Armando Tacchella

NuSMV 2: An OpenSource Tool for Symbolic Model Checking

This paper describes version 2 of the NuSMV tool. NuSMV is a symbolic model checker originated from the reengineering, reimplementation and extension of SMV, the original BDD-based model checker developed at CMU [15]. The NuSMV project aims at the development of a state-of-the-art symbolic model checker, designed to be applicable in technology transfer projects: it is a well structured, open, flexible and documented platform for model checking, and is robust and close to industrial systems standards [6].

Benefits of Bounded Model Checking at an Industrial Setting

The usefulness of Bounded Model Checking (BMC) based on propositional satisfiability (SAT) methods for bug hunting has already been proven in several recent work. In this paper, we present two industrial strength systems performing BMC for both verification and falsification. The first is Thunder, which performs BMC on top of a new satisfiability solver, SIMO. The second is Forecast, which performs BMC on top of a BDD package. SIMO is based on the Davis Logemann Loveland procedure (DLL) and features the most recent search methods. It enjoys static and dynamic branching heuristics, advanced back-jumping and learning techniques. SIMO also includes new heuristics that are specially tuned for the BMC problem domain. With Thunder we have achieved impressive capacity and productivity for BMC. Real designs, taken from Intels Pentium©4, with over 1000 model variables were validated using the default tool settings and without manual tuning. In Forecast, we present several alternatives for adapting BDD-based model checking for BMC. We have conducted comparison of Thunder and Forecast on a large set of real and complex designs and on almost all of them Thunder has demonstrated clear win over Forecast in two important aspects: capacity and productivity.

QUBE: A System for Deciding Quantified Boolean Formulas Satisfiability

Deciding the satisfiability of a Quantified Boolean Formula (QBF) is an important research issue in Artificial Intelligence. Many reasoning tasks involving planning [1], abduction, reasoning about knowledge, non monotonic reasoning [2], can be directly mapped into the problem of deciding the satisfiability of a QBF. In this paper we present QuBE, a system for deciding QBFs satisfiability.

Backjumping for quantified Boolean logic satisfiability

The implementation of effective reasoning tools for deciding the satisfiability of Quantified Boolean Formulas (QBFs) is an important research issue in Artificial Intelligence. Many decision procedures have been proposed in the last few years, most of them based on the Davis, Logemann, Loveland procedure (DLL) for propositional satisfiability (SAT). In this paper we show how it is possible to extend the conflict-directed backjumping schema for SAT to QBF: when applicable, it allows to jump over existentially quantified literals while backtracking. We introduce solution-directed backjumping, which allows the same for universally quantified literals. Then, we show how it is possible to incorporate both conflict-directed and solution-directed backjumping in a DLL-based decision procedure for QBF satisfiability. We also implement and test the procedure: The experimental analysis shows that, because of backjumping, significant speed-ups can be obtained. While there have been several proposals for backjumping in SAT, this is the first time - as far as we know - this idea has been proposed, implemented and experimented for QBFs.

SAT-based planning in complex domains: concurrency, constraints and nondeterminism

Planning as satisfiability is a very efficient technique for classical planning, i.e., for planning domains in which both the effects of actions and the initial state are completely specified. In this paper we present C-SAT, a SAT-based procedure capable of dealing with planning domains having incomplete information about the initial state, and whose underlying transition system is specified using the highly expressive action language C. Thus, C-SAT allows for planning in domains involving (i) actions which can be executed concurrently; (ii) (ramification and qualification) constraints affecting the effects of actions; and (iii) nondeterminism in the initial state and in the effects of actions. We first prove the correctness and the completeness of C-SAT, discuss some optimizations, and then we present C-PLAN, a system based on C-SAT. C-PLAN works on any C planning problem, but some optimizations have not been fully implemented yet. Nevertheless, the experimental analysis shows that SAT-based approaches to planning with incomplete information are viable, at least in the case of problems with a high degree of parallelism.

A self-adaptive multi-engine solver for quantified Boolean formulas

In this paper we study the problem of engineering a robust solver for quantified Boolean formulas (QBFs), i.e., a tool that can efficiently solve formulas across different problem domains without the need for domain-specific tuning. The paper presents two main empirical results along this line of research. Our first result is the development of a multi-engine solver, i.e., a tool that selects among its reasoning engines the one which is more likely to yield optimal results. In particular, we show that syntactic QBF features can be correlated to the performances of existing QBF engines across a variety of domains. We also show how a multi-engine solver can be obtained by carefully picking state-of-the-art QBF solvers as basic engines, and by harnessing inductive reasoning techniques to learn engine-selection policies. Our second result is the improvement of our multi-engine solver with the capability of updating the learned policies when they fail to give good predictions. In this way the solver becomes also self-adaptive, i.e., able to adjust its internal models when the usage scenario changes substantially. The rewarding results obtained in our experiments show that our solver AQME—Adaptive QBF Multi-Engine—can be more robust and efficient than state-of-the-art single-engine solvers, even when it is confronted with previously uncharted formulas and competitors.

SAT-Based Decision Procedures for Classical Modal Logics

We present a set of SAT-based decision procedures for various classical modal logics. By SAT based, we mean built on top of a SAT solver. We show how the SAT-based approach allows for a modular implementation for these logics. For some of the logics we deal with, we are not aware of any other implementation. For the others, we define a testing methodology that generalizes the 3CNFK methodology by Giunchiglia and Sebastiani. The experimental evaluation shows that our decision procedures perform better than or as well as other state-of-the-art decision procedures.

A multi-engine solver for quantified boolean formulas

In this paper we study the problem of yielding robust performances from current state-of-the-art solvers for quantified Boolean formulas (QBFs). Building on top of existing QBF solvers, we implement a new multi-engine solver which can inductively learn its solver selection strategy. Experimental results confirm that our solver is always more robust than each single engine, that it is stable with respect to various perturbations, and that such results can be partially explained by a handful of features playing a crucial role in our solver.

Evaluating Search Heuristics and Optimization Techniques in Propositional Satisfiability

This paper is devoted to the experimental evaluation of several state-of-the-art search heuristics and optimization techniques in propositional satisfiability (SAT). The test set consists of random 3CNF formulas as well as real world instances from planning, scheduling, circuit analysis, bounded model checking, and security protocols. All the heuristics and techniques have been implemented in a new library for SAT, called SIM. The comparison is fair because in sim the selected heuristics and techniques are realized on a common platform. The comparison is significative because sim as a solver performs very well when compared to other state-of-the-art solvers.

Challenges in the QBF Arena: the SAT’03 Evaluation of QBF Solvers

The implementation of effective reasoning tools for deciding the satisfiability of Quantified Boolean Formulas (QBFs) is an important issue in several research fields such as Formal Verification, Planning, and Reasoning about Knowledge. Several QBF solvers have been implemented in the last few years, most of them extending the well-known Davis, Putnam, Logemann, Loveland procedure (DPLL) for propositional satisfiability (SAT). At the same time, a substantial breed of QBF benchmarks emerged, both in the form of statistical models for the generation of random formulas, and in the form of real-world instances. In this paper we report about the – first ever – evaluation of QBF solvers that was run as a joint event to SAT’03 Conference on Theory and Applications of Satisfiability Testing. Owing to the relative youngness of QBF tools and applications, we decided to run the comparison on a non-competitive basis, using the same technology that powered SAT’02 and SAT’03 competitions of SAT solvers. Running the evaluation enabled us to collect all sorts of data regarding the relative strength of different solvers and methods, the quality of the benchmarks, and to understand some of the current challenges for researchers involved in the QBF arena.