Enrico Giunchiglia

Università degli Studi di Genova, Genova, Liguria, Italy

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Publications (138)22.77 Total impact

  • Emanuele Di Rosa, Enrico Giunchiglia
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    ABSTRACT: The ability to effectively reason in the presence of qualitative preferences on literals or formulas is a central issue in Artificial Intelligence. In the last few years, two procedures have been presented in order to reason with propositional satisfiability (SAT) problems in the presence of additional, partially ordered qualitative preferences on literals or formulas: the first requires a modification of the branching heuristic of the SAT solver in order to guarantee that the first solution is optimal, while the second computes a sequence of solutions, each guaranteed to be better than the previous one. The two approaches have their own advantages and disadvantages and when compared on specific classes of instances – each having an empty partial order – the second seems to have superior performance. In this paper we show that the above two approaches for reasoning with qualitative preferences can be combined yielding a new effective procedure. In particular, in the new procedure we modify the branching heuristic – as in the first approach – by possibly changing the polarity of the returned literal, and then we continue the search – as in the second approach – looking for better solutions. We extended the experimental analysis conducted in previous papers by considering a wide variety of problems, having both an empty and a non-empty partial order: the results show that the new procedure performs better than the two previous approaches on average, and especially on the “hard” problems. As a preliminary result, we show that the framework of qualitative preferences on literals is more general and expressive than the framework on quantitative preferences.
    Ai Communications 01/2013; 26(4):395-408. · 0.45 Impact Factor
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    R.i.C.e.R.c.A: RCRA Incontri E Confronti -- Workshop of the XIII AI*IA Conference; 01/2013
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    ABSTRACT: Business processes under authorization control are sets of coordinated activities subject to a security policy stating which agent can access which resource. Their behavior is difficult to predict due to the complex and unexpected interleaving of different execution flows within the process. Serious flaws may thus go undetected and manifest themselves only after deployment. For this reason, business processes are being considered a new, promising application domain for formal methods and model checking techniques in particular. In this paper we show that action-based languages provide a rich and natural framework for the formal specification of and automated reasoning about business processes under authorization constraints. We do this by discussing the application of the action language C to the specification of a business process from the banking domain that is representative of an important class of business processes of practical relevance. Furthermore we show that a number of reasoning tasks that arise in this context (namely checking whether the control flow together with the security policy meets the expected security properties, building a security policy for the given business process under given security requirements, and finding an allocation of tasks to agents that guarantees the completion of the business process) can be carried out automatically using the Causal Calculator CCalc. We also compare C with the prominent specification language used in model-checking.1
    Journal of Computer and System Sciences. 01/2012;
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    Enrico Giunchiglia, Marco Maratea
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    ABSTRACT: Planning as Satisfiability is one of the most well-known and effective techniques for classical planning: satplan has been the winning system in the deterministic track for optimal planners in the 4th International Planning Competition (IPC) and a cowinner in the 5th IPC. Given a planning problem Π and a makespan n, the approach based on satisfiability (a.k.a. SAT-based) simply works by (i) constructing a SAT formula Π n and (ii) checking Ðn for satisfiability: if there is a model for Π n then we have found a plan, otherwise n is increased. The approach guarantees that the makespan is optimal, i.e. minimum. In this article we extend the Planning as Satisfiability approach in order to handle preferences and satplan in order to solve problems with simple preferences. This allows, e.g. to take into consideration ‘plan quality’ issues other than makespan, like number of actions and ‘soft’ goals. The basic idea is to explore the search space of possible plans in accordance with the given partially ordered preferences.We first prove that, at fixed makespan, our approach returns an ‘optimal’ plan, if any. Then, considering both classical planning problems and problems coming from IPC-5, we show that satplan extended in order to deal with preferences: (i) returns optimal plans that are often of considerable better quality, i.e. with fewer actions or with a better plan metric on soft goals, than satplan; and (ii) is overall competitive, in terms of plan quality, with sgplan, the winning system in the ‘SimplePreferences’ category of the IPC-5. Notably, such results are often obtained without sacrificing efficiency.
    J. Log. Comput. 01/2011; 21:205-229.
  • Emanuele Di Rosa, Enrico Giunchiglia, Barry O'Sullivan
