Artificial and Computational Intelligence in Games (Dagstuhl Seminar 12191)

This report documents the program and the outcomes of Dagstuhl Seminar 12191 "Artificial and Computational Intelligence in Games". The aim for the seminar was to bring together creative experts in an intensive meeting with the common goals of gaining a deeper understanding of various aspects of artificial and computational intelligence in games, to help identify the main challenges in game AI research and the most promising venues to deal with them. This was accomplished mainly by means of workgroups on 14 different topics (ranging from search, learning, and modeling to architectures, narratives, and evaluation), and plenary discussions on the results of the workgroups. This report presents the conclusions that each of the workgroups reached. We also added short descriptions of the few talks that were unrelated to any of the workgroups

Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon

Fast implementations of NMPC are important when addressing real-time control of systems exhibiting features like fast dynamics, large dimension, and long prediction horizon, as in such situations the computational burden of the NMPC may limit the achievable control bandwidth. For that purpose, this thesis addresses both algorithms and applications. First, fast NMPC algorithms for controlling continuous-time dynamic systems using a long prediction horizon have been developed. A bridge between linear and nonlinear MPC is built using partial linearizations or sensitivity update. In order to update the sensitivities only when necessary, a Curvature-like measure of nonlinearity (CMoN) for dynamic systems has been introduced and applied to existing NMPC algorithms. Based on CMoN, intuitive and advanced updating logic have been developed for different numerical and control performance. Thus, the CMoN, together with the updating logic, formulates a partial sensitivity updating scheme for fast NMPC, named CMoN-RTI. Simulation examples are used to demonstrate the effectiveness and efficiency of CMoN-RTI. In addition, a rigorous analysis on the optimality and local convergence of CMoN-RTI is given and illustrated using numerical examples. Partial condensing algorithms have been developed when using the proposed partial sensitivity update scheme. The computational complexity has been reduced since part of the condensing information are exploited from previous sampling instants. A sensitivity updating logic together with partial condensing is proposed with a complexity linear in prediction length, leading to a speed up by a factor of ten. Partial matrix factorization algorithms are also proposed to exploit partial sensitivity update. By applying splitting methods to multi-stage problems, only part of the resulting KKT system need to be updated, which is computationally dominant in on-line optimization. Significant improvement has been proved by giving floating point operations (flops). Second, efficient implementations of NMPC have been achieved by developing a Matlab based package named MATMPC. MATMPC has two working modes: the one completely relies on Matlab and the other employs the MATLAB C language API. The advantages of MATMPC are that algorithms are easy to develop and debug thanks to Matlab, and libraries and toolboxes from Matlab can be directly used. When working in the second mode, the computational efficiency of MATMPC is comparable with those software using optimized code generation. Real-time implementations are achieved for a nine degree of freedom dynamic driving simulator and for multi-sensory motion cueing with active seat

Synthesis of a large communications aperture using small antennas

In this report we compare the cost of an array of small antennas to that of a single large antenna assuming both the array and single large antenna have equal performance and availability. The single large antenna is taken to be one of the 70-m antennas of the Deep Space Network. The cost of the array is estimated as a function of the array element diameter for three different values of system noise temperature corresponding to three different packaging schemes for the first amplifier. Array elements are taken to be fully steerable paraboloids and their cost estimates were obtained from commercial vendors. Array loss mechanisms and calibration problems are discussed. For array elements in the range 3 - 35 m there is no minimum in the cost versus diameter curve for the three system temperatures that were studied

Deriving a mathematical framework for data-driven analyses of immune cell dynamics

