the Mur$\varphi$ Verifier." In Hybrid Systems: Computation and Control, 6th International Workshop, HSCC 2003 Prague, Czech Republic, April 3-5, 2003, Proceedings, edited by O. Maler and A. Pnueli, 141–155. Lecture Notes in Computer Science 2623. Springer, 2003. ISSN: 3-540-00913-2. DOI: 10.1007/3-540-36580-X.
Abstract: Automatic analysis of Hybrid Systems poses formidable challenges both from a modeling as well as from a verification point of view. We present a case study on automatic verification of a Turbogas Control System (TCS) using an extended version of the Mur$\varphi$ verifier. TCS is the heart of ICARO, a 2MW Co-generative Electric Power Plant. For large hybrid systems, as TCS is, the modeling effort accounts for a significant part of the whole verification activity. In order to ease our modeling effort we extended the Mur$\varphi$ verifier by importing the C language long double type (finite precision real numbers) into it. We give experimental results on running our extended Mur$\varphi$ on our TCS model. For example using Mur$\varphi$ we were able to compute an admissible range of values for the variation speed of the user demand of electric power to the turbogas.
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L. Tortora, G. Meynen, J. Bijlsma, E. Tronci, and S. Ferracuti. "Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective." Frontiers in Psychology 11 (2020): 220. ISSN: 1664-1078. DOI: 10.3389/fpsyg.2020.00220.
Abstract: Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.
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T. Mancini, F. Mari, A. Massini, I. Melatti, and E. Tronci. "Anytime system level verification via parallel random exhaustive hardware in the loop simulation." Microprocessors and Microsystems 41 (2016): 12–28. ISSN: 0141-9331. DOI: 10.1016/j.micpro.2015.10.010.
Abstract: Abstract System level verification of cyber-physical systems has the goal of verifying that the whole (i.e., software + hardware) system meets the given specifications. Model checkers for hybrid systems cannot handle system level verification of actual systems. Thus, Hardware In the Loop Simulation (HILS) is currently the main workhorse for system level verification. By using model checking driven exhaustive HILS, System Level Formal Verification (SLFV) can be effectively carried out for actual systems. We present a parallel random exhaustive HILS based model checker for hybrid systems that, by simulating all operational scenarios exactly once in a uniform random order, is able to provide, at any time during the verification process, an upper bound to the probability that the System Under Verification exhibits an error in a yet-to-be-simulated scenario (Omission Probability). We show effectiveness of the proposed approach by presenting experimental results on SLFV of the Inverted Pendulum on a Cart and the Fuel Control System examples in the Simulink distribution. To the best of our knowledge, no previously published model checker can exhaustively verify hybrid systems of such a size and provide at any time an upper bound to the Omission Probability.
Keywords: Model Checking of Hybrid Systems; Model checking driven simulation; Hardware in the loop simulation
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G. Dipoppa, G. D'Alessandro, R. Semprini, and E. Tronci. "Integrating Automatic Verification of Safety Requirements in Railway Interlocking System Design." In High Assurance Systems Engineering, 2001. Sixth IEEE International Symposium on, 209–219. Albuquerque, NM, USA: IEEE Computer Society, 2001. ISSN: 0-7695-1275-5. DOI: 10.1109/HASE.2001.966821.
Abstract: A railway interlocking system (RIS) is an embedded system (namely a supervisory control system) that ensures the safe, operation of the devices in a railway station. RIS is a safety critical system. We explore the possibility of integrating automatic formal verification methods in a given industry RIS design flow. The main obstructions to be overcome in our work are: selecting a formal verification tool that is efficient enough to solve the verification problems at hand; and devising a cost effective integration strategy for such tool. We were able to devise a successful integration strategy meeting the above constraints without requiring major modification in the pre-existent design flow nor retraining of personnel. We run verification experiments for a RIS designed for the Singapore Subway. The experiments show that the RIS design flow obtained from our integration strategy is able to automatically verify real life RIS designs.
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Giuseppe Della Penna, Daniele Magazzeni, Alberto Tofani, Benedetto Intrigila, Igor Melatti, and Enrico Tronci. "Automatic Synthesis of Robust Numerical Controllers." In Icas '07, 4. IEEE Computer Society, 2007. ISSN: 0-7695-2859-5. DOI: 10.1109/CONIELECOMP.2007.59.
