Parameterised model checking of probabilistic multi-agent systems

Swarm robotics has been put forward as a method of addressing a number of scenarios where scalability and robustness are desired. In order to deploy robotic swarms in safety-critical situations, it is necessary to verify their behaviour. Model checking gives a possible approach to do this; however, with traditional model checking techniques only systems of a finite size can be considered. This presents an issue for swarm systems, where the number of participants in the system is not known at design-time and may be arbitrarily large. To overcome this, parameterised model checking (PMC) techniques have been developed which enable the verification of systems where the number of participants is not known until run-time. However, protocols followed by robotic swarms are often stochastic in nature, and this cannot be modelled with current PMC techniques. This is the gap that this thesis aims to overcome. In particular, two parameterised semantics for reasoning about multi-agent systems are extended to incorporate probabilities. One of these semantics is synchronous, whilst the other is interleaved. Abstract models which overapproximate the systems being considered are constructed using counter abstraction techniques. These abstract models are used to develop parameterised verification procedures for a number of specification logics on both bounded and unbounded traces. The decision procedures presented are shown to be sound, and in some cases also complete. Further, the techniques are extended to allow modelling of situations where agents may exhibit faulty behaviour, as well as scenarios where the strategic capabilities of the participants needs to be verified. The procedures are all implemented in a novel verification toolkit called PSV (Probabilistic Swarm Verifier), built on top of the probabilistic model checker PRISM. This toolkit is used to verify three case studies from both swarm robotics and other application domains.Open Acces

Symmetry Reduction Enables Model Checking of More Complex Emergent Behaviours of Swarm Navigation Algorithms

The emergent global behaviours of robotic swarms are important to achieve their navigation task goals. These emergent behaviours can be verified to assess their correctness, through techniques like model checking. Model checking exhaustively explores all possible behaviours, based on a discrete model of the system, such as a swarm in a grid. A common problem in model checking is the state-space explosion that arises when the states of the model are numerous. We propose a novel implementation of symmetry reduction, in the form of encoding navigation algorithms relatively with respect to a reference, based on the symmetrical properties of swarms in grids. We applied the relative encoding to a swarm navigation algorithm, Alpha, modelled for the NuSMV model checker. A comparison of the state-space and verification results with an absolute (or global) and a relative encoding of the Alpha algorithm highlights the advantages of our approach, allowing model checking larger grid sizes and number of robots, and consequently, verifying more complex emergent behaviours. For example, a property was verified for a grid with 3 robots and a maximum allowed size of 8x8 cells in a global encoding, whereas this size was increased to 16x16 using a relative encoding. Also, the time to verify a property for a swarm of 3 robots in a 6x6 grid was reduced from almost 10 hours to only 7 minutes. Our approach is transferable to other swarm navigation algorithms.Comment: Accepted for presentation in Towards Autonomous Robotic Systems (TAROS) 2015, Liverpool, U

Orion GN&C Fault Management System Verification: Scope And Methodology

In order to ensure long-term ability to meet mission goals and to provide for the safety of the public, ground personnel, and any crew members, nearly all spacecraft include a fault management (FM) system. For a manned vehicle such as Orion, the safety of the crew is of paramount importance. The goal of the Orion Guidance, Navigation and Control (GN&C) fault management system is to detect, isolate, and respond to faults before they can result in harm to the human crew or loss of the spacecraft. Verification of fault management/fault protection capability is challenging due to the large number of possible faults in a complex spacecraft, the inherent unpredictability of faults, the complexity of interactions among the various spacecraft components, and the inability to easily quantify human reactions to failure scenarios. The Orion GN&C Fault Detection, Isolation, and Recovery (FDIR) team has developed a methodology for bounding the scope of FM system verification while ensuring sufficient coverage of the failure space and providing high confidence that the fault management system meets all safety requirements. The methodology utilizes a swarm search algorithm to identify failure cases that can result in catastrophic loss of the crew or the vehicle and rare event sequential Monte Carlo to verify safety and FDIR performance requirements

Swarm SLAM: Challenges and Perspectives

A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints

Towards temporal verification of swarm robotic systems

A robot swarm is a collection of simple robots designed to work together to carry out some task. Such swarms rely on the simplicity of the individual robots; the fault tolerance inherent in having a large population of identical robots; and the self-organised behaviour of the swarm as a whole. Although robot swarms present an attractive solution to demanding real-world applications, designing individual control algorithms that can guarantee the required global behaviour is a difficult problem. In this paper we assess and apply the use of formal verification techniques for analysing the emergent behaviours of robotic swarms. These techniques, based on the automated analysis of systems using temporal logics, allow us to analyse whether all possible behaviours within the robot swarm conform to some required specification. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether temporal properties are satisfied on all the possible behaviours of the system. We target a particular swarm control algorithm that has been tested in real robotic swarms, and show how automated temporal analysis can help to refine and analyse such an algorithm. © 2012 Elsevier B.V. All rights reserved

