Refine
H-BRS Bibliography
- yes (30)
Departments, institutes and facilities
Document Type
- Conference Object (15)
- Article (5)
- Preprint (5)
- Report (3)
- Part of a Book (1)
- Doctoral Thesis (1)
Year of publication
Keywords
- Quality diversity (7)
- Bayesian optimization (3)
- Autoencoder (2)
- Compositional Pattern Producing Networks (2)
- Evolutionary Computation (2)
- Evolutionary computation (2)
- Generative Models (2)
- Lattice Boltzmann Method (2)
- Optimization (2)
- cellular automata (2)
- parametric (2)
- AI usage in sports (1)
- Artificial Intelligence (1)
- Clustering (1)
- Co-creative processes (1)
- Computational Fluid Dynamics (1)
- Computational chemistry (1)
- Computational creativity (1)
- Computational fluid dynamics (1)
- Computational modeling (1)
- Dimensionality reduction (1)
- Divergent optimization (1)
- Domestic robotics (1)
- Efficiency (1)
- Electric mobility (1)
- Evolutionary optimization (1)
- Feature Model (1)
- Fusion (1)
- Genetic algorithm (1)
- Hochleistungssport (1)
- Human-Computer Interaction (1)
- Informationsgewinnung (1)
- Informationsverarbeitung (1)
- Künstliche Intelligenz (1)
- Leistungsdiagnostik (1)
- Leistungssport (1)
- Lennard-Jones parameters (1)
- Machine learning (1)
- Maximal covering location problem (1)
- Methodik (1)
- Modalities (1)
- Multi-Solution Optimization (1)
- Multi-objective (1)
- Multi-objective optimization (1)
- Multi-stage (1)
- Multimodal (1)
- Multimodal optimization (1)
- Object recognition (1)
- Phenotypic niching (1)
- Quality Diversity (1)
- SMPA loop (1)
- Single-objective (1)
- Software (1)
- Spielanalyse (1)
- Surrogate models (1)
- Surrogate-assistance (1)
- Synergetik (1)
- Trainingssteuerung (1)
- Transparency (1)
- Variational Autoencoder (1)
- Wettkampfanalyse (1)
- activation function (1)
- bagging (1)
- bloat (1)
- design process (1)
- designing air flow (1)
- dimensionality reduction (1)
- diversity (1)
- elite sports (1)
- encoding, representation (1)
- evolutionary illumination (1)
- explainable AI (1)
- feature discovery (1)
- force field (1)
- genetic neutrality (1)
- heterogeneous networks (1)
- ideation (1)
- local optimization (1)
- multi-objective optimization (1)
- multi-solution optimization (1)
- multimodal optimization (1)
- multiscale parameterization (1)
- neuroevolution (1)
- non-linear projection (1)
- objective function (1)
- phenotypic diversity (1)
- phenotypic feature (1)
- phenotypic niching (1)
- pre-optimization (1)
- prototype theory (1)
- quality-diversity (1)
- rds encoding (1)
- regression (1)
- representation (1)
- surrogate assisted phenotypic niching (1)
- surrogate modeling (1)
- surrogate models (1)
- weighting factors (1)
- wind nuisance (1)
- wind nuisance threshold (1)
Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force field parameters need to be adjusted. This force field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties’ errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the overall error is reduced. One interesting outcome is a large variety in the resulting optimized force field parameters (FFParams) and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.
The representation, or encoding, utilized in evolutionary algorithms has a substantial effect on their performance. Examination of the suitability of widely used representations for quality diversity optimization (QD) in robotic domains has yielded inconsistent results regarding the most appropriate encoding method. Given the domain-dependent nature of QD, additional evidence from other domains is necessary. This study compares the impact of several representations, including direct encoding, a dictionary-based representation, parametric encoding, compositional pattern producing networks, and cellular automata, on the generation of voxelized meshes in an architecture setting. The results reveal that some indirect encodings outperform direct encodings and can generate more diverse solution sets, especially when considering full phenotypic diversity. The paper introduces a multi-encoding QD approach that incorporates all evaluated representations in the same archive. Species of encodings compete on the basis of phenotypic features, leading to an approach that demonstrates similar performance to the best single-encoding QD approach. This is noteworthy, as it does not always require the contribution of the best-performing single encoding.
The representation, or encoding, utilized in evolutionary algorithms has a substantial effect on their performance. Examination of the suitability of widely used representations for quality diversity optimization (QD) in robotic domains has yielded inconsistent results regarding the most appropriate encoding method. Given the domain-dependent nature of QD, additional evidence from other domains is necessary. This study compares the impact of several representations, including direct encoding, a dictionary-based representation, parametric encoding, compositional pattern producing networks, and cellular automata, on the generation of voxelized meshes in an architecture setting. The results reveal that some indirect encodings outperform direct encodings and can generate more diverse solution sets, especially when considering full phenotypic diversity. The paper introduces a multi-encoding QD approach that incorporates all evaluated representations in the same archive. Species of encodings compete on the basis of phenotypic features, leading to an approach that demonstrates similar performance to the best single-encoding QD approach. This is noteworthy, as it does not always require the contribution of the best-performing single encoding.
Quality diversity algorithms can be used to efficiently create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing 100.000s of evaluations. Even with the assistance of surrogate models, quality diversity needs 100s or even 1000s of evaluations, which can make it use infeasible. In this study we try to tackle this problem by using a pre-optimization strategy on a lower-dimensional optimization problem and then map the solutions to a higher-dimensional case. For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints. For a diverse set of building designs, by sampling the space of 2D footprints with a quality diversity algorithm, a predictive model can be trained that is more accurate than when trained on a set of footprints that were selected with a space-filling algorithm like the Sobol sequence. Simulating only 16 buildings in 3D, a set of 1024 building designs with low predicted wind nuisance is created. We show that we can produce better machine learning models by producing training data with quality diversity instead of using common sampling techniques. The method can bootstrap generative design in a computationally expensive 3D domain and allow engineers to sweep the design space, understanding wind nuisance in early design phases.
This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of variation are unknown or too complex to encode manually, generative models can provide a learned latent space to approximate these factors. When used as a search space, however, the range and diversity of possible outputs are limited to the expressivity and generative capabilities of the learned model. We compare the output diversity of a quality diversity evolutionary search performed in two different search spaces: 1) a predefined parameterized space and 2) the latent space of a variational autoencoder model. We find that the search on an explicit parametric encoding creates more diverse artifact sets than searching the latent space. A learned model is better at interpolating between known data points than at extrapolating or expanding towards unseen examples. We recommend using a generative model's latent space primarily to measure similarity between artifacts rather than for search and generation. Whenever a parametric encoding is obtainable, it should be preferred over a learned representation as it produces a higher diversity of solutions.
In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines. Possible implementations of such processes are presented using novel algorithms that perform divergent search to feed the users' intuition with many examples of high quality solutions, allowing them to take influence interactively. The machine feeds and reflects upon human intuition, combining both what is possible and preferred. The machine model and the divergent optimization algorithms are the motor behind this co-creative process, in which machine and users co-create and interactively choose branches of an ad hoc hierarchical decomposition of the solution space.
The proposed co-creative process consists of several elements: a formal model for interactive co-creative processes, evolutionary divergent search, diversity and similarity, data-driven methods to discover diversity, limitations of artificial creative agents, matters of efficiency in behavioral and morphological modeling, visualization, a connection to prototype theory, and methods to allow users to influence artificial creative agents. This thesis helps putting the human back into the design loop in generative AI and optimization.