Cellular Automata

Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented

Mapping boundaries of generative systems for design synthesis

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2007.Page 123 blank.Includes bibliographical references (p. 121-122).Architects have been experimenting with generative systems for design without a clear reference or theory of what, why or how to deal with such systems. In this thesis I argue for three points. The first is that generative systems in architecture are implemented at a skin-deep level as they are only used to synthesize form within confined domains. The second is that such systems can be only implemented if a design formalism is defined. The third is that generative systems can be deeper integrated within a design process if they were coupled with performance-based evaluation methods. These arguments are discussed in four chapters: 1- Introduction: a panoramic view of generative systems in architecture and in. computing mapping their occurrences and implementations. 2- Generative Systems for Design: highlights on integrating generative systems in architecture design processes; and discussions on six generative systems including: Algorithmic, Parametrics, L-systems, Cellular Automata, Fractals and Shape Grammars. 3- Provisional taxonomy: A summery table of systems properties and a classification of generative systems properties as discussed in the previous chapter 4- Conclusion: comments and explanations on why such systems are simplicity implemented within design.by Maher El-Khaldi.S.M

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

Active Learning for Reducing Labeling Effort in Text Classification Tasks

Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on AL in a text classification setting and next to none has involved the more recent, state-of-the-art Natural Language Processing (NLP) models. Here, we present an empirical study that compares different uncertainty-based algorithms with BERT$_{base}$ as the used classifier. We evaluate the algorithms on two NLP classification datasets: Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore heuristics that aim to solve presupposed problems of uncertainty-based AL; namely, that it is unscalable and that it is prone to selecting outliers. Furthermore, we explore the influence of the query-pool size on the performance of AL. Whereas it was found that the proposed heuristics for AL did not improve performance of AL; our results show that using uncertainty-based AL with BERT$_{base}$ outperforms random sampling of data. This difference in performance can decrease as the query-pool size gets larger.Comment: Accepted as a conference paper at the joint 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021). This camera-ready version submitted to BNAIC/BENELEARN, adds several improvements including a more thorough discussion of related work plus an extended discussion section. 28 pages including references and appendice

The Use of Games and Crowdsourcing for the Fabrication-aware Design of Residential Buildings

State-of-the-art participatory design acknowledges the true, ill-defined nature of design problems, taking into account stakeholders' values and preferences. However, it overburdens the architect, who has to synthesize far more constraints into a one-of-a-kind design. Generative Design promises to equip architects with great power to standardize and systemize the design process. However, the common trap of generative design is trying to treat architecture simply as a tame problem. In this work, I investigate the use of games and crowdsourcing in architecture through two sets of explorative questions. First, if everyone can participate in the network-enabled creation of the built environment, what role will they play? And what tools will they need to enable them? And second, if anyone can use digital fabrication to build any building, how will we design it? What design paradigms will govern this process? I present a map of design paradigms that lie at the intersections of Participatory Design, Generative Design, Game Design, and Crowd Wisdom. In four case studies, I explore techniques to employ the practices from the four fields in the service of architecture. Generative Design can lower the difficulty of the challenge to design by automating a large portion of the work. A newly formulated, unified taxonomy of generative design across the disciplines of architecture, computer science, and computer games builds the base for the use of algorithms in the case studies. The work introduces Playable Voxel-Shape Grammars, a new type of generative technique. It enables Game Design to guide participants through a series of challenges, effectively increasing their skills by helping them understand the underlying principles of the design task at hand. The use of crowdsourcing in architecture can mean thousands of architects creating content for a generative design system, to expand and open up its design space. Crowdsourcing can also be about millions of people online creating designs that an architect or a homeowner can refer to increase their understanding of the complex issues at hand in a given design project and for better decision making. At the same time, game design in architecture helps find the balance between algorithmically exploring pre-defined design alternatives and open-ended, free creativity. The research reveals a layered structure of entry points for crowd-contributed content as well as the granular nature of authorship among four different roles: non-expert stakeholders, architects, the crowd, and the tool-makers

Spatio-temporal logics for verification and control of networked systems

Emergent behaviors in networks of locally interacting dynamical systems have been a topic of great interest in recent years. As the complexity of these systems increases, so does the range of emergent properties that they exhibit. Due to recent developments in areas such as synthetic biology and multi-agent robotics, there has been a growing necessity for a formal and automated framework for studying global behaviors in such networks. We propose a formal methods approach for describing, verifying, and synthesizing complex spatial and temporal network properties. Two novel logics are introduced in the first part of this dissertation: Tree Spatial Superposition Logic (TSSL) and Spatial Temporal Logic (SpaTeL). The former is a purely spatial logic capable of formally describing global spatial patterns. The latter is a temporal extension of TSSL and is ideal for expressing how patterns evolve over time. We demonstrate how machine learning techniques can be utilized to learn logical descriptors from labeled and unlabeled system outputs. Moreover, these logics are equipped with quantitative semantics and thus provide a metric for distance to satisfaction for randomly generated system trajectories. We illustrate how this metric is used in a statistical model checking framework for verification of networks of stochastic systems. The parameter synthesis problem is considered in the second part, where the goal is to determine static system parameters that lead to the emergence of desired global behaviors. We use quantitative semantics to formulate optimization procedures with the purpose of tuning system inputs. Particle swarm optimization is employed to efficiently solve these optimization problems, and the efficacy of this framework is demonstrated in two applications: biological cell networks and smart power grids. The focus of the third part is the control synthesis problem, where the objective is to find time-varying control strategies. We propose two approaches to solve this problem: an exact solution based on mixed integer linear programming, and an approximate solution based on gradient descent. These algorithms are not restricted to the logics introduced in this dissertation and can be applied to other existing logics in the literature. Finally, the capabilities of our framework are shown in the context of multi-agent robotics and robotic swarms

Microscopic and macroscopic models for pedestrian crowds

This thesis is concerned with microscopic and macroscopic models for pedes- trian crowds. In the first chapter, we consider pedestrians exit choices and model human behaviour in an evacuation process. Two microscopic models, discrete and continuous, are studied in this chapter. The former is a cellular automaton model and the latter is a social force model. Different numerical test cases are investigated and their results are compared. In chapter 2, a hierarchy of models for pedestrian flows is derived. We examine a detailed microscopic social force model coupled to a local visibil- ity model on the one hand and macroscopic models including the interaction forces and a local visibility term on the other hand. Particle methods are applied to solve these models. Numerical experiments are explored and com- pared on the microscopic as well as on the hydrodynamic and scalar models

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity