Automated Telescience: Active Machine Learning Of Remote Dynamical Systems

Automated science is an emerging field of research and technology that aims to extend the role of computers in science from a tool that stores and analyzes data to one that generates hypotheses and designs experiments. Despite the tremendous discoveries and advancements brought forth by the scientific method, it is a process that is fundamentally driven by human insight and ingenuity. Automated science aims to develop algorithms, protocols and design philosophies that are capable of automating the scientific process. This work presents advances the field of automated science and the specific contributions of this work fall into three categories: coevolutionary search methods and applications, inferring the underlying structure of dynamical systems, and remote controlled automated science. First, a collection of coevolutionary search methods and applications are presented. These approaches include: a method to reduce the computational overhead of evolutionary algorithms via trainer selection strategies in a rank predictor framework, an approach for optimal experiment design for nonparametric models using Shannon information, and an application of coevolutionary algorithms to infer kinematic poses from RGBD images. Second, three algorithms are presented that infer the underlying structure of dynamical systems: a method to infer discrete-continuous hybrid dynamical systems from unlabeled data, an approach to discovering ordinary differential equations of arbitrary order, and a principle to uncover the existence and dynamics of hidden state variables that correspond to physical quantities from nonlinear differential equations. All of these algorithms are able to uncover structure in an unsupervised manner without any prior domain knowledge. Third, a remote controlled, distributed system is demonstrated to autonomously generate scientific models by perturbing and observing a system in an intelligent fashion. By automating the components of physical experimentation, scientific modeling and experimental design, models of luminescent chemical reactions and multi-compartmental pharmacokinetic systems were discovered without any human intervention, which illustrates how a set of distributed machines can contribute scientific knowledge while scaling beyond geographic constraints

Automation and Control

Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

Evolutionary Model Discovery: Automating Causal Inference for Generative Models of Human Social Behavior

The desire to understand the causes of complex societal phenomena is fundamental to the social sciences. Society, at a macro-scale has many measurable characteristics in the form of statistical distributions and aggregate measures; data which is increasingly abundant with the proliferation of online social media, mobile devices, and the internet of things. However, the decision-making processes and limits of the individuals who interact to generate these statistical patterns are often difficult to unravel. Furthermore, multiple causal factors often interact to determine the outcome of a particular behavior. Quantifying the importance of these causal factors and their interactions, which make up a particular decision-making process, towards a societal outcome of interest helps extract explanations that provide a deeper understanding of social behavior. Holistic, generative modeling techniques, in particular agent-based modeling, are able to \u27grow\u27 artificial societies that replicate emergent patterns seen in the real world. Driving the autonomous agents of these models are rules, generalized hypotheses of human behavior, which upon validation against real-world data, help assemble theories of human behavior. Yet often, multiple hypothetical causal factors can be suggested for the construction of these rules. With traditional agent-based modeling, it is often up to the modeler\u27s discretion to decide which combination of factors best represent the rule at hand. Yet, due to the aforementioned lack of insight, the modeled agent rule is often one out of a vast space of possible rules. In this dissertation, I introduce Evolutionary Model Discovery, a novel framework for automated causal inference, which treats such artificial societies as sandboxes for rule discovery and causal factor importance evaluation. Evolutionary Model Discovery consists of two major phases. Firstly, a rule of interest of a given agent-based model is genetically programmed with combinations of hypothesized factors, attempting to find rules which enable the agent-based model to more closely mimic real-world phenomena. Secondly, the data produced through genetic programming, regarding the correspondence of factor presence in the rule to fitness, is used to train a random forest regressor for importance evaluation. Besides its scientific contributions, this work has also led to the contribution of two Python open-source software libraries for high performance computing with NetLogo, Evolutionary Model Discovery and NL4Py. The results of applying Evolutionary Model Discovery for the causal inference of three very different cases of human social behavior are discussed, revisiting the rules underlying two widely studied models in the literature, the Artificial Anasazi and Schelling\u27s Segregation, and an ensemble model of diffusion of information and information overload. First, previously unconsidered factors driving the socio-agricultural behavior of an ancient Pueblo society are discovered, assisting in the construction of a more robust and accurate version of the Artificial Anasazi model. Second, factors that contribute to the coexistence of mixed patterns of segregation and integration are discovered on a recent extension of Schelling\u27s Segregation model. Finally, causal factors important to the prioritization of social media notifications under loss of attention due to information overload are discovered on an ensemble of a model of Extended Working Memory and the Multi-Action Cascade Model of conversation

1993 Annual report on scientific programs: A broad research program on the sciences of complexity

This report provides a summary of many of the research projects completed by the Santa Fe Institute (SFI) during 1993. These research efforts continue to focus on two general areas: the study of, and search for, underlying scientific principles governing complex adaptive systems, and the exploration of new theories of computation that incorporate natural mechanisms of adaptation (mutation, genetics, evolution)

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp