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Della Penna, Giuseppe; Magazzeni, Daniele; Tofani, Alberto; Intrigila, Benedetto; Melatti, Igor; Tronci, Enrico |
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Automated Generation Of Optimal Controllers Through Model Checking Techniques |
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2008 |
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Informatics in Control Automation and Robotics. Selected Papers from ICINCO 2006 |
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107-119 |
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yes |
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Sapienza @ mari @ Dmtmt08 |
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26 |
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Böhm, Corrado; Tronci, Enrico |
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About Systems of Equations, X-Separability, and Left-Invertibility in the lambda-Calculus |
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1991 |
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Inf. Comput. |
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90 |
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1 |
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1-32 |
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Sapienza @ mari @ infcomp91 |
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59 |
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Mazzini, Silvia; Puri, Stefano; Mari, Federico; Melatti, Igor; Tronci, Enrico |
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Title |
Formal Verification at System Level |
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2009 |
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In: DAta Systems In Aerospace (DASIA), Org. EuroSpace, Canadian Space Agency, CNES, ESA, EUMETSAT. Instanbul, Turkey, EuroSpace |
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System Level Analysis calls for a language comprehensible to experts with different background and yet precise enough to support meaningful analyses. SysML is emerging as an effective balance between such conflicting goals. In this paper we outline some the results obtained as for SysML based system level functional formal verification by an ESA/ESTEC study, with a collaboration among INTECS and La Sapienza University of Roma. The study focuses on SysML based system level functional requirements techniques. |
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Sapienza @ mari @ Dasia09 |
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20 |
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Mancini, T. |
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Now or Never: negotiating efficiently with unknown counterparts |
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2015 |
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In proceedings of the 22nd RCRA International Workshop. Ferrara, Italy. CEUR, 2015 (Co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)). |
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Sapienza @ preissler @ Mancini2015 |
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131 |
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Mancini, Toni; Mari, Federico; Massini, Annalisa; Melatti, Igor; Tronci, Enrico |
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Title |
Anytime System Level Verification via Random Exhaustive Hardware In The Loop Simulation |
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2014 |
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In Proceedings of 17th EuroMicro Conference on Digital System Design (DSD 2014) |
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MCLab @ davi @ |
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122 |
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Cesta, Amedeo; Finzi, Alberto; Fratini, Simone; Orlandini, Andrea; Tronci, Enrico |
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Title |
Merging Planning, Scheduling & Verification – A Preliminary Analysis |
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Conference Article |
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2008 |
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In Proc. of 10th ESA Workshop on Advanced Space Technologies for Robotics and Automation (ASTRA) |
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yes |
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Sapienza @ mari @ Astra08 |
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24 |
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Cesta, Amedeo; Finzi, Alberto; Fratini, Simone; Orlandini, Andrea; Tronci, Enrico |
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Title |
Validation and Verification Issues in a Timeline-based Planning System |
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Conference Article |
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2008 |
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In E-Proc. of ICAPS Workshop on Knowledge Engineering for Planning and Scheduling |
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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 “obvious†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. |
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yes |
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Sapienza @ mari @ Keps08 |
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25 |
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Author |
Mancini, T.; Melatti, I.; Tronci, E. |
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Title |
Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification |
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2021 |
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IEEE Transactions on Software Engineering |
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1-1 |
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Model-based approaches to the verification of non-terminating Cyber-Physical 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 simulation-based statistical model checking. Unfortunately, in case of non-trivial 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 any-horizon 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 simulation-based 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. |
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1939-3520 |
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To appear |
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MCLab @ davi @ ref9527998 |
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191 |
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Hayes, B. P. ; Melatti, I.; Mancini, T.; Prodanovic, M.; Tronci, E. |
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Title |
Residential Demand Management using Individualised Demand Aware Price Policies |
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2017 |
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IEEE Transactions On Smart Grid |
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8 |
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3 |
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1284-1294 |
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MCLab @ davi @ |
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157 |
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Melatti, I.; Mari, F.; Mancini, T.; Prodanovic, M.; Tronci, E. |
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A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries |
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2021 |
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IEEE Transactions on Industrial Electronics |
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1-1 |
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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 user-oblivious 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 two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields near-optimal 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. |
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MCLab @ davi @ ref9513535 |
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190 |
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