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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 04/2012; 15(4):485-515. · 0.66 Impact Factor
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CoRR. 01/2011; abs/1111.0860.
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J. Log. Comput. 01/2011; 21:205-229.
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Fundam. Inform. 01/2011; 107:139-166.
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J. Autom. Reasoning. 01/2010; 45:397-414.
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JSAT. 01/2010; 7:83-88.
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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: 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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ICST 2009, Second International Conference on Software Testing Verification and Validation, 1-4 April 2009, Denver, Colorado, USA; 01/2009
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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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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
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Theory and Applications of Satisfiability Testing - SAT 2009, 12th International Conference, SAT 2009, Swansea, UK, June 30 - July 3, 2009. Proceedings; 01/2009
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ECAI 2008 - 18th European Conference on Artificial Intelligence, Patras, Greece, July 21-25, 2008, Proceedings; 01/2008
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Principles and Practice of Constraint Programming, 14th International Conference, CP 2008, Sydney, Australia, September 14-18, 2008. Proceedings; 01/2008
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Ann. Math. Artif. Intell. 01/2008; 53:169-204.
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ABSTRACT: The best currently available solvers for quantified Boolean formulas (QBFs) process their input in prenex form, i.e., all the quantifiers have to appear in the prefix of the formula separated from the purely propositional part representing the matrix. However, in many QBFs derived from applications, the propositional part is intertwined with the quantifier structure. To tackle this problem, the standard approach is to convert such QBFs in prenex form, thereby losing structural information about the prefix. In the case of search-based solvers, the prenex-form conversion introduces additional constraints on the branching heuristic and reduces the benefits of the learning mechanisms. In this paper, we show that conversion to prenex form is not necessary: current search-based solvers can be naturally extended in order to handle nonprenex QBFs and to exploit the original quantifier structure. We highlight the two mentioned drawbacks of the conversion in prenex form with a simple example, and we show that our ideas can also be useful for solving QBFs in prenex form. To validate our claims, we implemented our ideas in the state-of-the-art search-based solver QuBE and conducted an extensive experimental analysis. The results show that very substantial speedups can be obtained
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 04/2007; · 1.27 Impact Factor
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AI*IA 2007: Artificial Intelligence and Human-Oriented Computing, 10th Congress of the Italian Association for Artificial Intelligence, Rome, Italy, September 10-13, 2007, Proceedings; 01/2007
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Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, July 22-26, 2007, Vancouver, British Columbia, Canada; 01/2007
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ABSTRACT: The formalization of agents attitudes, and belief in particular, has been investigated in the past by the authors of this paper, along two different but related streams. Giunchiglia and Giunchiglia investigate the properties of contexts for the formal specification of agents mutual beliefs, combining extensional specification with (finite) presentation by means of contexts. Cimatti and Serafini address the representational and implementational implications of the use of contexts for representing prepositional attitudes by tackling a paradigmatic case study. The goal of this paper is to show how these two streams are actually complementary, i.e. how the methodology proposed in the former can be successfully applied to formally specify the case study discussed in the latter. In order to achieve this goal, the formal framework is extended to take into account some relevant aspects of the case study, the specification of which is then worked out in detail.
04/2006: pages 117-130;
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ABSTRACT: Contexts are defined as axiomatic formal systems. More than one context can be defined, each one modeling/solving (part of) the problem. The (global) model/solution of the problem is obtained making contexts communicate via bridge rules. Bridge rules and contexts are the components of Multi Context systems. In this paper we want to study the applicability of multi contexts systems to reason about temporal evolution. The basic idea is to associate a context to each temporal interval in which the model of the problem does not change (corresponding to a state of the system). Switch among contexts (corresponding to modifications in the model) are controlled via a meta-theoric context responsible to keep_track_of the temporal evolution. In this way (i) we keep a clear distinction between the theory describing the particular system at hand and the theory necessary for predicting the temporal evolution (ii) we have simple object level models of the system states and (iii) the theorem prover can faster analize and answer to queries about a particular state. The temporal evolution of a U-tube is taken as an example to show both the proposed framework and the GETFOL implementation.
01/2006: pages 548-557;
