Certified Universal Gathering in R2R^2 for Oblivious Mobile Robots

We present a unified formal framework for expressing mobile robots models, protocols, and proofs, and devise a protocol design/proof methodology dedicated to mobile robots that takes advantage of this formal framework. As a case study, we present the first formally certified protocol for oblivious mobile robots evolving in a two-dimensional Euclidean space. In more details, we provide a new algorithm for the problem of universal gathering mobile oblivious robots (that is, starting from any initial configuration that is not bivalent, using any number of robots, the robots reach in a finite number of steps the same position, not known beforehand) without relying on a common orientation nor chirality. We give very strong guaranties on the correctness of our algorithm by proving formally that it is correct, using the COQ proof assistant. This result demonstrates both the effectiveness of the approach to obtain new algorithms that use as few assumptions as necessary, and its manageability since the amount of developed code remains human readable.Comment: arXiv admin note: substantial text overlap with arXiv:1506.0160

An incremental prototyping methodology for distributed systems based on formal specifications

This thesis presents a new incremental prototyping methodology for formally specified distributed systems. The objective of this methodology is to fill the gap which currently exists between the phase where a specification is simulated, generally using some sequential logical inference tool, and the phase where the modeled system has a reliable, efficient and maintainable distributed implementation in a main-stream object-oriented programming language. This objective is realized by application of a methodology we call Mixed Prototyping with Object-Orientation (in short: OOMP). This is an extension of an existing approach, namely Mixed Prototyping, that we have adapted to the object-oriented paradigm, of which we exploit the flexibility and inherent capability of modeling abstract entities. The OOMP process proceeds as follows. First, the source specifications are automatically translated into a class-based object-oriented language, thus providing a portable and high-level initial implementation. The generated class hierarchy is designed so that the developer may independently derive new sub-classes in order to make the prototype more efficient or to add functionalities that could not be specified with the given formalism. This prototyping process is performed incrementally in order to safely validate the modifications against the semantics of the specification. The resulting prototype can finally be considered as the end-user implementation of the specified software. The originality of our approach is that we exploit object-oriented programming techniques in the implementation of formal specifications in order to gain flexibility in the development process. Simultaneously, the object paradigm gives the means to harness this newly acquired freedom by allowing automatic generation of test routines which verify the conformance of the hand-written code with respect to the specifications. We demonstrate the generality of our prototyping scheme by applying it to a distributed collaborative diary program within the frame of CO-OPN (Concurrent Object-Oriented Petri Nets), a very powerful specification formalism which allows expressing concurrent and non-deterministic behaviours, and which provides structuring facilities such as modularity, encapsulation and genericity. An important effort has also been accomplished in the development or adaptation of distributed algorithms for cooperative symbolic resolution. These algorithms are used in the run-time support of the generated CO-OPN prototypes

Bulletin of the University of San Diego Graduate Division 2007-2009

220 pages : illustrations, photographs ; 27.5 cmhttps://digital.sandiego.edu/coursecatalogs-grad/1023/thumbnail.jp

Lessons and prospects for age-restricted active adult housing development in Massachusetts

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2010."June 2010." Cataloged from PDF version of thesis.Includes bibliographical references (p. 77-79).In the last fifteen years, Massachusetts and neighboring states have experienced explosive growth in a hitherto alien form of residential development to the region: the age-restricted active adult retirement community (ARAAC). The growth proved too much for the market to handle, and now developers and municipalities alike are coping with the fallout from oversupply, partially completed projects, and recession-dampened demand. This thesis describes and analyzes the factors that contributed to the current crisis of ARAAC oversupply in Massachusetts. Based on interviews with town officials, developers, and industry observers and analysts, I find that much of the responsibility for this falls upon municipalities, who failed to adequately plan around ARAACs and were often only too eager to approve projects in the belief that they would bring a fiscal windfall. After a thorough exegesis of the legal, policy, and economic factors at play in this finding, I propose a new framework that municipalities can use to better manage the supply and form of ARAACs and conclude with key findings and recommendations directed at municipalities.by Sloan William Dawson.M.C.P

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

On the connection of probabilistic model checking, planning, and learning for system verification

