L-systems in Geometric Modeling

We show that parametric context-sensitive L-systems with affine geometry interpretation provide a succinct description of some of the most fundamental algorithms of geometric modeling of curves. Examples include the Lane-Riesenfeld algorithm for generating B-splines, the de Casteljau algorithm for generating Bezier curves, and their extensions to rational curves. Our results generalize the previously reported geometric-modeling applications of L-systems, which were limited to subdivision curves.Comment: In Proceedings DCFS 2010, arXiv:1008.127

Creating Complex Building Blocks through Generative Representation

One of the main limitations for the functional scalability of computer automated design systems is the representation used for encoding designs. Using computer programs as an analogy, representations can be thought of as having the properties of combination, control-flow and abstraction. We define generative representations as those which have the ability to reuse elements in an encoding through either iteration or abstraction and argue that reuse improves functional scalability by allowing the representation to construct buildingblocks and capture design dependencies. Next we describe GENRE, an evolutionary design system for evolving a variety of different types of designs. Using this system we compare the generative representation against a non-generative representation on evolving tables and robots and show that designs evolved with the generative representation have higher fitness than designs created with the non-generative representation. Further, we show that designs evolved with the generative representation are constructed in a modular way through the reuse of discovered building blocks

Creating Data Art: Authentic Learning and Visualisation Exhibition

We present an authentic learning task designed for computing students, centred on the creation of data-art visualisations from chosen datasets for a public exhibition. This exhibition was showcased in the cinema foyer for two weeks in June, providing a real-world platform for students to display their work. Over the course of two years, we implemented this active learning task with two different cohorts of students. In this paper, we share our experiences and insights from these activities, highlighting the impact on student engagement and learning outcomes. We also provide a detailed description of the seven individual tasks that learners must perform: topic and data selection and analysis, research and art inspiration, design conceptualisation, proposed solution, visualisation creation, exhibition curation, and reflection. By integrating these tasks, students not only develop technical skills but also gain practical experience in presenting their work to a public audience, bridging the gap between academic learning and professional practice

Virtual Forestry Generation: Evaluating Models for Tree Placement in Games

A handful of approaches have been previously proposed to generate procedurally virtual forestry for virtual worlds and computer games, including plant growth models and point distribution methods. However, there has been no evaluation to date which assesses how effective these algorithms are at modelling real-world phenomena. In this paper, we tackle this issue by evaluating three algorithms used in the generation of virtual forests—a randomly uniform point distribution method (control), a plant competition model, and an iterative random point distribution technique. Our results show that a plant competition model generated more believable content when viewed from an aerial perspective. Interestingly, however, we also found that a randomly uniform point distribution method produced forestry which was rated higher in playability and photorealism, when viewed from a first-person perspective. We conclude that the objective of the game designer is important to consider when selecting an algorithm to generate forestry, as the algorithms produce forestry that is perceived differently

Learning From Geometry In Learning For Tactical And Strategic Decision Domains

Artificial neural networks (ANNs) are an abstraction of the low-level architecture of biological brains that are often applied in general problem solving and function approximation. Neuroevolution (NE), i.e. the evolution of ANNs, has proven effective at solving problems in a variety of domains. Information from the domain is input to the ANN, which outputs its desired actions. This dissertation presents a new NE algorithm called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT), based on a novel indirect encoding of ANNs. The key insight in HyperNEAT is to make the algorithm aware of the geometry in which the ANNs are embedded and thereby exploit such domain geometry to evolve ANNs more effectively. The dissertation focuses on applying HyperNEAT to tactical and strategic decision domains. These domains involve simultaneously considering short-term tactics while also balancing long-term strategies. Board games such as checkers and Go are canonical examples of such domains; however, they also include real-time strategy games and military scenarios. The dissertation details three proposed extensions to HyperNEAT designed to work in tactical and strategic decision domains. The first is an action selector ANN architecture that allows the ANN to indicate its judgements on every possible action all at once. The second technique is called substrate extrapolation. It allows learning basic concepts at a low resolution, and then increasing the resolution to learn more advanced concepts. The iii final extension is geometric game-tree pruning, whereby HyperNEAT can endow the ANN the ability to focus on specific areas of a domain (such as a checkers board) that deserve more inspection. The culminating contribution is to demonstrate the ability of HyperNEAT with these extensions to play Go, a most challenging game for artificial intelligence, by combining HyperNEAT with UC