A survey of modern exogenous fault detection and diagnosis methods for swarm robotics

Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robust-ness being viewed as an inherent property of them. However, this has been shown to not always be thecase. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importancefor ensuring the continued operation and success of the swarm. This paper provides an overview of recentwork in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areaswhere research is concentrated: immune system, data modelling, and blockchain-based fault detectionmethods and local-sensing based fault diagnosis methods. Each of these areas have significant advan-tages and disadvantages which are explored in detail. Though the work presented here represents a sig-nificant advancement in the field, there are still large areas that require further research. Specifically,further research is required in testing these methods on real robotic swarms, fault diagnosis methods,and integrating fault detection, diagnosis and recovery methods in order to create robust swarms thatcan be used for non-trivial tasks

On the Limits and Practice of Automatically Designing Self-Stabilization

A protocol is said to be self-stabilizing when the distributed system executing it is guaranteed to recover from any fault that does not cause permanent damage. Designing such protocols is hard since they must recover from all possible states, therefore we investigate how feasible it is to synthesize them automatically. We show that synthesizing stabilization on a fixed topology is NP-complete in the number of system states. When a solution is found, we further show that verifying its correctness on a general topology (with any number of processes) is undecidable, even for very simple unidirectional rings. Despite these negative results, we develop an algorithm to synthesize a self-stabilizing protocol given its desired topology, legitimate states, and behavior. By analogy to shadow puppetry, where a puppeteer may design a complex puppet to cast a desired shadow, a protocol may need to be designed in a complex way that does not even resemble its specification. Our shadow/puppet synthesis algorithm addresses this concern and, using a complete backtracking search, has automatically designed 4 new self-stabilizing protocols with minimal process space requirements: 2-state maximal matching on bidirectional rings, 5-state token passing on unidirectional rings, 3-state token passing on bidirectional chains, and 4-state orientation on daisy chains

Improving Data Availability in Decentralized Storage Systems

PhD thesis in Information technologyPreserving knowledge for future generations has been a primary concern for humanity since the dawn of civilization. State-of-the-art methods have included stone carvings, papyrus scrolls, and paper books. With each advance in technology, it has become easier to record knowledge. In the current digital age, humanity may preserve enormous amounts of knowledge on hard drives with the click of a button. The aggregation of several hard drives into a computer forms the basis for a storage system. Traditionally, large storage systems have comprised many distinct computers operated by a single administrative entity. With the rise in popularity of blockchain and cryptocurrencies, a new type of storage system has emerged. This new type of storage system is fully decentralized and comprises a network of untrusted peers cooperating to act as a single storage system. During upload, files are split into chunks and distributed across a network of peers. These storage systems encode files using Merkle trees, a hierarchical data structure that provides integrity verification and lookup services. While decentralized storage systems are popular and have a user base in the millions, many technical aspects are still in their infancy. As such, they have yet to prove themselves viable alternatives to traditional centralized storage systems. In this thesis, we contribute to the technical aspects of decentralized storage systems by proposing novel techniques and protocols. We make significant contributions with the design of three practical protocols that each improve data availability in different ways. Our first contribution is Snarl and entangled Merkle trees. Entangled Merkle trees are resilient data structures that decrease the impact hierarchical dependencies have on data availability. Whenever a chunk loss is detected, Snarl uses the entangled Merkle trees to find parity chunks to repair the lost chunk. Our results show that by encoding data as an entangled Merkle tree and using Snarl’s repair algorithm, the storage utilization in current systems could be improved by over five times, with improved data availability. Second, we propose SNIPS, a protocol that efficiently synchronizes the data stored on peers to ensure that all peers have the same data. We designed a Proof of Storage-like construction using a Minimal Perfect Hash Function. Each peer uses the PoS-like construction to create a storage proof for those chunks it wants to synchronize. Peers exchange storage proofs and use them to efficiently determine which chunks they are missing. The evaluation shows that by using SNIPS, the amount of synchronization data can be reduced by three orders of magnitude in current systems. Lastly, in our third contribution, we propose SUP, a protocol that uses cryptographic proofs to check if a chunk is already stored in the network before doing wasteful uploads. We show that SUP may reduce the amount of data transferred by up to 94 % in current systems. The protocols may be deployed independently or in combination to create a decentralized storage system that is more robust to major outages. Each of the protocols has been implemented and evaluated on a large cluster of 1,000 peers

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Managing Byzantine Robots via Blockchain Technology in a Swarm Robotics Collective Decision Making Scenario

While swarm robotics systems are often claimed to be highly fault-tolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robots—i.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in swarm robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure swarm coordination mechanisms and to identify and exclude Byzantine swarm members. We studied the performance of our blockchain-based approach in a collective decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the swarm.Marie Skłodowska-Curie actions (EU project BROS - DLV-751615