This thesis presents approaches using techniques from the model checking, planning, and learning community to make systems more reliable and perspicuous. First, two heuristic search and dynamic programming algorithms are adapted to be able to check extremal reachability probabilities, expected accumulated rewards, and their bounded versions, on general Markov decision processes (MDPs). Thereby, the problem space originally solvable by these algorithms is enlarged considerably. Correctness and optimality proofs for the adapted algorithms are given, and in a comprehensive case study on established benchmarks it is shown that the implementation, called Modysh, is competitive with state-of-the-art model checkers and even outperforms them on very large state spaces. Second, Deep Statistical Model Checking (DSMC) is introduced, usable for quality assessment and learning pipeline analysis of systems incorporating trained decision-making agents, like neural networks (NNs). The idea of DSMC is to use statistical model checking to assess NNs resolving nondeterminism in systems modeled as MDPs. The versatility of DSMC is exemplified in a number of case studies on Racetrack, an MDP benchmark designed for this purpose, flexibly modeling the autonomous driving challenge. In a comprehensive scalability study it is demonstrated that DSMC is a lightweight technique tackling the complexity of NN analysis in combination with the state space explosion problem.Diese Arbeit präsentiert Ansätze, die Techniken aus dem Model Checking, Planning und Learning Bereich verwenden, um Systeme verlässlicher und klarer verständlich zu machen. Zuerst werden zwei Algorithmen für heuristische Suche und dynamisches Programmieren angepasst, um Extremwerte für Erreichbarkeitswahrscheinlichkeiten, Erwartungswerte für Kosten und beschränkte Varianten davon, auf generellen Markov Entscheidungsprozessen (MDPs) zu untersuchen. Damit wird der Problemraum, der ursprünglich mit diesen Algorithmen gelöst wurde, deutlich erweitert. Korrektheits- und Optimalitätsbeweise für die angepassten Algorithmen werden gegeben und in einer umfassenden Fallstudie wird gezeigt, dass die Implementierung, namens Modysh, konkurrenzfähig mit den modernsten Model Checkern ist und deren Leistung auf sehr großen Zustandsräumen sogar übertrifft. Als Zweites wird Deep Statistical Model Checking (DSMC) für die Qualitätsbewertung und Lernanalyse von Systemen mit integrierten trainierten Entscheidungsgenten, wie z.B. neuronalen Netzen (NN), eingeführt. Die Idee von DSMC ist es, statistisches Model Checking zur Bewertung von NNs zu nutzen, die Nichtdeterminismus in Systemen, die als MDPs modelliert sind, auflösen. Die Vielseitigkeit des Ansatzes wird in mehreren Fallbeispielen auf Racetrack gezeigt, einer MDP Benchmark, die zu diesem Zweck entwickelt wurde und die Herausforderung des autonomen Fahrens flexibel modelliert. In einer umfassenden Skalierbarkeitsstudie wird demonstriert, dass DSMC eine leichtgewichtige Technik ist, die die Komplexität der NN-Analyse in Kombination mit dem State Space Explosion Problem bewältigt

Affinity-Based Reinforcement Learning : A New Paradigm for Agent Interpretability

The steady increase in complexity of reinforcement learning (RL) algorithms is accompanied by a corresponding increase in opacity that obfuscates insights into their devised strategies. Methods in explainable artificial intelligence seek to mitigate this opacity by either creating transparent algorithms or extracting explanations post hoc. A third category exists that allows the developer to affect what agents learn: constrained RL has been used in safety-critical applications and prohibits agents from visiting certain states; preference-based RL agents have been used in robotics applications and learn state-action preferences instead of traditional reward functions. We propose a new affinity-based RL paradigm in which agents learn strategies that are partially decoupled from reward functions. Unlike entropy regularisation, we regularise the objective function with a distinct action distribution that represents a desired behaviour; we encourage the agent to act according to a prior while learning to maximise rewards. The result is an inherently interpretable agent that solves problems with an intrinsic affinity for certain actions. We demonstrate the utility of our method in a financial application: we learn continuous time-variant compositions of prototypical policies, each interpretable by its action affinities, that are globally interpretable according to customers’ financial personalities. Our method combines advantages from both constrained RL and preferencebased RL: it retains the reward function but generalises the policy to match a defined behaviour, thus avoiding problems such as reward shaping and hacking. Unlike Boolean task composition, our method is a fuzzy superposition of different prototypical strategies to arrive at a more complex, yet interpretable, strategy.publishedVersio