Abstract: A major problem of numerical controllers is their robustness, i.e. the state read from the plant may not be in the controller table, although it may be close to some states in the table. For continuous systems, this problem is typically handled by interpolation techniques. Unfortunately, when the plant contains both continuous and discrete variables, the interpolation approach does not work well. To cope with this kind of systems, we propose a general methodology that exploits explicit model checking in an innovative way to automatically synthesize a (time-) optimal numerical controller from a plant specification and apply an optimized strengthening algorithm only on the most significant states, in order to reach an acceptable robustness degree. We implemented all the algorithms within our CGMurphi tool, an extension of the well-known CMurphi verifier, and tested the effectiveness of our approach by applying it to the well-known truck and trailer obstacles avoidance problem.
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S. Sinisi, V. Alimguzhin, T. Mancini, and E. Tronci. "Reconciling interoperability with efficient Verification and Validation within open source simulation environments." Simulation Modelling Practice and Theory (2021): 102277. ISSN: 1569-190x. DOI: 10.1016/j.simpat.2021.102277.
Abstract: A Cyber-Physical System (CPS) comprises physical as well as software subsystems. Simulation-based approaches are typically used to support design and Verification and Validation (V&V) of CPSs in several domains such as: aerospace, defence, automotive, smart grid and healthcare. Accordingly, many simulation-based tools are available to support CPS design. This, on one side, enables designers to choose the toolchain that best suits their needs, on the other side poses huge interoperability challenges when one needs to simulate CPSs whose subsystems have been designed and modelled using different toolchains. To overcome such an interoperability problem, in 2010 the Functional Mock-up Interface (FMI) has been proposed as an open standard to support both Model Exchange (ME) and Co-Simulation (CS) of simulation models created with different toolchains. FMI has been adopted by several modelling and simulation environments. Models adhering to such a standard are called Functional Mock-up Units (FMUs). Indeed FMUs play an essential role in defining complex CPSs through, e.g., the System Structure and Parametrization (SSP) standard. Simulation-based V&V of CPSs typically requires exploring different simulation scenarios (i.e., exogenous input sequences to the CPS under design). Many such scenarios have a shared prefix. Accordingly, to avoid simulating many times such shared prefixes, the simulator state at the end of a shared prefix is saved and then restored and used as a start state for the simulation of the next scenario. In this context, an important FMI feature is the capability to save and restore the internal FMU state on demand. This is crucial to increase efficiency of simulation-based V&V. Unfortunately, the implementation of this feature is not mandatory and it is available only within some commercial software. As a result, the interoperability enabled by the FMI standard cannot be fully exploited for V&V when using open-source simulation environments. This motivates developing such a feature for open-source CPS simulation environments. Accordingly, in this paper, we focus on JModelica, an open-source modelling and simulation environment for CPSs based on an open standard modelling language, namely Modelica. We describe how we have endowed JModelica with our open-source implementation of the FMI 2.0 functions needed to save and restore internal states of FMUs for ME. Furthermore, we present experimental results evaluating, through 934 benchmark models, correctness and efficiency of our extended JModelica. Our experimental results show that simulation-based V&V is, on average, 22 times faster with our get/set functionality than without it.
Keywords: Simulation, Verification and Validation, Interoperability, FMI/FMU, Model Exchange, Cyber-Physical Systems
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Giuseppe Della Penna, Alberto Tofani, Marcello Pecorari, Orazio Raparelli, Benedetto Intrigila, Igor Melatti, and Enrico Tronci. "A Case Study on Automated Generation of Integration Tests." In Fdl, 278–284. Ecsi, 2006. ISSN: 978-3-00-019710-9.
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Amedeo Cesta, Simone Fratini, Andrea Orlandini, Alberto Finzi, and Enrico Tronci. "Flexible Plan Verification: Feasibility Results." Fundamenta Informaticae 107, no. 2 (2011): 111–137. DOI: 10.3233/FI-2011-397.
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Amedeo Cesta, Alberto Finzi, Simone Fratini, Andrea Orlandini, and Enrico Tronci. "Flexible Timeline-Based Plan Verification." In KI 2009: Advances in Artificial Intelligence, 32nd Annual German Conference on AI, Paderborn, Germany, September 15-18, 2009. Proceedings, edited by B. Ã. ¤rbel Mertsching, M. Hund and M. Z. Aziz, 49–56. Lecture Notes in Computer Science 5803. Springer, 2009. ISSN: 978-3-642-04616-2. DOI: 10.1007/978-3-642-04617-9_7.
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Amedeo Cesta, Alberto Finzi, Simone Fratini, Andrea Orlandini, and Enrico Tronci. "Flexible Plan Verification: Feasibility Results." In 16th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion” (RCRA). Proceedings., 2009.
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