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    ABSTRACT: Satisfiability problems with preferences enrich the expressive power of the Boolean Satisfiability problem (SAT) and facilitate the representation of qualitative/quantitative preferences on literals/formulas, defining an optimization problem. In some cases, it is not strictly necessary to compute an optimal solution, but it is enough to compute a sub-optimal solution of high quality and, possibly, provide a lower bound on the probability of finding an optimal solution. The 1/e - rule is the optimal stopping rule for the secretary problem that guarantees an optimal solution with probability at least 1/e can be found. In this paper: we show how to apply the 1/e-rule for solving satisfiability problems with preferences; we show that its theoretical success rate of about 37% is greater than 90% on random benchmarks; and, we show that the performance of the 1/e-rule on structured benchmarks is sometimes many orders-of-magnitude worse than that of complete search-based algorithms, and we explain the reasons why. We propose an algorithm based on the idea underlying the 1/e-rule, which needs the generation of just two solutions: the experimental evaluation shows that the average success rate of the proposed algorithm is a good approximation of the theoretical one of the 1/e-rule, since it is about 50.92% on 1956 structured problems and 48.33% on 2400 randomly generated instances with 200 variables.
    Proceedings of the 2011 ACM Symposium on Applied Computing (SAC), TaiChung, Taiwan, March 21 - 24, 2011; 01/2011
  • Fundam. Inform. 01/2011; 107:139-166.
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    Emanuele Di Rosa, Enrico Giunchiglia, Marco Maratea
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    ABSTRACT: Propositional satisfiability (SAT) is a success story in Computer Science and Artificial Intelligence: SAT solvers are currently used to solve problems in many different application domains, including planning and formal verification. The main reason for this success is that modern SAT solvers can successfully deal with problems having millions of variables. All these solvers are based on the Davis–Logemann–Loveland procedure (dll). In its original version, dll is a decision procedure, but it can be very easily modified in order to return one or all assignments satisfying the input set of clauses, assuming at least one exists. However, in many cases it is not enough to compute assignments satisfying all the input clauses: Indeed, the returned assignments have also to be “optimal” in some sense, e.g., they have to satisfy as many other constraints—expressed as preferences—as possible. In this paper we start with qualitative preferences on literals, defined as a partially ordered set (poset) of literals. Such a poset induces a poset on total assignments and leads to the definition of optimal model for a formula ψ as a minimal element of the poset on the models of ψ. We show (i) how dll can be extended in order to return one or all optimal models of ψ (once converted in clauses and assuming ψ is satisfiable), and (ii) how the same procedures can be used to compute optimal models wrt a qualitative preference on formulas and/or wrt a quantitative preference on literals or formulas. We implemented our ideas and we tested the resulting system on a variety of very challenging structured benchmarks. The results indicate that our implementation has comparable performances with other state-of-the-art systems, tailored for the specific problems we consider. KeywordsSatisfiability-Preferences
    Constraints 10/2010; 15(4):485-515. · 0.74 Impact Factor
  • Enrico Giunchiglia, Paolo Marin, Massimo Narizzano
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    ABSTRACT: In this paper we present sQueezeBF, an effective preprocessor for QBFs that combines various techniques for eliminating variables and/or redundant clauses. In particular sQueezeBF combines (i) variable elimination via Q-resolution, (ii) variable elimination via equivalence substitution and (iii) equivalence breaking via equivalence rewriting. The experimental analysis shows that sQueezeBF can produce significant reductions in the number of clauses and/or variables - up to the point that some instances are solved directly by sQueezeBF - and that it can significantly improve the efficiency of a range of state-of-the-art QBF solvers - up to the point that some instances cannot be solved without sQueezeBF preprocessing.
    Theory and Applications of Satisfiability Testing - SAT 2010, 13th International Conference, SAT 2010, Edinburgh, UK, July 11-14, 2010. Proceedings; 01/2010
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    ABSTRACT: Testing and Bounded Model Checking (BMC) are two techniques used in Software Verification for bug-hunting. They are expression of two different philosophies: testing is used on the compiled code and it is more suited to find errors in common behaviors, while BMC is used on the source code to find errors in uncommon behaviors of the system. Nowadays, testing is by far the most used technique for software verification in industry: it is easy to use and even when no error is found, it can release a set of tests certifying the (partial) correctness of the compiled system. In the case of safety critical software, in order to increase the confidence of the correctness of the compiled system, it is often required that the provided set of tests covers 100% of the code. This requirement, however, substantially increases the costs associated to the testing phase, since it often involves the manual generation of tests. In this paper we show how BMC can be productively applied to