Zelluläre Entscheidungen, wie z. B. die Differenzierung von T-Helferzellen (Th-Zellen) in spezialisierte Effektorlinien, haben großen Einfluss auf die Spezifität von Immunreaktionen. Solche Reaktionen sind das Ergebnis eines komplexen Zusammenspiels einzelner Zellen, die über kleine Signalmoleküle, so genannte Zytokine, kommunizieren. Die hohe Anzahl der Komponenten, sowie deren komplizierte und oft nichtlineare Interaktionen erschweren dabei die Vorhersage, wie bestimmte zelluläre Reaktionen erzeugt werden. Aus diesem Grund sind die globalen Auswirkungen der gezielten Beeinflussung einzelner Zellen oder spezifischer Signalwege nur unzureichend verstanden. So wirken beispielsweise etablierte Behandlungen von Autoimmunkrankheiten oft nur bei einem Teil der Patienten. Durch Einzelzellmethoden wie Live-Cell-Imaging, Massenzytometrie und Einzelzellsequenzierung, können Immunzellen heutzutage quantitativ auf mehreren Ebenen charakterisiert werden. Diese Ansammlung quantitativer Daten erlaubt die Formulierung datengetriebener Modelle zur Vorhersage von zellulären Entscheidungen, allerdings fehlen in vielen Fällen Methoden, um die verschiedenen Daten auf geeignete Weise zu integrieren und zu annotieren. Die vorliegende Arbeit befasst sich mit quantitativen Modellformulierungen für die Entscheidungsfindung von Zellen im Immunsystem mit dem Schwerpunkt auf Lymphozytenproliferation, -differenzierung und -tod.Cellular decisions, such as the differentiation of T helper (Th) cells into specialized effector lineages, largely impact the direction of immune responses. Such population-level responses are the result of a complex interplay of individual cells which communicate via small signaling molecules called cytokines. The system's complexity, stemming not only from the number of components but also from their intricate and oftentimes non-linear interactions, makes it difficult to develop intuition for how cellular responses are actually generated. Not surprisingly, the global effects of targeting individual cells or specific signaling pathways through perturbations are poorly understood. For instance, common treatments of autoimmune diseases often work for some patients, but not for others. Recently developed methods such as live-cell imaging, mass cytometry and single-cell sequencing now enable quantitative characterization of individual immune cells. This accumulating wealth of quantitative data has laid the basis to derive predictive, data-driven models of immune cell behavior, but in many cases, methods to integrate and annotate the data in a way suitable for model formulation are missing. In this thesis, quantitative workflows and methods are introduced that allow to formulate data-driven models of immune cell decision-making with a particular focus on lymphocyte proliferation, differentiation and death

Development of the multi-scale dynamic modeling techniques for multi-modal brain recordings

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied in the micro- to meso-scale in studying the connections between the dynamical patterns and the behaviors, the investigation of the neural population dynamics is mainly limited by the single-scale analysis. My goal is to develop a multi-scale dynamical model for the collective activity of neuronal populations. First, I introduced a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales (Chapter 2). The NBGNet not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. I also show that the NBGNet robustly predicts held-out data across a long time scale (two weeks) without retraining. The effective connectivity defined from the presented model agrees with the established neuroanatomical hierarchy of motor control in the literature. I then introduced the m̲ulti-s̲cale n̲eural d̲ynamics n̲eural o̲rdinary d̲ifferential e̲quation (msDyNODE) to uncover multiscale brain communications governing cognitive behaviors (Chapter 3). I demonstrated that msDyNODE successfully captured multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived casual interactions between recording channels and scales not only aligned well with the abstraction of the hierarchical anatomy of the mammalian nervous system but also exhibited behavioral dependences. This work offers a new approach for in depth mechanistic studies on the brain where simultaneous acquisition of multi-scale neural activity is available. While the traditional neuronal tracing method serves as gold standard to assess the neural connectivity, toxicity and the limited efficiency prevent it from serving as a reliable tool to validate the multi-scale connectivity. It has been demonstrated that dendritic spines increase or become bigger after long-term potentiation while decrease or become smaller after long-term depression. Thus, we hypothesized the existence of excitatory and inhibitory connectivity can be revealed by the dynamics of dendritic spines. Since the dendritic spines can be smaller than the Abbe diffraction limit (~200 nm), we need an in-vivo super-resolution microscopy to achieve our goal. As a super-resolution imaging method, stimulated emission depletion (STED) microscopy has unraveled fine intracellular structures and provided insights into nanoscale organizations in cells. Although image resolution can be further enhanced by continuously increasing the STED-beam power, the resulting photodamage and phototoxicity are major issues for real-world applications of STED microscopy. In Chapter 4, I demonstrated that, with 50% less STED-beam power, the STED image resolution can be improved up to 1.45-fold using the separation of photons by a lifetime tuning (SPLIT) scheme combined with a deep learning-based phasor analysis algorithm termed flimGANE (f̲luorescence l̲ifetime i̲m̲aging based on a g̲enerative a̲dversarial n̲e̲twork). This work offers a new approach for STED imaging in situations where only a limited photon budget is available. In summary, I emphasized that this is the first time we are able to infer the nonlinear multiscale interactions in the brain networks by our multi-scale dynamic modeling approach. This opens a window of opportunity that multi-scale connectivity can provide a mechanistic understanding of brain computations underlying behaviors or mental states.Biomedical Engineerin

