Dynamic relational mereotopology: Logics for stable and unstable relations

In this paper we present stable and unstable versions of several well-known relations from mereotopology: part-of, overlap, underlap and contact. An intuitive semantics is given for the stable and unstable relations, describing them as dynamic counterparts of the base mereotopo-logical relations. Stable relations are described as ones that always hold, while unstable relations hold sometimes. A set of first-order sentences is provided to serve as axioms for the stable and unstable relations, and representation theory is developed in similar fashion to Stone’s representation theory for Boolean algebras and distributive lattices. Then we present some results about the first-order predicate logic of these relations and about its quantifier-free fragment. Completeness theorems for these logics are proved, the full first-order theory is proved to be hereditary undecidable and the satisfiability problem of the quantifier-free fragment is proved to be NP-complete

Optimale Regelung von mobilen Robotern in teilweise unbekannten Umgebungen

An autonomous mobile robot that performs a task in a partially unknown environment pursues two goals simultaneously: on the one hand, it has to acquire information from the environment (necessary for solving the task), and on the other hand, it must use this information to adapt its control strategy. This fundamental compromise between exploration and exploitation in motion planning has been extensively addressed in robotics, machine learning and other related fields. However, the influence of the underlying continuous dynamics of the robot was thereby often neglected. This thesis proposes optimal control schemes for a mobile robot with continuous dynamics that has to satisfy a high-level specification in a partially unknown environment. In particular, a setup is addressed, where a robot with a finite sensing range has to find and collect objects in a bounded space and move them to a marked safe spot in minimum time. The problem is first approached by a heuristically-motivated hierarchical decomposition that allows for efficient discrete motion optimization and re-planning. Assuming that the robot moves on a line, these heuristic simplifications can be omitted, and the optimal exploration and control problem is solved for the worst and a probabilistic case assuming a uniform distribution of the objects' positions. This result is then used to obtain an event-driven receding horizon control scheme for higher dimensional position spaces possibly containing obstacles. Then, under the presence of a richer high-level specification given as a linear temporal logic formula, the optimal control problem is investigated both for known and partially unknown environments. The developed methods are relevant for a large class of dynamic vehicle routing problems, in particular for search and rescue, cleaning, coating, monitoring, manufacturing, field harvesting etc.Ein autonomer mobiler Roboter, der in einer teilweise unbekannten Umgebung eine Aufgabe erfüllt, verfolgt gleichzeitig zwei Ziele: einerseits muss Information von der Umgebung gesammelt werden, die für die Erfüllung der Aufgabe notwendig ist, und andererseits, muss diese Information für die Anpassung der gewählten Regelstrategie effizient genutzt werden. Dieser fundamentale Kompromiss zwischen Exploration und Exploitation in der Bewegungsplanung wurde in der Robotik, dem maschinellen Lernen und anderen verwandten Gebieten ausführlich behandelt. Dabei wurde allerdings der Einfluss der kontinuierlichen Dynamik des Roboters oft vernachlässigt. Diese Arbeit stellt optimale Regelungsansätze für einen mobilen Roboter mit kontinuierlicher Dynamik, der eine High-Level-Spezifikation in einer teilweise unbekannten Umgebung erfüllen soll, vor. Insbesondere wird eine Problemstellung behandelt, bei der ein Roboter mit endlichem Sensorfeld Objekte in einem beschränkten Raum finden, mitnehmen und zu einem vorher bekannten Ort in minimaler Zeit transportieren soll. Dieses Problem wird zunächst mittels einer heuristisch motivierten hierarchischen Dekomposition gelöst, die eine effiziente Neuplanung und Bewegungsoptimierung ermöglicht. Für den Fall, dass sich der Roboter entlang einer Linie bewegt, werden diese heuristischen Vereinfachungen nicht mehr benötigt und das optimale Explorations- und Regelungsproblem wird für den worst-case und für einen probabilistischen Fall, bei dem angenommen wird, dass die Position aller Objekte gleichverteilt ist, gelöst. Dieses Ergebnis wird dann verwendet um einen ereignis-getriggerten modellprädiktiven Ansatz für höher dimensionale Positionsräume, die potentiell Hindernisse beinhalten, abzuleiten. Für eine komplexere höhere Spezifikation, gegeben in Form einer linearen temporalen Logikformel, wird dann das optimale Regelungsproblem für bekannte und teilweise unbekannte Umgebungen untersucht. Die entwickelten Methoden sind für eine Reihe von dynamischen Vehicle Routing Problemen anwendbar, insbesondere für automatisierte Suche und Rettung, Reinigung, Beschichtung, Monitoring, Herstellung usw

