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Statins are a group of hypolipidemic drugs that act by competitive inhibition of the HMGR enzyme. They are generally considered effective and safe but claimed to have side effects on skeletal muscles. A molecular side effect of statins is the block of terpene biosynthesis and hence of dolichol involved in N-glycosylation and O-mannosylation of proteins. Defects in O-mannosylation lead to α-dystroglycan (α-DG) hypoglycosylation and a series of hereditary dystroglycanopathies. The current project aims to get insight into molecular pathomechanisms induced by statins in mammalian muscle cells and to unravel a potential link between these effects and statin-induced decreases of α-DG O-mannosylation. The study was based on mass spectrometric proteomics supported by western blot analysis to reveal Rosuvastatin effects on cellular pathways under high (micromolar) or low (nanomolar) conditions. Differential proteomics revealed higher statin effects on muscle cell function in micromolar than nanomolar concentration, which is reached in the patient’s plasma. We demonstrated distinct and partially overlapping patterns of fold-changed proteins under high and low statin conditions. Gene ontology term enrichment (GOTE) analyses of fold-changed proteins revealed cellular pathways related to muscle function and development are affected, even under low statin conditions, typically reached in the patient’s plasma during prophylactic medication.
This thesis investigates the benefit of rubrics for grading short answers using an active learning mechanism. Automating short answer grading using Natural Language Processing (NLP) is one of the active research areas in the education domain. This could save time for the evaluator and invest more time in preparing for the lecture. Most of the research on short answer grading was treated as a similarity task between reference and student answers. However, grading based on reference answers does not account for partial grades and does not provide feedback. Also, the grading is automatic that tries to replace the evaluator. Hence, using rubrics for short answer grading with active learning eliminates the drawbacks mentioned earlier.
Initially, the proposed approach is evaluated on the Mohler dataset, popularly used to benchmark the methodology. This phase is used to determine the parameters for the proposed approach. Therefore, the approach with the selected parameter exceeds the performance of current State-Of-The-Art (SOTA) methods resulting in the Pearson correlation value of 0.63 and Root Mean Square Error (RMSE) of 0.85. The proposed approach has surpassed the SOTA methods by almost 4%.
Finally, the benchmarked approach is used to grade the short answer based on rubrics instead of reference answers. The proposed approach evaluates short answers from Autonomous Mobile Robot (AMR) dataset to provide scores and feedback (formative assessment) based on the rubrics. The average performance of the dataset results in the Pearson correlation value of 0.61 and RMSE of 0.83. Thus, this research has proven that rubrics-based grading achieves formative assessment without compromising performance. In addition, the rubrics have the advantage of generalizability to all answers.
Modern engineering relies heavily on utilizing computer technologies. This is especially true for thermoplastic manufacturing, such as blow molding. A crucial milestone for digitalization is the continuous integration of data in unified or interoperable systems. While new simulation technologies are constantly developed, data management standards such as STEP fail at integrating them. On the other hand, industrial standards such as ”VMAP” manage to improve interoperability for Small and Medium-sized Enterprises. However, they do not provide Simulation Process and Data Management (SPDM) technologies. For SPDM integration of VMAP data, Ontology-Based Data Access is used to allow continuing the digital thread in custom semantic-based open-source solutions. An ontology of the database format (VMAP) was generated alongside an expandable knowledge graph of data access methods. A Python-based software architecture was developed, automatically using the semantic representations of database format and data access to query data and metadata within the VMAP file. The result is a software architecture template that can be adapted for other data standards and integrated into semantic data management systems. It allows semantic queries on simulation data down to element-wise resolution without integrating the whole model information. The architecture can instantiate a file in a knowledge graph, query a file’s metadatum and, in case it is not yet available, find a semantically represented process that allows the creation and instantiation of the required metadatum. See Figure 1. The results of this thesis can be expected to form a basis for semantic SPDM tools.
Machine learning-based solutions are frequently adapted in several applications that require big data in operations. The performance of a model that is deployed into operations is subject to degradation due to unanticipated changes in the flow of input data. Hence, monitoring data drift becomes essential to maintain the model’s desired performance. Based on the conducted review of the literature on drift detection, statistical hypothesis testing enables to investigate whether incoming data is drifting from training data. Because Maximum Mean Discrepancy (MMD) and Kolmogorov-Smirnov (KS) have shown to be reliable distance measures between multivariate distributions in the literature review, both were selected from several existing techniques for experimentation. For the scope of this work, the image classification use case was experimented with using the Stream-51 dataset. Based on the results from different drift experiments, both MMD and KS showed high Area Under Curve values. However, KS exhibited faster performance than MMD with fewer false positives. Furthermore, the results showed that using the pre-trained ResNet-18 for feature extraction maintained the high performance of the experimented drift detectors. Furthermore, the results showed that the performance of the drift detectors highly depends on the sample sizes of the reference (training) data and the test data that flow into the pipeline’s monitor. Finally, the results also showed that if the test data is a mixture of drifting and non-drifting data, the performance of the drift detectors does not depend on how the drifting data are scattered with the non-drifting ones, but rather their amount in the test set
This thesis proposes a multi-label classification approach using the Multimodal Transformer (MulT) [80] to perform multi-modal emotion categorization on a dataset of oral histories archived at the Haus der Geschichte (HdG). Prior uni-modal emotion classification experiments conducted on the novel HdG dataset provided less than satisfactory results. They uncovered issues such as class imbalance, ambiguities in emotion perception between annotators, and lack of representative training data to perform transfer learning [28]. Hence, the objectives of this thesis were to achieve better results by performing a multi-modal fusion and resolving the problems arising from class imbalance and annotator-induced bias in emotion perception. A further objective was to assess the quality of the novel HdG dataset and benchmark the results using SOTA techniques. Through a literature survey on the challenges, models, and datasets related to multi-modal emotion recognition, we created a methodology utilizing the MulT along with a multi-label classification approach. This approach produced a considerable improvement in the overall emotion recognition by obtaining an average AUC of 0.74 and Balanced-accuracy of 0.70 on the HdG dataset, which is comparable to state-of-the-art (SOTA) results on other datasets. In this manner, we were also able to benchmark the novel HdG dataset as well as introduce a novel multi-annotator learning approach to understand each annotator’s relative strengths and weaknesses for emotion perception. Our evaluation results highlight the potential benefits of the novel multi-annotator learning approach in improving overall performance by resolving the problems arising from annotator-induced bias and variation in the perception of emotions. Complementing these results, we performed a further qualitative analysis of the HdG annotations with a psychologist to study the ambiguities found in the annotations. We conclude that the ambiguities in annotations may have resulted from a combination of several socio-psychological factors and systemic issues associated with the process of creating these annotations. As these problems are also present in most multi-modal emotion recognition datasets, we conclude that the domain could benefit from a set of annotation guidelines to create standardized datasets.
Object detection concerns the classification and localization of objects in an image. To cope with changes in the environment, such as when new classes are added or a new domain is encountered, the detector needs to update itself with the new information while retaining knowledge learned in the past. Previous works have shown that training the detector solely on new data would produce a severe "forgetting" effect, in which the performance on past tasks deteriorates through each new learning phase. However, in many cases, storing and accessing past data is not possible due to privacy concerns or storage constraints. This project aims to investigate promising continual learning strategies for object detection without storing and accessing past training images and labels. We show that by utilizing the pseudo-background trick to deal with missing labels, and knowledge distillation to deal with missing data, the forgetting effect can be significantly reduced in both class-incremental and domain-incremental scenarios. Furthermore, an integration of a small latent replay buffer can result in a positive backward transfer, indicating the enhancement of past knowledge when new knowledge is learned.
As cameras are ubiquitous in autonomous systems, object detection is a crucial task. Object detectors are widely used in applications such as autonomous driving, healthcare, and robotics. Given an image, an object detector outputs both the bounding box coordinates as well as classification probabilities for each object detected. The state-of-the-art detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications in particular. It is therefore crucial to explain the reason behind each detector decision in order to gain user trust, enhance detector performance, and analyze their failure.
Previous work fails to explain as well as evaluate both bounding box and classification decisions individually for various detectors. Moreover, no tools explain each detector decision, evaluate the explanations, and also identify the reasons for detector failures. This restricts the flexibility to analyze detectors. The main contribution presented here is an open-source Detector Explanation Toolkit (DExT). It is used to explain the detector decisions, evaluate the explanations, and analyze detector errors. The detector decisions are explained visually by highlighting the image pixels that most influence a particular decision. The toolkit implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. To the author’s knowledge, this is the first work to conduct extensive qualitative and novel quantitative evaluations of different explanation methods across various detectors. The qualitative evaluation incorporates a visual analysis of the explanations carried out by the author as well as a human-centric evaluation. The human-centric evaluation includes a user study to understand user trust in the explanations generated across various explanation methods for different detectors. Four multi-object visualization methods are provided to merge the explanations of multiple objects detected in an image as well as the corresponding detector outputs in a single image. Finally, DExT implements the procedure to analyze detector failures using the formulated approach.
The visual analysis illustrates that the ability to explain a model is more dependent on the model itself than the actual ability of the explanation method. In addition, the explanations are affected by the object explained, the decision explained, detector architecture, training data labels, and model parameters. The results of the quantitative evaluation show that the Single Shot MultiBox Detector (SSD) is more faithfully explained compared to other detectors regardless of the explanation methods. In addition, a single explanation method cannot generate more faithful explanations than other methods for both the bounding box and the classification decision across different detectors. Both the quantitative and human-centric evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides more trustworthy explanations among selected methods across all detectors. Finally, a convex polygon-based multi-object visualization method provides more human-understandable visualization than other methods.
The author expects that DExT will motivate practitioners to evaluate object detectors from the interpretability perspective by explaining both bounding box and classification decisions.
This project focuses on object detection in dense volume data. There are several types of dense volume data, namely Computed Tomography (CT) scan, Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI). This work focuses on CT scans. CT scans are not limited to the medical domain; they are also used in industries. CT scans are used in airport baggage screening, assembly lines, and the object detection systems in these places should be able to detect objects fast. One of the ways to address the issue of computational complexity and make the object detection systems fast is to use low-resolution images. Low-resolution CT scanning is fast. The entire process of scanning and detection can be made faster by using low-resolution images. Even in the medical domain, to reduce the rad iation dose, the exposure time of the patient should be reduced. The exposure time of patients could be reduced by allowing low-resolution CT scans. Hence it is essential to find out which object detection model has better accuracy as well as speed at low-resolution CT scans. However, the existing approaches did not provide details about how the model would perform when the resolution of CT scans is varied. Hence in this project, the goal is to analyze the impact of varying resolution of CT scans on both the speed and accuracy of the model. Three object detection models, namely RetinaNet, YOLOv3, and YOLOv5, were trained at various resolutions. Among the three models, it was found that YOLOv5 has the best mAP and f1 score at multiple resolutions on the DeepLesion dataset. RetinaNet model h as the least inference time on the DeepLesion dataset. From the experiments, it could be asserted that sacrificing mean average precision (mAP) to improve inference time by reducing resolution is feasible.
In (dynamic) adaptive mesh refinement (AMR) an input mesh is refined or coarsened to the need of the numerical application. This refinement happens with no respect to the originally meshed domain and is therefore limited to the geometrical accuracy of the original input mesh. We presented a novel approach to equip this input mesh with additional geometry information, to allow refinement and high-order cells based on the geometry of the original domain. We already showed a limited implementation of this algorithm. Now we evaluate this prototype with a numerical application and we prove its influence on the accuracy of certain numerical results. To be as practical as possible, we implement the ability to import meshes generated by Gmsh and equip them with the needed geometry information. Furthermore, we improve the mapping algorithm, which maps the geometry information of the boundary of a cell into the cell's volume. With these preliminary steps done, we use out new approach in a simulation of the advection of a concentration along the boundary of a sphere shell and past the boundary of a rotating cylinder. We evaluate the accuracy of our approach in comparison to the conventional refinement of cells to answer our research question: How does the performance and accuracy of the hexahedral curved domain AMR algorithm compare to linear AMR when solving the advection equation with the linear finite volume method? To answer this question, we show the influence of curved AMR on our simulation results and see, that it is even able to outperform far finer linear meshes in terms of accuracy. We also see that the current implementation of this approach is too slow for practical usage. We can therefore prove the benefits of curved AMR in certain, geometry-related application scenarios and show possible improvements to make it more feasible and practical in the future.
In the field of autonomous robotics, sensors have played a major role in defining the scope of technology and to a great extent, limitations of it as well. This cycle of constant updates and hence technological advancement has made given birth to some serious industries which were once inconceivable. Industries like autonomous driving which has a serious impact on safety and security of people, also has an equally harsh implication on the dynamics and economics of the market. With sensors like LiDAR and RADAR delivering 3D measurements as point clouds, there is a necessity to process the raw measurements directly and many research groups are working on the same. A sizable research has gone in solving the task of object detection on 2D images. In this thesis we aim to develop a LiDAR based 3D object detection scheme. We combine the ideas of PointPillars and feature pyramid networks from 2D vision to propose Pillar-FPN. The proposed method directly takes 3D point clouds as input and outputs a 3D bounding box. Our pipeline consists of multiple variations of proposed Pillar-FPN at the feature fusion level that are described in the results section. We have trained our model on the KITTI train dataset and evaluated it on KITTI validation dataset.
The ability to finely segment different instances of various objects in an environment forms a critical tool in the perception tool-box of any autonomous agent. Traditionally instance segmentation is treated as a multi-label pixel-wise classification problem. This formulation has resulted in networks that are capable of producing high-quality instance masks but are extremely slow for real-world usage, especially on platforms with limited computational capabilities. This thesis investigates an alternate regression-based formulation of instance segmentation to achieve a good trade-off between mask precision and run-time. Particularly the instance masks are parameterized and a CNN is trained to regress to these parameters, analogous to bounding box regression performed by an object detection network.
In this investigation, the instance segmentation masks in the Cityscape dataset are approximated using irregular octagons and an existing object detector network (i.e., SqueezeDet) is modified to regresses to the parameters of these octagonal approximations. The resulting network is referred to as SqueezeDetOcta. At the image boundaries, object instances are only partially visible. Due to the convolutional nature of most object detection networks, special handling of the boundary adhering object instances is warranted. However, the current object detection techniques seem to be unaffected by this and handle all the object instances alike. To this end, this work proposes selectively learning only partial, untainted parameters of the bounding box approximation of the boundary adhering object instances. Anchor-based object detection networks like SqueezeDet and YOLOv2 have a discrepancy between the ground-truth encoding/decoding scheme and the coordinate space used for clustering, to generate the prior anchor shapes. To resolve this disagreement, this work proposes clustering in a space defined by two coordinate axes representing the natural log transformations of the width and height of the ground-truth bounding boxes.
When both SqueezeDet and SqueezeDetOcta were trained from scratch, SqueezeDetOcta lagged behind the SqueezeDet network by a massive ≈ 6.19 mAP. Further analysis revealed that the sparsity of the annotated data was the reason for this lackluster performance of the SqueezeDetOcta network. To mitigate this issue transfer-learning was used to fine-tune the SqueezeDetOcta network starting from the trained weights of the SqueezeDet network. When all the layers of the SqueezeDetOcta were fine-tuned, it outperformed the SqueezeDet network paired with logarithmically extracted anchors by ≈ 0.77 mAP. In addition to this, the forward pass latencies of both SqueezeDet and SqueezeDetOcta are close to ≈ 19ms. Boundary adhesion considerations, during training, resulted in an improvement of ≈ 2.62 mAP of the baseline SqueezeDet network. A SqueezeDet network paired with logarithmically extracted anchors improved the performance of the baseline SqueezeDet network by ≈ 1.85 mAP.
In summary, this work demonstrates that if given sufficient fine instance annotated data, an existing object detection network can be modified to predict much finer approximations (i.e., irregular octagons) of the instance annotations, whilst having the same forward pass latency as that of the bounding box predicting network. The results justify the merits of logarithmically extracted anchors to boost the performance of any anchor-based object detection network. The results also showed that the special handling of image boundary adhering object instances produces more performant object detectors.