the Software Verification process in industry. In particular, we show how to productively use a Bounded Model Checker for C programs (CBMC) as an automatic test generator for the Coverage Analysis of Safety Critical Software. In particular, we experimented CBMC on a subset of the modules of the European Train Control System (ETCS) of the European Rail Traffic Management System (ERTMS) source code, an industrial system for the control of the traffic railway, provided by Ansaldo STS. The Code of the ERTMS/ETCS, with thousands of lines, has been used as trial application with CBMC obtaining a set of tests satisfying the target 100% code coverage, requested by the CENELEC EN50128 guidelines for software development of safety critical systems. The use of CBMC for test generation led to a dramatic increase in the productivity of the entire Software Development process by substantially reducing the costs of the testing phase. To the best of our knowledge, this is the first time that BMC techniques have been used in an industrial setting for automatically generating tests achieving full coverage of Safety-Critical Software. The positive results demonstrate the maturity of Bounded Model Checking techniques for automatic test generation in industry.
    Journal of Automated Reasoning 01/2010; 45:397-414. · 0.57 Impact Factor
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    VERIFY@IJCAR; 01/2010
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    Article: QuBE7.0.
    Enrico Giunchiglia, Paolo Marin, Massimo Narizzano
    JSAT. 01/2010; 7:83-88.
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    Alessandro Armando, Enrico Giunchiglia, Serena Elisa Ponta
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    ABSTRACT: We present an approach to the formal specification and automatic analysis of business processes under authorization constraints based on the action language C\cal{C}. The use of C\cal{C} allows for a natural and concise modeling of the business process and the associated security policy and for the automatic analysis of the resulting specification by using the Causal Calculator (CCALC). Our approach improves upon previous work by greatly simplifying the specification step while retaining the ability to perform a fully automatic analysis. To illustrate the effectiveness of the approach we describe its application to a version of a business process taken from the banking domain and use CCALC to determine resource allocation plans complying with the security policy.
    08/2009: pages 63-72;
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    ABSTRACT: In this paper we examine the effect that different knowledge sharing strategies have on the performance of our parallel QBF Solver PaQuBE. This new Master/Slave MPI based solver leverages the additional computational power that can be exploited from modern computer and system architectures, to solve more relevant instances and faster than previous generation solvers. Knowledge sharing plays a critical role in the performance of PaQuBE. However, due to the overhead associated with sending and receiving MPI messages, and the restricted communication/network bandwidth available between solvers, it is essential that we optimize not only which information is shared, but how it is shared. In this context, we compare multiple conflict clause and solution cube sharing strategies, and finally show that an adaptive method works best. Additionally, compression of solution cubes was explored which reduced the system time associated with message passing while also reducing network traffic.
    High Performance Computing & Simulation, 2009. HPCS '09. International Conference on; 07/2009
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    ABSTRACT: ERTMS is the European Railway Traffic Management System. The CENELEC EN50128 guidelines for software development of safety critical system require that the software produced is verified by providing a set of tests covering the 100% of the code. This requirement, however, substantially increases the costs associated to the testing phase, since it may involve the manual generation of tests. In this paper we present a methodology to automatic generate test achieving the desired code coverage. The automatization of the test generation phase, applied to some modules of the ERTMS developed by Ansaldo STS (an Italian leading company in the field), led to a dramatic increase in the productivity and to a reduction of the costs of the entire software development process.
    ICST 2009, Second International Conference on Software Testing Verification and Validation, 1-4 April 2009, Denver, Colorado, USA; 01/2009
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    Enrico Giunchiglia, Marco Maratea
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    ABSTRACT: Planning as Satisfiability (SAT) is the best approach for optimally (wrt makespan) solving classical planning problems. SAT-based planners, like SAT- PLAN, can thus return plans having minimal makespan guaranteed. However, the returned plan does not take into account plan quality issues introduced in th e last two International Planning Competitions (IPCs): such issues include minimal- actions plans and plans with "soft" goals, where a metric has to be optimized over actions/goals. Recently, an approach to address such issues has been presented, in the framework of planning as satisfiability with preferences: by modifyin g the heuristic of the underlying SAT solver, the related system (called SATPLAN(P)) is guaranteed to return plans with minimal number of actions, or with maximal number of soft goals satisfied. But, besides such feature, it is well-kno wn that introducing ordering in SAT heuristics can lead to significant degradation in per- formances. In this paper, we present a generate-and-test approach to tackle the problem of dealing with such optimization issues: without imposing any ordering, a (candidate optimal) plan is first generated, and then a constraint is adde d impos- ing that the new plan (if any) has to be "better" than the last computed, i.e., the plan quality is increased at each iteration. We implemented this idea in SATPLAN, and compared the resulting systems wrt SATPLAN(P) and SGPLAN on planning problems coming from IPCs. The analysis shows performance benefi ts for the new approach, in particular on planning problems with many preferences.