Robustness Analysis of MAPK Signaling Cascades

The MAPK cascade is responsible for transmitting information in the cytoplasm of the cell and regulating important fate decisions like cell division and apoptosis. Due to scarce experimental data and limited knowledge about many complex biochemical processes, existing MAPK pathway models, which exhibit bistability, have a significant structural uncertainty. Often, small perturbations of network interactions or components can reduce the bistable region significantly or make it even disappear and small fluctuations of the input can make the system switch back, which reflects its low robustness. However, real biological systems have developed significant robustness through evolution and this robustness should be reflected by the models. The main goal of the present thesis is the development of a methodology for increasing the robustness of biochemical models, which exhibit bistability. Based on modifying existing network interactions or introducing new interactions to the system, several methods for both internal and external robustification are proposed. Internal robustness is addressed through a sensitivity analysis, which deals with a linearization of the model and can be used sequentially to introduce multiple modifications to the model. The methods for external robustness improvement are based on eigenvalue placement and slope modification (drawing on the linear model) and on the identification of feedback structures (nonlinear model). Further, a way to integrate static interaction changes to the nonlinear model, so that these perturbations have only a local impact on its behavior, is proposed. The application of the methods to existing MAPK models shows that, by introducing small modifications, the internal and external robustness of models can be increased significantly and thus provides knowledge about complex dynamics and interactions that play a key role for the inherent robustness of real biological systems. Furthermore, by employing a robustness analysis, stable steady-state branches can be recovered and bistability can be induced

Learning to falsify automated driving vehicles with prior knowledge

While automated driving technology has achieved a tremendous progress, the scalable and rigorous testing and verification of safe automated and autonomous driving vehicles remain challenging. Assuming that the specification is associated with a violation metric on possible scenarios, this paper proposes a learning-based falsification framework for testing the implementation of an automated or self-driving function in simulation. Prior knowledge is incorporated to limit the scenario parameter variance and into a model-based falsifier to guide and improve the learning process. For an exemplary adaptive cruise controller, the presented framework yields non-trivial falsifying scenarios with higher reward, compared to scenarios obtained by purely learning-based or purely model-based falsification approaches

Automatic requirements extraction, analysis, and graph representation using an approach derived from computational linguistics

International audienceThe quality of requirements is fundamental in engineering projects. Requirements are usually expressed partly or totally in a natural language (NL) format and come from different documents. Their qualities are difficult to analyze manually, especially when hundreds of thousands of them have to be considered. The assistance of software tools is becoming a necessity. In this article, the goal was to develop a set of metrics supported by NL processing (NLP) methods supporting different types of analysis of requirements and especially the dependencies between requirements. An NLP approach is used to extract requirements from text; to analyze their quality, links, similarities , and contradictions; and to cluster them automatically. The analysis framework includes different combinations of methods such as cosine similarity, singular value decomposition, and K-means clustering. One objective is to assess the possible combinations and their impacts on detections to establish optimal metrics. Three case studies exemplify and support the validation of the work. Graphs are used to represent the automatically clustered requirements, as well as similarities and contradictions. A new contradiction analysis process that includes a rules-based approach is proposed. Finally, the combined results are presented as graphs, which unveil the semantic relationships between requirements. Subsection 4.8 compares the results provided by the tool and the results obtained from experts. The proposed methodology and network presentation not only support the understanding of the semantics of the requirements but also help requirements engineers to review the interconnections and consistency of requirements systems and manage trace-ability. The approach is valuable during the early phases of projects when requirements are evolving dynamically and rapidly