Multiagent Learning Through Indirect Encoding

Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding

Parallel fluid dynamics for the film and animation industries

Includes bibliographical references (leaves 142-149).The creation of automated fluid effects for film and media using computer simulations is popular, as artist time is reduced and greater realism can be achieved through the use of numerical simulation of physical equations. The fluid effects in today’s films and animations have large scenes with high detail requirements. With these requirements, the time taken by such automated approaches is large. To solve this, cluster environments making use of hundreds or more CPUs have been used. This overcomes the processing power and memory limitations of a single computer and allows very large scenes to be created. One of the newer methods for fluid simulation is the Lattice Boltzmann Method (LBM). This is a cellular automata type of algorithm, which parallelizes easily. An important part of the process of parallelization is load balancing; the distribution of computation amongst the available computing resources in the cluster. To date, the parallelization of the Lattice Boltzmann method only makes use of static load balancing. Instead, it is possible to make use of dynamic load balancing, which adjusts the computation distribution as the simulation progresses. Here, we investigate the use of the LBM in conjunction with a Volume of Fluid (VOF) surface representation in a parallel environment with the aim of producing large scale scenes for the film and animation industries. The VOF method tracks mass exchange between cells of the LBM. In particular, we implement the new dynamic load balancing algorithm to improve the efficiency of the fluid simulation using this method. Fluid scenes from films and animations have two important requirements: the amount of detail and the spatial resolution of the fluid. These aspects of the VOF LBM are explored by considering the time for scene creation using a single and multi-CPU implementation of the method. The scalability of the method is studied by plotting the run time, speedup and efficiency of scene creation against the number of CPUs. From such plots, an estimate is obtained of the feasibility of creating scenes of a giving level of detail. Such estimates enable the recommendation of architectures for creation of specific scenes. Using a parallel implementation of the VOF LBM method we successfully create large scenes with great detail. In general, considering the significant amounts of communication required for the parallel method, it is shown to scale well, favouring scenes with greater detail. The scalability studies show that the new dynamic load balancing algorithm improves the efficiency of the parallel implementation, but only when using lower number of CPUs. In fact, for larger number of CPUs, the dynamic algorithm reduces the efficiency. We hypothesise the latter effect can be removed by making using of centralized load balancing decision instead of the current decentralized approach. The use of a cluster comprising of 200 CPUs is recommended for the production of large scenes of a grid size 6003 in a reasonable time frame

Enabling and Measuring Complexity in Evolving Designs using Generative Representations for Artificial Architecture

As the complexity of evolutionary design problems grow, so too must the quality of solutions scale to that complexity. In this research, we develop a genetic programming system with individuals encoded as tree-based generative representations to address scalability. This system is capable of multi-objective evaluation using a ranked sum scoring strategy. We examine Hornby's features and measures of modularity, reuse and hierarchy in evolutionary design problems. Experiments are carried out, using the system to generate three-dimensional forms, and analyses of feature characteristics such as modularity, reuse and hierarchy were performed. This work expands on that of Hornby's, by examining a new and more difficult problem domain. The results from these experiments show that individuals encoded with those three features performed best overall. It is also seen, that the measures of complexity conform to the results of Hornby. Moving forward with only this best performing encoding, the system was applied to the generation of three-dimensional external building architecture. One objective considered was passive solar performance, in which the system was challenged with generating forms that optimize exposure to the Sun. The results from these and other experiments satisfied the requirements. The system was shown to scale well to the architectural problems studied

Modelagem de L-sistemas no âmbito computacional do GeoGebra

Through mathematical and computational modeling, objects from mathematics theories are developed via discrete dynamic systems known as Lindenmayer systems, or L-systems. Among the objects that has been studied and modeled are fractals (Cantor’s Set and Koch’s Snowflake), space-Filling, self-Avoiding, Simple, and self-Similar (FASS curves) (Peano, Hilber and Moore Curves, Dragon’s Curve), Lindenmayer axial trees, and tessellations in the plane R2, focusing on the well-known Penrose tessellation. With the help of software GeoGebra using Turtle Graphics and JavaScript, it has been possible to create a dynamic visualization, with simulations, in this computational environment. Another aspect of the visualization is provided by the interaction a user has via the Applets that have been produced in GeoGebra. And finally in the appendix of this manuscript of the PDF file there are samples of animations developed using the \usepackage{animate} available in LATEX.Trabalho de Conclusão de Curso (Graduação)Através de modelagens matemáticas e computacionais desenvolvem-se objetos matemáticos via sistemas dinâmicos discretos conhecidos como sistemas de Lindenmayer ou L-sistemas. Entre os objetos estudados e modelados encontram-se fractais (Conjunto de Cantor e Floco de Neve de Koch), curvas de preenchimento espacial, sem auto-interseções, contínuas, e auto-similares (Curvas de Peano, Hilber e Moore, Curva do Dragão), árvores axiais de Lindenmayer, e tesselações no plano R2, com enfoque na conhecida tesselação de Penrose. Com o excepcional auxílio do software GeoGebra em conjunto com suas ferramentas poderosas de codificação, a tartaruga, utlizando a dinâmica do Turtle Graphics e o JavaScript, têm sido possíveis a visualização das dinâmicas com simulações neste ambiente computacional. Outro aspecto da visualização das modelagens é fornecido pela interação com um usuário via os Applets produzidos com as dinâmicas no GeoGebra. Finalizando com animações desenvolvidas através do software LATEX e seu pacote \usepackage{animate} que, se encontram no apêndice deste manuscrito e são mostradas no arquivo de formato PDF