    AI*IA 2009: Emergent Perspectives in Artificial Intelligence, XIth International Conference of the Italian Association for Artificial Intelligence, Reggio Emilia, Italy, December 9-12, 2009, Proceedings; 01/2009
  • Alessandro Armando, Enrico Giunchiglia, Serena Elisa Ponta
    Trust, Privacy and Security in Digital Business, 6th International Conference, TrustBus 2009, Linz, Austria, September 3-4, 2009. Proceedings; 01/2009
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    ABSTRACT: In this paper we present the parallel QBF Solver PaQuBE. This new solver leverages the additional computational power that can be exploited from modern computer architectures, from pervasive multicore boxes to clusters and grids, to solve more relevant instances and faster than previous generation solvers. PaQuBE extends QuBE, its sequential core, by providing a Master/Slave Message Passing Interface (MPI) based design that allows it to split the problem up over an arbitrary number of distributed processes. Furthermore, PaQuBE’s progressive parallel framework is the first to support advanced knowledge sharing in which solution cubes as well as conflict clauses can be shared. According to the last QBF Evaluation, QuBE is the most powerful state-of-the-art QBF Solver. It was able to solve more than twice as many benchmarks as the next best independent solver. Our results here, show that PaQuBE provides additional speedup, solving even more instances, faster.
    Theory and Applications of Satisfiability Testing - SAT 2009, 12th International Conference, SAT 2009, Swansea, UK, June 30 - July 3, 2009. Proceedings; 01/2009
  • Enrico Giunchiglia, Paolo Marin, Massimo Narizzano
    01/2009;
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    E Giunchiglia, Yu Lierler, M Maratea, A Tacchella
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    ABSTRACT: Answer Set Programming (ASP) emerged in the late 1990s as a new logic programming paradigm which has been successfully applied in various application domains. Propositional satisfiability (SAT) is one of the most studied problems in Computer Science. ASP and SAT are closely related: Recent works have studied their relation, and efficient SAT-based ASP solvers (like assat and Cmodels) exist. In this paper we report about (i) the extension of the basic procedures in Cmodels in order to incorporate the most popular SAT reasoning strategies, and (ii) an extensive comparative analysis involving also other state-of-the-art answer set solvers. The experimental analysis points out, besides the fact that Cmodels is highly competitive, that the reasoning strategies that work best on "small but hard" problems are ineffective on "big but easy" problems and vice-versa.
    01/2009;
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    Enrico Giunchiglia, Nicola Leone, Marco Maratea
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    ABSTRACT: In this paper, we study the relation among Answer Set Programming (ASP) systems from a computational point of view. We consider smodels, dlv, and cmodels ASP systems based on stable model semantics, the first two being native ASP systems and the last being a SAT-based system. We first show that smodels, dlv, and cmodels explore search trees with the same branching nodes (assuming, of course, a same branching heuristic) on the class of tight logic programs. Leveraging on the fact that SAT-based systems rely on the deeply studied Davis–Logemann–Loveland (dll) algorithm, we derive new complexity results for the ASP procedures. We also show that on nontight programs the SAT-based systems are computationally different from native procedures, and the latter have computational advantages. Moreover, we show that native procedures can guarantee the “correctness” of a reported solution when reaching the leaves of the search trees (i.e., no stability check is needed), while this is not the case for SAT-based procedures on nontight programs. A similar advantage holds for dlv in comparison with smodels if the “well-founded” operator is disabled and only Fitting’s operator is used for negative inferences. We finally study the “cost” of achieving such advantages and comment on to what extent the results presented extend to other systems.
    Annals of Mathematics and Artificial Intelligence 01/2008; 53:169-204. · 0.20 Impact Factor

Publication Stats

3k Citations
22.77 Total Impact Points

Institutions

  • 1994–2012
    • Università degli Studi di Genova
      • • Dipartimento di Matematica (DIMA)
      • • Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS)
      Genova, Liguria, Italy
  • 1995–2009
    • University of Texas at Austin
      • Department of Computer Science
      Austin, TX, United States
  • 2005
    • University of Geneva
      Genève, Geneva, Switzerland
  • 1998–2002
    • Università degli Studi di Trento
      • Department of Information Engineering and Computer Science
      Trient, Trentino-Alto Adige, Italy