Laboratoire Spécification et Vérification

« Mobiliser son réseau ou sa parentèle pour obtenir justice : jeux de pouvoir et pratiques judiciaires au Moyen Âge et à l’époque moderne » : tous les termes qui figurent dans le titre du colloque qui nous a réunis ont été largement questionnés au cours de ces deux journées, et peuvent être légitimement discutés : c’est un sujet d’étude délicat qui a été soumis à notre réflexion, situé au croisement de différentes historiographies, qui n’ont pas l’habitude de se rencontrer. Nous pouvons déjà ...

Abstract Policy Interaction Graph Analysis is a Host-based Intrusion Detection tool that uses Linux MAC Mandatory access control policy to build the licit information flow graph and uses a detection policy defined by the administrator to extract illicit behaviour from the graph. The main limitation of this tool is the generation of a huge signature base of illicit behaviours; hence, this leads to the use of huge memory space to store it. Our primary goal in this article is to reduce this memory space while keeping the tool’s efficiency in terms of intrusion detection rate and false generated alarms. First, the interactions between the two nodes of the graph were grouped into a single interaction. The notion of equivalence class was used to classify the paths in the graph and was compressed by using a genetic algorithm. Such an approach showed its efficiency compared to the approach proposed by Pierre Clairet, by which the detection rate obtained was 99.9%, and no false-positive with a compression rate of illicit behaviour signature database reached 99.44%. Having these results is one of the critical aspects of realizing successful host-based intrusion detection systems.

Computing via synthetically engineered bacteria is a vibrant and active field with numerous applications in bio-production, bio-sensing, and medicine. Motivated by the lack of robustness and by resource limitation inside single cells, distributed approaches with communication among bacteria have recently gained in interest. In this paper, we focus on the problem of population growth happening concurrently, and possibly interfering, with the desired bio-computation. Specifically, we present a fast protocol in systems with continuous population growth for the majority consensus problem and prove that it correctly identifies the initial majority among two inputs with high probability if the initial difference is varOmega (sqrt{nlog n}) where n is the total initial population. We also present a fast protocol that correctly computes the Nand of two inputs with high probability. By combining Nand gates with the majority consensus protocol as an amplifier, it is possible to compute arbitrary Boolean functions. Finally, we extend the protocols to several biologically relevant settings. We simulate a plausible implementation of a noisy Nand gate with engineered bacteria. In the context of continuous cultures with a constant outflow and a constant inflow of fresh media, we demonstrate that majority consensus is achieved only if the flow is slower than the maximum growth rate. Simulations suggest that flow increases consensus time over a wide parameter range. The proposed protocols help set the stage for bio-engineered distributed computation that directly addresses continuous stochastic population growth.

This paper describes experimental results regarding the real time implementation of continuous time recurrent neural networks (CTRNN) and the dynamic back-propagation through time (BPTT) algorithm for the on-line learning control laws. Experiments are carried out to control the balance of a biped robot prototype in its standing posture. The neural controller is trained to compensate for external perturbations by controlling the torso's joint motions. Algorithms are embedded in the real time electronic unit of the robot. On-line learning implementations are presented in detail. The results on learning behavior and control performance demonstrate the strength and the efficiency of the proposed approach.

Markov chains and Markov decision processes (MDPs) are well-established probabilistic models. While finite Markov models are well-understood, analysing their infinite counterparts remains a significant challenge. Decisiveness has proven to be an elegant property for countable Markov chains: it is general enough to be satisfied by several natural classes of countable Markov chains, and it is a sufficient condition for simple qualitative and approximate quantitative model-checking algorithms to exist. In contrast, existing works on the formal analysis of countable MDPs usually rely on ad hoc techniques tailored to specific classes. We provide here a general framework to analyse countable MDPs by extending the notion of decisiveness. Compared to Markov chains, MDPs exhibit extra non-determinism that can be resolved in an adversarial or cooperative way, leading to multiple natural notions of decisiveness. We show that these notions enable the approximation of reachability and safety probabilities in countable MDPs using simple model-checking procedures. We then instantiate our generic approach to two concrete classes of models inducing countable MDPs: non-deterministic probabilistic lossy channel systems and partially observable MDPs. This leads to an algorithm to approximately compute safety probabilities in each of these classes.

For decades, two-player (antagonistic) games on graphs have been a framework of choice for many important problems in theoretical computer science. A notorious one is controller synthesis, which can be rephrased through the game-theoretic metaphor as the quest for a winning strategy of the system in a game against its antagonistic environment. Depending on the specification, optimal strategies might be simple or quite complex, for example having to use (possibly infinite) memory. Hence, research strives to understand which settings allow for simple strategies. In 2005, Gimbert and Zielonka [Hugo Gimbert and Wieslaw Zielonka, 2005] provided a complete characterization of preference relations (a formal framework to model specifications and game objectives) that admit memoryless optimal strategies for both players. In the last fifteen years however, practical applications have driven the community toward games with complex or multiple objectives, where memory - finite or infinite - is almost always required. Despite much effort, the exact frontiers of the class of preference relations that admit finite-memory optimal strategies still elude us. In this work, we establish a complete characterization of preference relations that admit optimal strategies using arena-independent finite memory, generalizing the work of Gimbert and Zielonka to the finite-memory case. We also prove an equivalent to their celebrated corollary of great practical interest: if both players have optimal (arena-independent-)finite-memory strategies in all one-player games, then it is also the case in all two-player games. Finally, we pinpoint the boundaries of our results with regard to the literature: our work completely covers the case of arena-independent memory (e.g., multiple parity objectives, lower- and upper-bounded energy objectives), and paves the way to the arena-dependent case (e.g., multiple lower-bounded energy objectives).

In this work, a general concept of the human-exoskeleton compatibility and interaction control is addressed. Rehabilitation, as applied to humans with motor control disorder, involves repetitive gait training in relation to lower limb extremity and repetitive task training in relation to upper limb extremity. It is in this regard that exoskeletal systems must be kinematically compatible with those of the subject in order to guarantee that the subject is being trained properly. The incompatibility between the wearable robotic device and the wearer results in joint misalignment, thus introducing interaction forces during movement. This, therefore, leads to the introduction of the need for interaction control in wearable robotic devices. Human-exoskeleton joint alignment is an uphill task; hence, measures to actualize this in order to guarantee the safety and comfort of humans are necessary. These measures depend on the types of joints involved in the rehabilitation or assistive process. Hence, several upper and lower extremity exoskeletons with concepts relating to interaction forces reduction are reviewed. The significant distinction in the modelling strategy of lower and upper limb exoskeletons is highlighted. Limitations of certain exoskeletal systems which may not allow the application of interaction control are also discussed.

Timed Automata (TA) are an appropriate model for specifying timed requirements for Continuous Time Markov Chains (CTMC). However in order to keep tractable the model checking of a TA over a CTMC, temporal logics based on TA, like CSL\(^{\!\text {TA}}\), restrict TA to have a single clock and to be deterministic (DTA). Different variants of DTAs have been proposed to address the issue of their expressiveness and conciseness. Here we study the effect of two possible features: (1) autonomous transitions which are triggered by time elapsing in addition to synchronized transitions and (2) transitions guarded by propositional formulas instead of propositional formulas guarding locations. We first show that autonomous guarded transitions increase the expressiveness of DTAs (as already shown for guarded locations). Then we identify a hierarchy of DTAs subclasses all equivalent to DTAs without guarded autonomous transitions and we analyze their respective conciseness. In particular we show that eliminating resets in autonomous transitions implies an exponential blow-up, while eliminating autonomous transitions without reset can be performed in polynomial time if decision diagrams are used. Finally we compare TA with guarded transitions to TA with guarded locations showing that the former model is exponentially more concise than the latter one.

Fugger et al. (2016) proved that no existing digital circuit model, including those based on pure and inertial delay channels, faithfully captures glitch propagation: for the short-pulse filtration (SPF) problem similar to that of building a one-shot inertial delay, they showed that every member of the broad class of bounded single-history channels either contradicts the unsolvability of SPF in bounded time or the solvability of SPF in unbounded time in physical circuits. In this article, we propose binary circuit models based on novel involution channels that do not suffer from this deficiency. Namely, in sharp contrast to bounded single-history channels, SPF cannot be solved in bounded time with involution channels, whereas it is easy to provide an unbounded SPF implementation. Hence, binary-valued circuit models based on involution channels allow to solve SPF precisely when this is possible in physical circuits. Additionally, using both SPICE simulations and physical measurements of an inverter chain instrumented by high-speed analog amplifiers, we demonstrate that our model provides good modeling accuracy with respect to real circuits as well. Consequently, our involution channel model is not only a promising basis for sound formal verification but also allows to seamlessly improve existing dynamic timing analysis.