Fixed-parameter tractability, definability, and model checking

In this article, we study parameterized complexity theory from the perspective of logic, or more specifically, descriptive complexity theory. We propose to consider parameterized model-checking problems for various fragments of first-order logic as generic parameterized problems and show how this approach can be useful in studying both fixed-parameter tractability and intractability. For example, we establish the equivalence between the model-checking for existential first-order logic, the homomorphism problem for relational structures, and the substructure isomorphism problem. Our main tractability result shows that model-checking for first-order formulas is fixed-parameter tractable when restricted to a class of input structures with an excluded minor. On the intractability side, for every t >= 0 we prove an equivalence between model-checking for first-order formulas with t quantifier alternations and the parameterized halting problem for alternating Turing machines with t alternations. We discuss the close connection between this alternation hierarchy and Downey and Fellows' W-hierarchy. On a more abstract level, we consider two forms of definability, called Fagin definability and slicewise definability, that are appropriate for describing parameterized problems. We give a characterization of the class FPT of all fixed-parameter tractable problems in terms of slicewise definability in finite variable least fixed-point logic, which is reminiscent of the Immerman-Vardi Theorem characterizing the class PTIME in terms of definability in least fixed-point logic.Comment: To appear in SIAM Journal on Computin

Combining Machine Learning and Formal Methods for Complex Systems Design

During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements. In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above. In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study.During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements. In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above. In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study

A Team Based Variant of CTL

We introduce two variants of computation tree logic CTL based on team semantics: an asynchronous one and a synchronous one. For both variants we investigate the computational complexity of the satisfiability as well as the model checking problem. The satisfiability problem is shown to be EXPTIME-complete. Here it does not matter which of the two semantics are considered. For model checking we prove a PSPACE-completeness for the synchronous case, and show P-completeness for the asynchronous case. Furthermore we prove several interesting fundamental properties of both semantics.Comment: TIME 2015 conference version, modified title and motiviatio

Decidable Reasoning in Terminological Knowledge Representation Systems

Terminological knowledge representation systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). We analyze from a theoretical point of view a TKRS whose capabilities go beyond the ones of presently available TKRSs. The new features studied, often required in practical applications, can be summarized in three main points. First, we consider a highly expressive terminological language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, we allow to express inclusion statements between general concepts, and terminological cycles as a particular case. Third, we prove the decidability of a number of desirable TKRS-deduction services (like satisfiability, subsumption and instance checking) through a sound, complete and terminating calculus for reasoning in ALCNR-knowledge bases. Our calculus extends the general technique of constraint systems. As a byproduct of the proof, we get also the result that inclusion statements in ALCNR can be simulated by terminological cycles, if descriptive semantics is adopted.Comment: See http://www.jair.org/ for any accompanying file

An LTL Semantics of Business Workflows with Recovery

We describe a business workflow case study with abnormal behavior management (i.e. recovery) and demonstrate how temporal logics and model checking can provide a methodology to iteratively revise the design and obtain a correct-by construction system. To do so we define a formal semantics by giving a compilation of generic workflow patterns into LTL and we use the bound model checker Zot to prove specific properties and requirements validity. The working assumption is that such a lightweight approach would easily fit into processes that are already in place without the need for a radical change of procedures, tools and people's attitudes. The complexity of formalisms and invasiveness of methods have been demonstrated to be one of the major drawback and obstacle for deployment of formal engineering techniques into mundane projects