On the Origin of Phenotypic Variation: Novel Technologies to Dissect Molecular Determinants of Phenotype

This thesis describes the conception, design, and development of novel computational tools, theoretical models, and experimental techniques applied to the dissection of molecular factors underlying phenotypic variation. The first part of my work is focused on finding rare genetic variants in pooled DNA samples, leading to the development of a novel set of algorithms, SNPseeker and SPLINTER, applied to next-generation sequencing data. The second part of my work describes the creation of a reporter system for DNA methylation for the purpose of dissecting the genetic contribution of tissue-specific patterns of DNA methylation across the genome. Finally the last part of my work is focused on understanding the basis of stochastic variation in gene expression with a focus on modeling and dissecting the relationship between single-cell protein variance and mean at a genome-wide scale

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

An information-theoretic approach to understanding the neural coding of relevant tactile features

Objective: Traditional theories in neuroscience state that tactile afferents present in the glabrous skin of the human hand encode tactile information following a submodality segregation strategy, meaning that each modality (eg. motion, vibration, shape, ... ) is encoded by a different afferent class. Modern theories suggest a submodality convergence instead, in which different afferent classes work together to capture information about the environment through tactile sense. Typically, studies involve electrophysiological recordings of tens of afferents. At the same time, the human hand is filled with around 17.000 afferents. In this thesis, we want to tackle the theoretical gap this poses. Specifically, we aim to address whether the peripheral nervous system relies on population coding to represent tactile information and whether such population coding enables us to disambiguate submodality convergence against the classical segregation. Approach: Understanding the encoding and flow of information in the nervous system is one of the main challenges of modern neuroscience. Neural signals are highly variable and may be non-linear. Moreover, there exist several candidate codes compatible with sensory and behavioral events. For example, they can rely on single cells or populations and also on rate or timing precision. Information-theoretic methods can capture non-linearities while being model independent, statistically robust, and mathematically well-grounded, becoming an ideal candidate to design pipelines for analyzing neural data. Despite information-theoretic methods being powerful for our objective, the vast majority of neural signals we can acquire from living systems makes analyses highly problem-specific. This is so because of the rich variety of biological processes that are involved (continuous, discrete, electrical, chemical, optical, ...). Main results: The first step towards solving the aforementioned challenges was to have a solid methodology we could trust and rely on. Consequently, the first deliverable from this thesis is a toolbox that gathers classical and state-of-the-art information-theoretic approaches and blends them with advanced machine learning tools to process and analyze neural data. Moreover, this toolbox also provides specific guidance on calcium imaging and electrophysiology analyses, encompassing both simulated and experimental data. We then designed an information-theoretic pipeline to analyze large-scale simulations of the tactile afferents that overcomes the current limitations of experimental studies in the field of touch and the peripheral nervous system. We dissected the importance of population coding for the different afferent classes, given their spatiotemporal dynamics. We also demonstrated that different afferent classes encode information simultaneously about very simple features, and that combining classes increases information levels, adding support to the submodality convergence theory. Significance: Fundamental knowledge about touch is essential both to design human-like robots exhibiting naturalistic exploration behavior and prostheses that can properly integrate and provide their user with relevant and useful information to interact with their environment. Demonstrating that the peripheral nervous system relies on heterogeneous population coding can change the designing paradigm of artificial systems, both in terms of which sensors to choose and which algorithms to use, especially in neuromorphic implementations