
Giuseppe Della Penna, Daniele Magazzeni, Alberto Tofani, Benedetto Intrigila, Igor Melatti, and Enrico Tronci. "Automated Generation of Optimal Controllers through Model Checking Techniques." In IcincoIcso, edited by J. AndradeCetto, J.  L. Ferrier, J. M. C. D. Pereira and J. Filipe, 26–33. INSTICC Press, 2006. ISSN: 9728865597. DOI: 10.1007/9783540791423.
Abstract: We present a methodology for the synthesis of controllers, which exploits (explicit) model checking techniques. That is, we can cope with the systematic exploration of a very large state space. This methodology can be applied to systems where other approaches fail. In particular, we can consider systems with an highly nonlinear dynamics and lacking a uniform mathematical description (model). We can also consider situations where the required control action cannot be specified as a local action, and rather a kind of planning is required. Our methodology individuates first a raw optimal controller, then extends it to obtain a more robust one. A case study is presented which considers the well known trucktrailer obstacle avoidance parking problem, in a parking lot with obstacles on it. The complex nonlinear dynamics of the trucktrailer system, within the presence of obstacles, makes the parking problem extremely hard. We show how, by our methodology, we can obtain optimal controllers with different degrees of robustness.



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.



V. Alimguzhin, F. Mari, I. Melatti, I. Salvo, and E. Tronci. "Linearising Discrete Time Hybrid Systems." IEEE Transactions on Automatic Control 62, no. 10 (2017): 5357–5364. ISSN: 00189286. DOI: 10.1109/TAC.2017.2694559.
Abstract: Model Based Design approaches for embedded systems aim at generating correctbyconstruction control software, guaranteeing that the closed loop system (controller and plant) meets given system level formal specifications. This technical note addresses control synthesis for safety and reachability properties of possibly nonlinear discrete time hybrid systems. By means of syntactical transformations that require nonlinear terms to be Lipschitz continuous functions, we overapproximate nonlinear dynamics with a linear system whose controllers are guaranteed to be controllers of the original system. We evaluate performance of our approach on meaningful control synthesis benchmarks, also comparing it to a stateoftheart tool.



I. Melatti, F. Mari, T. Mancini, M. Prodanovic, and E. Tronci. "A TwoLayer NearOptimal Strategy for Substation Constraint Management via Home Batteries." IEEE Transactions on Industrial Electronics (2021): 1. Notes: To appear. DOI: 10.1109/TIE.2021.3102431.
Abstract: Within electrical distribution networks, substation constraints management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation constraints management can be measured as the reduction of constraints violations w.r.t. unmanaged demand. Home batteries hold the promise of enabling efficient and useroblivious substation constraints management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. We present a novel twolayer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields nearoptimal substation constraints management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraints management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy.



B. P. Hayes, I. Melatti, T. Mancini, M. Prodanovic, and E. Tronci. "Residential Demand Management using Individualised Demand Aware Price Policies." IEEE Transactions On Smart Grid 8, no. 3 (2017): 1284–1294. DOI: 10.1109/TSG.2016.2596790.



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.



Amedeo Cesta, Alberto Finzi, Simone Fratini, Andrea Orlandini, and Enrico Tronci. "Validation and Verification Issues in a Timelinebased Planning System." In In EProc. of ICAPS Workshop on Knowledge Engineering for Planning and Scheduling., 2008.
Abstract: One of the key points to take into account to foster effective introduction of AI planning and scheduling systems in real world is to develop end user trust in the related technologies. Automated planning and scheduling systems often brings solutions to the users which are neither Ã¢â‚¬Å“obviousÃ¢â‚¬Âť nor immediately acceptable for them. This is due to the ability of these tools to take into account quite an amount of temporal and causal constraints and to employ resolution processes often designed to optimize the solution with respect to non trivial evaluation functions. To increase technology trust, the study of tools for verifying and validating plans and schedules produced by AI systems might be instrumental. In general, validation and verification techniques represent a needed complementary technology in developing domain independent architectures for automated problem solving. This paper presents a preliminary report of the issues concerned with the use of two software tools for formal verification of finite state systems to the validation of the solutions produced by MrSPOCK, a recent effort for building a timeline based planning tool in an ESA project.



Amedeo Cesta, Alberto Finzi, Simone Fratini, Andrea Orlandini, and Enrico Tronci. "Merging Planning, Scheduling & Verification – A Preliminary Analysis." In In Proc. of 10th ESA Workshop on Advanced Space Technologies for Robotics and Automation (ASTRA)., 2008.



Toni Mancini, Federico Mari, Annalisa Massini, Igor Melatti, and Enrico Tronci. "Anytime System Level Verification via Random Exhaustive Hardware In The Loop Simulation." In In Proceedings of 17th EuroMicro Conference on Digital System Design (DSD 2014)., 2014. DOI: 10.1109/DSD.2014.91.



T. Mancini. "Now or Never: negotiating efficiently with unknown counterparts." In proceedings of the 22nd RCRA International Workshop. Ferrara, Italy. CEUR, 2015 (Colocated with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)). (2015).

