
T. Mancini. "Now or Never: Negotiating Efficiently with Unknown or Untrusted Counterparts." Fundamenta Informaticae 149, no. 12 (2016): 61–100. DOI: 10.3233/FI20161443.



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/FI2011397.



T. Mancini, E. Tronci, A. Scialanca, F. Lanciotti, A. Finzi, R. Guarneri, and S. Di Pompeo. "Optimal FaultTolerant Placement of Relay Nodes in a Mission Critical Wireless Network." In 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018)., 2018. DOI: 10.29007/grw9.



T. Mancini, F. Mari, A. Massini, I. Melatti, I. Salvo, S. Sinisi, E. Tronci, R. Ehrig, S. RÃ¶blitz, and B. Leeners. "Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction." In 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018)., 2018. DOI: 10.29007/g864.



Verzino Giovanni, Federico Cavaliere, Federico Mari, Igor Melatti, Giovanni Minei, Ivano Salvo, Yuri Yushtein, and Enrico Tronci. "Model checking driven simulation of sat procedures." In Proceedings of 12th International Conference on Space Operations (SpaceOps 2012)., 2012. DOI: 10.2514/6.20121275611.



Vadim Alimguzhin, Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "On Model Based Synthesis of Embedded Control Software." In Proceedings of the 12th International Conference on Embedded Software, EMSOFT 2012, part of the Eighth Embedded Systems Week, ESWeek 2012, Tampere, Finland, October 712, 2012, edited by Ahmed Jerraya and Luca P. Carloni and Florence Maraninchi and John Regehr, 227–236. ACM, 2012. ISBN: 9781450314251. Notes: Techreport version can be found at arxiv.org. DOI: 10.1145/2380356.2380398.



T. Mancini, A. Massini, and E. Tronci. "Parallelization of CycleBased Logic Simulation." Parallel Processing Letters 27, no. 02 (2017). DOI: 10.1142/S0129626417500037.



Adolfo Piperno, and Enrico Tronci. "Regular Systems of Equations in λcalculus." Int. J. Found. Comput. Sci. 1, no. 3 (1990): 325–340. DOI: 10.1142/S0129054190000230.
Abstract: Many problems arising in equational theories like Lambdacalculus and Combinatory Logic can be expressed by combinatory equations or systems of equations. However, the solvability problem for an arbitrarily given class of systems is in general undecidable. In this paper we shall focus our attention on a decidable class of systems, which will be called regular systems, and we shall analyse some classical problems and wellknown properties of Lambdacalculus that can be described and solved by means of regular systems. The significance of such class will be emphasized showing that for slight extensions of it the solvability problem turns out to be undecidable.



Adolfo Piperno, and Enrico Tronci. "Regular Systems of Equations in λcalculus." In Ictcs. Mantova  Italy, 1989. DOI: 10.1142/S0129054190000230.
Abstract: Many problems arising in equational theories like Lambdacalculus and Combinatory Logic can be expressed by combinatory equations or systems of equations. However, the solvability problem for an arbitrarily given class of systems is in general undecidable. In this paper we shall focus our attention on a decidable class of systems, which will be called regular systems, and we shall analyse some classical problems and wellknown properties of Lambdacalculus that can be described and solved by means of regular systems. The significance of such class will be emphasized showing that for slight extensions of it the solvability problem turns out to be undecidable.



T. Mancini, I. Melatti, and E. Tronci. "Anyhorizon uniform random sampling and enumeration of constrained scenarios for simulationbased formal verification." IEEE Transactions on Software Engineering (2021): 1. ISSN: 19393520. Notes: To appear. DOI: 10.1109/TSE.2021.3109842.
Abstract: Modelbased approaches to the verification of nonterminating CyberPhysical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding </i>vacuity</i> in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulationbased statistical model checking. Unfortunately, in case of nontrivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator) can be synthesised, from which anyhorizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulationbased approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking.

