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"Level design (Computer science)"
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Procedural generation in game design
Making a game can be an intensive process, and if not planned accurately can easily run over budget. The use of procedural generation in game design can help with the intricate and multifarious aspects of game development; thus facilitating cost reduction. This form of development enables games to create their play areas, objects and stories based on a set of rules, rather than relying on the developer to handcraft each element individually. Readers will learn to create randomised maps, weave accidental plotlines, and manage complex systems that are prone to unpredictable behaviour. The book offers a wide collection of chapters from various experts that cover the implementation and enactment of procedural generation in games.
Book
A survey on large language model based autonomous agents
Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field.
Journal Article
Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape
The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.
Journal Article
A review on feature-mapping methods for structural optimization
In this review we identify a new category of methods for implementing and solving structural optimization problems that has emerged over the last 20 years, which we propose to call
feature-mapping methods
. The two defining aspects of these methods are that the design is parameterized by a high-level geometric description and that features are mapped onto a non-body-fitted mesh for analysis. One motivation for using these methods is to gain better control over the geometry to, for example, facilitate imposing direct constraints on geometric features, while avoiding issues with re-meshing. The review starts by providing some key definitions and then examines the ingredients that these methods use to map geometric features onto a fixed mesh. One of these ingredients corresponds to the mechanism for mapping the geometry of a single feature onto a fixed analysis grid, from which an ersatz material or an immersed-boundary approach is used for the analysis. For the former case, which we refer to as the pseudo-density approach, a test problem is formulated to investigate aspects of the material interpolation, boundary smoothing, and numerical integration. We also review other ingredients of feature-mapping techniques, including approaches for combining features (which are required to perform topology optimization) and methods for imposing a minimum separation distance among features. A literature review of feature-mapping methods is provided for shape optimization, combined feature/free-form optimization, and topology optimization. Finally, we discuss potential future research directions for feature-mapping methods.
Journal Article
Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization
Introducing a novel meta-heuristic optimization algorithm, the Flood Algorithm (FLA) draws inspiration from the intricate movement and flow patterns of water masses during flooding events in river basins. FLA mathematically models key phenomena such as the movement of water toward slopes, flow rates over time, soil permeability effects, and periodic increases and decreases in water levels from precipitation and losses. Leveraging these models, the algorithm guides the movement and evolution of a population of potential solutions toward enhanced optimality. The algorithm endeavors to establish an appropriate correlation between the fundamental aspects of natural flood events and the optimization process. Its formulation and working mechanism are described in detail. It operates in two main phases—a regular movement phase, where the population moves naturally toward current best solutions, and a flooding phase, which introduces random disturbances to increase diversity. New solutions are periodically introduced while weaker ones are removed, mirroring the natural cycles of water levels. FLA’s effectiveness is demonstrated through its application on well-known benchmark optimization problems and engineering design problems. Extensive comparisons have been carried out on CEC2005 functions using 16 algorithms in both basic and enhanced modes, as well as on CEC2014 functions with dimensions 30, 50, and 100 using a total of 20 other algorithms. These rigorous studies unequivocally confirm the robustness and strength of the proposed algorithm. Furthermore, the algorithm's performance on 12 constrained engineering problems demonstrates its ability to tackle real-world challenges. The FLA’s source code is publicly available at
https://www.optim-app.com/projects/fla
.
Journal Article
Hopfield neural network with multi-scroll attractors and application in image encryption
Hopfield neural networks are favored by academia and industrial fields due to their abundant dynamics. In this paper, the dynamical behavior of a small Hopfield neural network (HNN) simultaneously stimulated by electromagnetic radiation and multi-level-logic pulse is investigated. Firstly, a modified HNN with three neurons is presented by selecting appropriate synapse weight coefficients. And the system model of the HNN under electromagnetic radiation and an electrical pulse is constructed. Then its equilibrium stabilities and nonlinear dynamical phenomena are analyzed by using numerical analysis methods including phase portraits, Lyapunov exponents, and bifurcation diagrams. The research results show that the neural network affected by electromagnetic radiation and a multi-level-logic pulse signal can generate chaotic multi-scroll attractors, which has not been observed in the previous investigation for the Hopfield-type neural networks. In addition, the number of the scroll can be easily changed by adjusting the electrical pulse signal. Circuit simulations based on the designed neural network circuit are carried out to confirm the numerical simulations. Finally, an HNN-based image encryption scheme is designed from the perspective of engineering applications. Performance evaluations demonstrate that the proposed image cryptosystem has good security.
Journal Article
A survey of structural and multidisciplinary continuum topology optimization: post 2000
Topology optimization is the process of determining the optimal layout of material and connectivity inside a design domain. This paper surveys topology optimization of continuum structures from the year 2000 to 2012. It focuses on new developments, improvements, and applications of finite element-based topology optimization, which include a maturation of classical methods, a broadening in the scope of the field, and the introduction of new methods for multiphysics problems. Four different types of topology optimization are reviewed: (1) density-based methods, which include the popular Solid Isotropic Material with Penalization (SIMP) technique, (2) hard-kill methods, including Evolutionary Structural Optimization (ESO), (3) boundary variation methods (level set and phase field), and (4) a new biologically inspired method based on cellular division rules. We hope that this survey will provide an update of the recent advances and novel applications of popular methods, provide exposure to lesser known, yet promising, techniques, and serve as a resource for those new to the field. The presentation of each method’s focuses on new developments and novel applications.
Journal Article