Object detectors have improved considerably in the last years by using advanced Convolutional Neural Networks (CNNs) architectures. However, many detector hyper-parameters are not generally tuned, and they are used with values set by the detector authors. Blackbox optimization methods have gained more attention in recent years because of its ability to optimize the hyper-parameters of various machine learning algorithms and deep learning models. However, these methods are not explored in improving CNN-based object detector's hyper-parameters. In this research work, we propose the use of blackbox optimization methods such as Gaussian Process based Bayesian Optimization (BOGP), Sequential Model-based Algorithm Configuration (SMAC), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to tune the hyper-parameters in Faster R-CNN and Single Shot MultiBox Detector (SSD). In Faster R-CNN, tuning the input image size, prior box anchor scales and ratios using BOGP, SMAC, and CMA-ES has increased the performance around 1.5% in terms of Mean Average Precision (mAP) on PASCAL VOC. Tuning the anchor scales of SSD has increased the mAP by 3% on PASCAL VOC and marine debris datasets. On the COCO dataset with SSD, mAP improvement is observed in the medium and large objects, but mAP decreases by 1% in small objects. The experimental results show that the blackbox optimization methods have proved to increase the mAP performance by optimizing the object detectors. Moreover, it has achieved better results than the hand-tuned configurations in most of the cases.
This work aims to create a natural language generation (NLG) base for further development of systems for automatic examination questions generation and automatic summarization in Hochschule Bonn-Rhein-Sieg and Fraunhofer IAIS, respectively. Nowadays both tasks are very relevant. The first can significantly simplify the university teachers' work and the second to be of assistance for a faster retrieval of knowledge from an excessively large amount of information that people often work with. We focus on the search for an efficient and robust approach to the controlled NLG problem. Therefore, though the initial idea of the project was the usage of the generative adversarial neural networks (GANs), we switched our attention to more robust and easily-controllable autoencoders. Thus, in this work we implement an autoencoder for unsupervised discovery of latent space representations of text, and show the ability of the system to generate new sentences based on this latent space. Apart from that, we apply Gaussian mixture techniques in order to obtain meaningful text clusters and thereby try to create a tool that would allow us to generate sentences relevant to the semantics of the Gaussian clusters, e.g. positive or negative reviews or examination questions on certain topic. The developed system is tested on several datasets and compared to GANs' performance.
Neural network based object detectors are able to automatize many difficult, tedious tasks. However, they are usually slow and/or require powerful hardware. One main reason is called Batch Normalization (BN) [1], which is an important method for building these detectors. Recent studies present a potential replacement called Self-normalizing Neural Network (SNN) [2], which at its core is a special activation function named Scaled Exponential Linear Unit (SELU). This replacement seems to have most of BNs benefits while requiring less computational power. Nonetheless, it is uncertain that SELU and neural network based detectors are compatible with one another. An evaluation of SELU incorporated networks would help clarify that uncertainty. Such evaluation is performed through series of tests on different neural networks. After the evaluation, it is concluded that, while indeed faster, SELU is still not as good as BN for building complex object detector networks.
Interactive Object Detection
(2019)
The success of state-of-the-art object detection methods depend heavily on the availability of a large amount of annotated image data. The raw image data available from various sources are abundant but non-annotated. Annotating image data is often costly, time-consuming or needs expert help. In this work, a new paradigm of learning called Active Learning is explored which uses user interaction to obtain annotations for a subset of the dataset. The goal of active learning is to achieve superior object detection performance with images that are annotated on demand. To realize active learning method, the trade-off between the effort to annotate (annotation cost) unlabeled data and the performance of object detection model is minimised.
Random Forests based method called Hough Forest is chosen as the object detection model and the annotation cost is calculated as the predicted false positive and false negative rate. The framework is successfully evaluated on two Computer Vision benchmark and two Carl Zeiss custom datasets. Also, an evaluation of RGB, HoG and Deep features for the task is presented.
Experimental results show that using Deep features with Hough Forest achieves the maximum performance. By employing Active Learning, it is demonstrated that performance comparable to the fully supervised setting can be achieved by annotating just 2.5% of the images. To this end, an annotation tool is developed for user interaction during Active Learning.
