Manipulation-relevantcommon-sense knowledge is crucial to support actionplanning for complex tasks.In particular, instrumentality information of what can be done with certain tools can be used to limit the search space which is growing exponentially with the number of viable options.Typical sources for such knowledge, structured common-sense knowledge bases such as ConceptNet or WebChild, provide a limited amount of information which also varies drastically across different domains.Considering the recent success of pre-trained language models such as BERT, we investigate whether common-sense information can directly be extracted from semi-structured text with an acceptable annotation effort.Concretely, we compare the common-sense relations obtained from ConceptNet versus those extracted with BERT from large recipe databases.In this context, we propose a scoring function, based on the WordNet taxonomy to match specific terms to more general ones, enabling a rich evaluation against a set of ground-truth relations.

Real-time learning from data streams in non-stationary environments gains ever more relevance due to the exponentially increasing amounts of generated data. Recently, the Self-Adjusting Memory (SAM) was proposed, an algorithm able to robustly handle heterogeneous types on the basis of two dedicated memories for the current and former concepts that continuously preserve consistency with explicit filtering. Yet, since the algorithm is restricted to one memory architecture, the variety of possible alternatives is limited by design in favor of an overall model consistency. Moreover, it does not actively detect drift, thus adapting with a relatively high delay in case of abrupt changes. We propose a dynamic ensemble on the basis of the SAM algorithm, which is triggered by both, the inherent passive adaptation of SAM and active drift detection. Further, since SAM is based on the stable k-Nearest-Neighbor algorithm, we investigate multiple approaches to obtain a high diversity in the ensemble, resulting in an effective overall strategy. The increased computational demand is countered on the basis of a parallel implementation. We extensively evaluate the method on numerous benchmarks, where it consistently achieves superior results in comparison to state-of-the-art methods.

Personalized online machine learning allows a very accurate modelling of individual behavior and demands. In particular, a system that dynamically adapts during runtime can initiate a continuous collaboration with its user where both alternately adjust to each other to maximize the system's utility. However, in application scenarios based on supervised learning it is often unclear how to obtain the required ground truth for such dynamic systems. In this paper, we focus on applications where a real-time classification of sequential data is crucial. Concretely, we propose to adapt an online personalized model solely based on pseudo-ground-truth information which is provided by another machine learning model. This model has the advantage to classify sequences in retrospective with a small delay and thus is able to achieve a higher performance than real-time systems. In particular, it is a pre-trained offline model, which means that no ground-truth information is necessary during runtime. We apply the proposal on the task of online action classification, for which the benefits of personalization have been recently emphasized.

Online action classification is an important field of research, enabling the particularly interesting application scenario of controlling wearable devices which actively support the user's motions. The majority of machine learning applications of real-world systems are based on pre-trained average-user models without any personalization. Our long-term goal is to provide a system that adapts to its user's personal behavior patterns on the fly and in real-time. Ideally, we want to initiate a continuous collaboration between the system and the user where both alternatively adjust to each other to maximize the system's utility. Such tasks are not feasible with static models. In this paper, we investigate the potential and benefits of personalized online learning in the task of online action classification. We record motion sequences of different subjects wearing the Xsens bodysuit, which incorporates multiple inertial measuring units, enabling a fine-grained discrimination of motions. On this basis, we first perform a feature selection, showing that only a few sensors are necessary to achieve a high classification performance. Subsequently, we compare the recognition capabilities of offline average user models against personalized models trained in an online way. Our experiments conclude that personalized models require only few data to outperform average user systems and are particularly valuable for applications with limited computational hardware which rely on the raw sensor inputs only.

Data ubiquity highlights the need of efficient and adaptable data-driven solutions. In this paper, we present FAST AND SLOW LEARNING (FSL), a novel unified framework that sheds light on the symbiosis between batch and stream learning. FSL works by employing Fast (stream) and Slow (batch) Learners, emulating the mechanisms used by humans to make decisions. We showcase the applicability of FSL on the task of classification by introducing the FAST AND SLOW CLASSIFIER (FSC). A Fast Learner provides predictions on the spot, continuously updating its model and adapting to changes in the data. On the other hand, the Slow Learner provides predictions considering a wider spectrum of seen data, requiring more time and data to create complex models. Once that enough data has been collected, FSC trains the Slow Learner and starts tracking the performance of both learners. A drift detection mechanism triggers the creation of new Slow models when the current Slow model becomes obsolete. FSC selects between Fast and Slow Learners according to their performance on new incoming data. Test results on real and synthetic data show that FSC effectively drives the positive interaction of stream and batch models for learning from evolving data streams.

An increasing number of industrial areas recognize the opportunities of Big Data, requiring highly efficient algorithms which enable real-time processing to reduce the burden of data storage and maintenance. Decision trees are extremely fast, highly accurate and easy to use in practice. Merging multiple decision trees to an ensemble leads to one of the most powerful machine learning methods. The Very Fast Decision Tree is the state-of-the-art incremental decision tree induction algorithm, capable of learning from massive data streams. It is successful due to its theoretical guarantees based on the Hoeffding bound as well as its competitive performance in terms of classification accuracy and time / space efficiency. In this paper, we increase the efficiency even further by replacing its global splitting scheme, which periodically tries to split every nminexamples. Instead, we utilize local statistics to predict the split-time, thus, avoiding unnecessary split-attempts, usually dominating the computational cost. Concretely, we use the class distributions of previous split-attempts to approximate the minimum number of examples until the Hoeffding bound is met. This cautious approach yields by design a low delay and reduces the number of split-attempts at the same time. We extensively evaluate our method using common stream-learning benchmarks also considering non-stationary environments. The experiments confirm a substantially reduced run-time without a loss in classification performance.

Data Mining in non-stationary data streams is particularly relevant in the context of the Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self-Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm. SAM-kNN can deal with heterogeneous concept drift, i.e. different drift types and rates. Its basic idea are dedicated models for current and former concepts used according to the demands of the given situation. It can be robustly applied in practice without meta parameter optimization. We conduct an extensive evaluation on various benchmarks, consisting of artificial streams with known drift characteristics and real-world datasets. Highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capability to handle heterogeneous concept drift.

Data Mining in non-stationary data streams is gaining more attentionrecently, especially in the context of Internet of Things and Big Data. It is a highly challenging task, since the fundamentally different typesof possibly occurring drift undermine classical assumptions such asi.i.d. data or stationary distributions. Available algorithms are either struggling with certain forms of drift or require a priori knowledge in terms of a task specific setting. We propose the Self Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm since kNN constitutes a proven classifier within the streaming setting. SAM-kNN can deal with heterogeneous concept drift, i.e different drift types and rates, using biologically inspiredmemory models and their coordination. It can be easilyapplied in practice since an optimization of the meta parameters is not necessary. The basic idea is to construct dedicated models for thecurrent and former concepts and apply them according tothe demands of the given situation. An extensive evaluation on various benchmarks, consisting of artificial streamswith known drift characteristics as well as real world datasets is conducted. Thereby, we explicitly add new benchmarks enabling a precise performance evaluation on multiple types of drift. The highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capabilityto handle heterogeneous concept drift.

We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot. The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learning by adaptive insertions of representative vectors. We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy optimizing a cost-function based on a subset of samples. We demonstrate this model within a real-time application for a mobile robot scenario, where we perform interactive real-time learning of visual categories.