SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems

The recent drive towards achieving greater autonomy and intelligence in robotics has led to high levels of complexity. Autonomous robots increasingly depend on third party off-the-shelf components and complex machine-learning techniques. This trend makes it challenging to provide strong design-time certification of correct operation. To address these challenges, we present SOTER, a robotics programming framework with two key components: (1) a programming language for implementing and testing high-level reactive robotics software and (2) an integrated runtime assurance (RTA) system that helps enable the use of uncertified components, while still providing safety guarantees. SOTER provides language primitives to declaratively construct a RTA module consisting of an advanced, high-performance controller (uncertified), a safe, lower-performance controller (certified), and the desired safety specification. The framework provides a formal guarantee that a well-formed RTA module always satisfies the safety specification, without completely sacrificing performance by using higher performance uncertified components whenever safe. SOTER allows the complex robotics software stack to be constructed as a composition of RTA modules, where each uncertified component is protected using a RTA module. To demonstrate the efficacy of our framework, we consider a real-world case-study of building a safe drone surveillance system. Our experiments both in simulation and on actual drones show that the SOTER-enabled RTA ensures the safety of the system, including when untrusted third-party components have bugs or deviate from the desired behavior

Training artificial neural networks to learn a nondeterministic game

It is well known that artificial neural networks (ANNs) can learn deterministic automata. Learning nondeterministic automata is another matter. This is important because much of the world is nondeterministic, taking the form of unpredictable or probabilistic events that must be acted upon. If ANNs are to engage such phenomena, then they must be able to learn how to deal with nondeterminism. In this project the game of Pong poses a nondeterministic environment. The learner is given an incomplete view of the game state and underlying deterministic physics, resulting in a nondeterministic game. Three models were trained and tested on the game: Mona, Elman, and Numenta's NuPIC.Comment: ICAI'15: The 2015 International Conference on Artificial Intelligence, Las Vegas, NV, USA, 201

Improving GPU Simulations of Spiking Neural P Systems

In this work we present further extensions and improvements of a Spiking Neural P system (for short, SNP systems) simulator on graphics processing units (for short, GPUs). Using previous results on representing SNP system computations using linear algebra, we analyze and implement a compu- tation simulation algorithm on the GPU. A two-level parallelism is introduced for the computation simulations. We also present a set of benchmark SNP sys- tems to stress test the simulation and show the increased performance obtained using GPUs over conventional CPUs. For a 16 neuron benchmark SNP system with 65536 nondeterministic rule selection choices, we report a 2.31 speedup of the GPU-based simulations over CPU-based simulations.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Two nondeterministic event building methods derived from the barrel shifter

At High Energy Physics experiments, extensive parallelism allowsscalable, high-bandwidth data acquisition systems. On-line eventbuilding of physical events is only feasible by using switch-basedevent builders. The Barrel Shifter is a well-known event buildingmethod for switch-based event builders. Two nondeterministic versions of the Barrel Shifter introduced in this paper providecost-effective alternatives to the Barrel Shifter. Simulation resultsare presented to show the source buffer requirements of bothnondeterministic methods and the Barrel Shifter at different typesdetector data flow

Delimited Massively Parallel Algorithm Based on Rules Elimination for Application of Active Rules in Transition P Systems

In the field of Transition P systems implementation, it has been determined that it is very important to determine in advance how long takes evolution rules application in membranes. Moreover, to have time estimations of rules application in membranes makes possible to take important decisions related to hardware / software architectures design. The work presented here introduces an algorithm for applying active evolution rules in Transition P systems, which is based on active rules elimination. The algorithm complies the requisites of being nondeterministic, massively parallel, and what is more important, it is time delimited because it is only dependant on the number of membrane evolution rules

Simulators for teaching formal languages and automata theory: a comparative survey

Formal languages and automata theory (FL&AT) are central subjects in the CS curricula which are usually diffcult both to teach and to learn. This situation has motivated the development of a number of computer simulators as educational tools which allow the student to implement and `bring to life' many topics which traditionally were studied and analyzed mathematically rather than algorithmically. This paper discusses the main features of several educational software tools currently available for teaching FL&AT. Advantages and weaknesses of different tools are analyzed and contrasted. Based in our experience, some rationales and practical considerations for the development of this kind of educational tools are proposed.Eje: Tecnología aplicada en EducaciónRed de Universidades con Carreras en Informática (RedUNCI

A Statistical Model Checker for Nondeterminism and Rare Events

A great publication