Estimation of Prediction Uncertainty for Semantic Scene Labeling Using Bayesian Approximation
(2018)
With the advancement in technology, autonomous and assisted driving are close to being reality. A key component of such systems is the understanding of the surrounding environment. This understanding about the environment can be attained by performing semantic labeling of the driving scenes. Existing deep learning based models have been developed over the years that outperform classical image processing algorithms for the task of semantic labeling. However, the existing models only produce semantic predictions and do not provide a measure of uncertainty about the predictions. Hence, this work focuses on developing a deep learning based semantic labeling model that can produce semantic predictions and their corresponding uncertainties. Autonomous driving needs a real-time operating model, however the Full Resolution Residual Network (FRRN) [4] architecture, which is found as the best performing architecture during literature search, is not able to satisfy this condition. Hence, a small network, similar to FRRN, has been developed and used in this work. Based on the work of [13], the developed network is then extended by adding dropout layers and the dropouts are used during testing to perform approximate Bayesian inference. The existing works on uncertainties, do not have quantitative metrics to evaluate the quality of uncertainties estimated by a model. Hence, the area under curve (AUC) of the receiver operating characteristic (ROC) curves is proposed and used as an evaluation metric in this work. Further, a comparative analysis about the influence of dropout layer position, drop probability and the number of samples, on the quality of uncertainty estimation is performed. Finally, based on the insights gained from the analysis, a model with optimal configuration of dropout is developed. It is then evaluated on the Cityscape dataset and shown to be outperforming the baseline model with an AUC-ROC of about 90%, while the latter having AUC-ROC of about 80%.
In order to help journalists investigate inside large audiovisual archives, as maintained by news broadcast agencies, the multimedia data must be indexed by text-based search engies. By automatically creating a transcript through automatic speech recognition (ASR), the spoken word becomes accessible to text search, and queries for keywords are made possible. But stil, important contextual information like the identity of the speaker is not captured. Especially when gathering original footage in the political domain, the identity of the speaker can be the most important query constraint, although this name may not be prominent in the words spoken. It is thus desireable to have this information provided explicitely to the search engine. To provide this information, the archive must be an alyzed by automatic Speaker Identification (SID). While this research topic has seen substantial gains in accuracy and robustness over last years, it has not yet established itself as a helpful, large-scale tool outside the research community. This thesis sets out to establish a workflow to provide automatic speaker identification. Its application is to help journalists searching on speeches given in the German parliament (Bundestag). This is a contribution to the News-Stream 3.0 project, a BMBF funded research project that addresses accessibility of various data sources for journalists.
Semantic Image Segmentation Combining Visible and Near-Infrared Channels with Depth Information
(2015)
Image understanding is a vital task in computer vision that has many applications in areas such as robotics, surveillance and the automobile industry. An important precondition for image understanding is semantic image segmentation, i.e. the correct labeling of every image pixel with its corresponding object name or class. This thesis proposes a machine learning approach for semantic image segmentation that uses images from a multi-modal camera rig. It demonstrates that semantic segmentation can be improved by combining different image types as inputs to a convolutional neural network (CNN), when compared to a single-image approach. In this work a multi-channel near-infrared (NIR) image, an RGB image and a depth map are used. The detection of people is further improved by using a skin image that indicates the presence of human skin in the scene and is computed based on NIR information. It is also shown that segmentation accuracy can be enhanced by using a class voting method based on a superpixel pre-segmentation. Models are trained for 10-class, 3-class and binary classification tasks using an original dataset. Compared to the NIR-only approach, average class accuracy is increased by 7% for 10-class, and by 22% for 3-class classification, reaching a total of 48% and 70% accuracy, respectively. The binary classification task, which focuses on the detection of people, achieves a classification accuracy of 95% and true positive rate of 66%. The report at hand describes the proposed approach and the encountered challenges and shows that a CNN can successfully learn and combine features from multi-modal image sets and use them to predict scene labeling.
This work extends the affordance-inspired robot control architecture introduced in the MACS project [35] and especially its approach to integrate symbolic planning systems given in [24] by providing methods to automated abstraction of affordances to high-level operators. It discusses how symbolic planning instances can be generated automatically based on these operators and introduces an instantiation method to execute the resulting plans. Preconditions and effects of agent behaviour are learned and represented in Gärdenfors conceptual spaces framework. Its notion of similarity is used to group behaviours to abstract operators based on the affordance-inspired, function-centred view on the environment. Ways on how the capabilities of conceptual spaces to map subsymbolic to symbolic representations to generate PDDL planning domains including affordance-based operators are discussed. During plan execution, affordance-based operators are instantiated by agent behaviour based on the situation directly before its execution. The current situation is compared to past ones and the behaviour that has been most successful in the past is applied. Execution failures can be repaired by action substitution. The concept of using contexts to dynamically change dimension salience as introduced by Gärdenfors is realized by using techniques from the field of feature selection. The approach is evaluated using a 3D simulation environment and implementations of several object manipulation behaviours.