Self-adaptive Scouting---Autonomous Experimentation for Systems Biology

We introduce a new algorithm for autonomous experimentation. This algorithm uses evolution to drive exploration during scientific discovery. Population size and mutation strength are self-adaptive. The only variables remaining to be set are the limits and maximum resolution of the parameters in the experiment. In practice, these are determined by instrumentation. Aside from conducting physical experiments, the algorithm is a valuable tool for investigating simulation models of biological systems. We illustrate the operation of the algorithm on a model of HIV-immune system interaction. Finally, the difference between scouting and optimization is discussed

Utilizing Ground-Based LIDAR Measurements to Aid Autonomous Airdrop Systems

Uncertainty in atmospheric winds represents one of the primary sources of landing error in airdrop systems. In this work, a ground-based LIDAR system samples the wind field at discrete points above the target and transmits real-time data to approaching autonomous airdrop systems. In simulation and experimentation, the inclusion of a light detection and ranging (LIDAR) system showed a maximum of 40% improvement over unaided autonomous airdrop systems. Wind information nearest ground level has the largest impact on improving accuracy

Considerations about Continuous Experimentation for Resource-Constrained Platforms in Self-Driving Vehicles

Autonomous vehicles are slowly becoming reality thanks to the efforts of many academic and industrial organizations. Due to the complexity of the software powering these systems and the dynamicity of the development processes, an architectural solution capable of supporting long-term evolution and maintenance is required. Continuous Experimentation (CE) is an already increasingly adopted practice in software-intensive web-based software systems to steadily improve them over time. CE allows organizations to steer the development efforts by basing decisions on data collected about the system in its field of application. Despite the advantages of Continuous Experimentation, this practice is only rarely adopted in cyber-physical systems and in the automotive domain. Reasons for this include the strict safety constraints and the computational capabilities needed from the target systems. In this work, a concept for using Continuous Experimentation for resource-constrained platforms like a self-driving vehicle is outlined.Comment: Copyright 2017 Springer. Paper submitted and accepted at the 11th European Conference on Software Architecture. 8 pages, 1 figure. Published in Lecture Notes in Computer Science vol 10475 (Springer), https://link.springer.com/chapter/10.1007/978-3-319-65831-5_

System Design for Digital Experimentation and Explanation Generation

Experimentation increasingly drives everyday decisions in modern life, as it is considered by some to be the gold standard for determining cause and effect within any system. Digital experiments have expanded the scope and frequency of experiments, which can range in complexity from classic A/B tests to contextual bandits experiments, which share features with reinforcement learning. Although there exists a large body of prior work on estimating treatment effects using experiments, this prior work did not anticipate the new challenges and opportu- nities introduced by digital experimentation. Novel errors and threats to validity arise at the intersection of software and experimentation, especially when experimentation is in service of understanding humans behavior or autonomous black-box agents. We present several novel tools for automating aspects of the experimentation- analysis pipeline. We propose new methods for evaluating online field experimentation, automatically generating corresponding analyses of treatment effects. We then draw the connection between software testing and experimental design and argue that applying software testing techniques to a kind of autonomous agent—a deep reinforcement learning agent—to demonstrate the need for novel testing paradigms when a software stack uses learned components that may have emergent behavior. We show how our system may be used to evaluate claims made about the behavior of autonomous agents and find that some claims do not hold up under test. Finally, we show how to produce explanations of the behavior of black-box software-defined agents interacting with white-box environments via automated experimentation. We show how an automated system can be used for exploratory data analysis, with a human in the loop, to investigate a large space of possible counterfactual explanations

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments.

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection

Air vehicle simulator: an application for a cable array robot

The development of autonomous air vehicles can be an expensive research pursuit. To alleviate some of the financial burden of this process, we have constructed a system consisting of four winches each attached to a central pod (the simulated air vehicle) via cables - a cable-array robot. The system is capable of precisely controlling the three dimensional position of the pod allowing effective testing of sensing and control strategies before experimentation on a free-flying vehicle. In this paper, we present a brief overview of the system and provide a practical control strategy for such a system. ©2005 IEEE

Analysing the effects of sensor fusion, maps and trust models on autonomous vehicle satellite navigation positioning

This thesis analyzes the effects of maps, sensor fusion and trust models on autonomous vehicle satellite positioning. The aim is to analyze the localization improvements that commonly used sensors, technologies and techniques provide to autonomous vehicle positioning. This thesis includes both survey of localization techniques used by other research and their localization accuracy results as well as experimentation where the effects of different technologies and techniques on lateral position accuracy are reviewed. The requirements for safe autonomous driving are strict and while the performance of the average global navigation satellite system (GNSS) receiver alone may not prove to be adequate enough for accurate positioning, it may still provide valuable position data to an autonomous vehicle. For the vehicle, this position data may provide valuable information about the absolute position on the globe, it may improve localization accuracy through sensor fusion and it may act as an independent data source for sensor trust evaluation. Through empirical experimentation, the effects of sensor fusion and trust functions with an inertial measurement unit (IMU) on GNSS lateral position accuracy are measured and analyzed. The experimentation includes the measurements from both consumer-grade devices mounted on a traditional automobile and high-end devices of a truck that is capable of autonomous driving in a monitored environment. The maps and LIDAR measurements used in the experiments are prone to errors and are taken into account in the analysis of the data

Connecting Theater and Virtual Reality with Cognitive Sciences: Positioning from computer science and artist meeting

International audienceThis positioning paper presents arguments in favor of collaboration between artists and computer scientists in touch with cognitive science. Each part met the other for a technical collaboration during one theater experimentation named " il était Xn fois ". The article starts with the scientists position relative to the link between cognitive sciences, virtual reality and artificial intelligence. This section highlights the need of autonomous entities to improve presence in artificial world and presents enactive artificial intelligence which aims at producing strong autonomous entities. The second part presents the purpose of the theatrical experimentation "il était Xn fois", which was publicly presented in 2009 by the theater dérézo. The last section is a synthetic view of what should complete artistic and computer scientists area