Automated Reasoning in Quantified Modal and Temporal Logics

Centre for Intelligent Systems and their ApplicationsThis thesis is about automated reasoning in quantified modal and temporal logics, with an application to formal methods. Quantified modal and temporal logics are extensions of classical first-order logic in which the notion of truth is extended to take into account its necessity or equivalently, in the temporal setting, its persistence through time. Due to their high complexity, these logics are less widely known and studied than their propositional counterparts. Moreover, little so far is known about their mechanisability and usefulness for formal methods. The relevant contributions of this thesis are threefold: firstly, we devise a sound and complete set of sequent calculi for quantified modal logics; secondly, we extend the approach to the quantified temporal logic of linear, discrete time and develop a framework for doing automated reasoning via Proof Planning in it; thirdly, we show a set of experimental results obtained by applying the framework to the problem of Feature Interactions in telecommunication systems. These results indicate that (a) the problem can be concisely and effectively modeled in the aforementioned logic, (b) proof planning actually captures common structures in the related proofs, and (c) the approach is viable also from the point of view of efficiency

Tactic-based theorem proving in first-order modal and temporal logics

We describe the ongoing work on a tactic-based theorem prover for First-Order Modal and Temporal Logics (FOTLs for the temporal ones). In formal methods, especially temporal logics play a determining role; in particular, FOTLs are natural whenever the modeled systems are in nite-state. But reasoning in FOTLs is hard and few approaches have so far proved eective. Here we introduce a family of sequent calculi for rst-order modal and temporal logics which is modular in the structure of time; moreover, we present a tactic-based modal/temporal theorem prover enforcing this approach, obtained employing the higher-order logic programming language Prolog. Finally, we show some promising experimental results and raise some open issues. We believe that, together with the Proof Planning approach, our system will eventually be able to improve the state of the art of formal methods through the use of FOTLs.

The Planning Spectrum - One, Two, Three, Infinity

Linear Temporal Logic (LTL) is widely used for defining conditions on the execution paths of dynamic systems. In the case of dynamic systems that allow for nondeterministic evolutions, one has to specify, along with an LTL formula f, which are the paths that are required to satisfy the formula. Two extreme cases are the universal interpretation A.f, which requires that the formula be satisfied for all execution paths, and the existential interpretation E.f, which requires that the formula be satisfied for some execution path. When LTL is applied to the definition of goals in planning problems on nondeterministic domains, these two extreme cases are too restrictive. It is often impossible to develop plans that achieve the goal in all the nondeterministic evolutions of a system, and it is too weak to require that the goal is satisfied by some execution. In this paper we explore alternative interpretations of an LTL formula that are between these extreme cases. We define a new language that permits an arbitrary combination of the A and E quantifiers, thus allowing, for instance, to require that each finite execution can be extended to an execution satisfying an LTL formula (AE.f), or that there is some finite execution whose extensions all satisfy an LTL formula (EA.f). We show that only eight of these combinations of path quantifiers are relevant, corresponding to an alternation of the quantifiers of length one (A and E), two (AE and EA), three (AEA and EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for the new language that is based on an automata-theoretic approach, and study its complexity

Linear Temporal Logic and Propositional Schemata, Back and Forth (extended version)

This paper relates the well-known Linear Temporal Logic with the logic of propositional schemata introduced by the authors. We prove that LTL is equivalent to a class of schemata in the sense that polynomial-time reductions exist from one logic to the other. Some consequences about complexity are given. We report about first experiments and the consequences about possible improvements in existing implementations are analyzed.Comment: Extended version of a paper submitted at TIME 2011: contains proofs, additional examples & figures, additional comparison between classical LTL/schemata algorithms up to the provided translations, and an example of how to do model checking with schemata; 36 pages, 8 figure

Time window temporal logic

This paper introduces time window temporal logic (TWTL), a rich expressive language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are prevalent in various applications including robotics, sensor systems, and manufacturing systems. This paper also discusses the relaxation of TWTL formulae with respect to the deadlines of the tasks. Efficient automata-based frameworks are presented to solve synthesis, verification and learning problems. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the given formula. Some case studies are presented to illustrate the expressivity of the logic and the proposed algorithms

Sampling-Based Temporal Logic Path Planning

In this paper, we propose a sampling-based motion planning algorithm that finds an infinite path satisfying a Linear Temporal Logic (LTL) formula over a set of properties satisfied by some regions in a given environment. The algorithm has three main features. First, it is incremental, in the sense that the procedure for finding a satisfying path at each iteration scales only with the number of new samples generated at that iteration. Second, the underlying graph is sparse, which guarantees the low complexity of the overall method. Third, it is probabilistically complete. Examples illustrating the usefulness and the performance of the method are included.Comment: 8 pages, 4 figures; extended version of the paper presented at IROS 201