A cognitive robot equipped with autonomous tool innovation expertise

Like a human, a robot may benefit from being able to use a tool to solve a complex task. When an appropriate tool is not available, a very useful ability for a robot is to create a novel one based on its experience. With the advent of inexpensive 3D printing, it is now possible to give robots such an ability, at least to create simple tools. We proposed a method for learning how to use an object as a tool and, if needed, to design and construct a new tool. The robot began by learning an action model of tool use for a PDDL planner by observing a trainer. It then refined the model by learning by trial and error. Tool creation consisted of generalising an existing tool model and generating a novel tool by instantiating the general model. Further learning by experimentation was performed. Reducing the search space of potentially useful tools could be achieved by providing a tool ontology. We then used a constraint solver to obtain numerical parameters from abstract descriptions and use them for a ready-to-print design. We evaluated our system using a simulated and a real Baxter robot in two cases: hook and wedge. We found that our system performs tool creation successfully

SLAPS: Simultaneous Localization and Phase Shift for a RIS-equipped UAV in 5G/6G Wireless Communication Networks

Unmanned aerial vehicles (UAVs) are utilized to improve the performance of wireless communication networks (WCNs). In 5G/6G WCNs, where massive muti-input multi-output (mMIMO) base stations (BSs) are operated for beamforming to address fast fading, shadowing, and blockage issues of millimeter waves (mmWave) and quasi-optic signals, the application of UAVs as active mMIMO transceivers is questionable. This is due to the prohibitive complexity of the required overhead baseband processor. Reconfigurable intelligent surface (RIS) is a complementary technology to mMIMO BSs to address the energy inefficiency and complexity of 5G/6G WCNs. Equipping UAVs with RISs, comprising passive elements, allows UAVs to remain promising gadgets for improving coverage and blockage issues in 5G/6G by reflecting in the sky and providing aerial line-of-sight (ALoS) service. Particularly, RIS-equipped UAVs (RISeUAVs) can be beneficial for ALoS vehicle-to-vehicle (V2V) communication of autonomous intelligent vehicles. However, channel estimation is prohibitive in a highly dynamic environment. In this light, accurate localization makes it feasible to use geometry information for phase shift and passive beam-steering. Also, accurate localization is required for crash avoidance and safe navigation in dense urban canyons. We propose the simultaneous localization and phase shift (SLAPS) method as a mmWave-localization technique for RISeUAVs. Simulation results prove the effectiveness of the method

Machine Learning Applications for Load Predictions in Electrical Energy Network

In this work collected operational data of typical urban and rural energy network are analysed for predictions of energy consumption, as well as for selected region of Nordpool electricity markets. The regression techniques are systematically investigated for electrical energy prediction and correlating other impacting parameters. The k-Nearest Neighbour (kNN), Random Forest (RF) and Linear Regression (LR) are analysed and evaluated both by using continuous and vertical time approach. It is observed that for 30 minutes predictions the RF Regression has the best results, shown by a mean absolute percentage error (MAPE) in the range of 1-2 %. kNN show best results for the day-ahead forecasting with MAPE of 2.61 %. The presented vertical time approach outperforms the continuous time approach. To enhance pre-processing stage, refined techniques from the domain of statistics and time series are adopted in the modelling. Reducing the dimensionality through principal components analysis improves the predictive performance of Recurrent Neural Networks (RNN). In the case of Gated Recurrent Units (GRU) networks, the results for all the seasons are improved through principal components analysis (PCA). This work also considers abnormal operation due to various instances (e.g. random effect, intrusion, abnormal operation of smart devices, cyber-threats, etc.). In the results of kNN, iforest and Local Outlier Factor (LOF) on urban area data and from rural region data, it is observed that the anomaly detection for the scenarios are different. For the rural region, most of the anomalies are observed in the latter timeline of the data concentrated in the last year of the collected data. For the urban area data, the anomalies are spread out over the entire timeline. The frequency of detected anomalies where considerably higher for the rural area load demand than for the urban area load demand. Observing from considered case scenarios, the incidents of detected anomalies are more data driven, than exceptions in the algorithms. It is observed that from the domain knowledge of smart energy systems the LOF is able to detect observations that could not have detected by visual inspection alone, in contrast to kNN and iforest. Whereas kNN and iforest excludes an upper and lower bound, the LOF is density based and separates out anomalies amidst in the data. The capability that LOF has to identify anomalies amidst the data together with the deep domain knowledge is an advantage, when detecting anomalies in smart meter data. This work has shown that the instance based models can compete with models of higher complexity, yet some methods in preprocessing (such as circular coding) does not function for an instance based learner such as k-Nearest Neighbor, and hence kNN can not option for this kind of complexity even in the feature engineering of the model. It will be interesting for the future work of electrical load forecasting to develop solution that combines a high complexity in the feature engineering and have the explainability of instance based models.publishedVersio

Simultaneous localisation and mapping: A stereo vision based approach

With limited dynamic range and poor noise performance, cameras still pose considerable challenges in the application of range sensors in the context of robotic navigation, especially in the implementation of Simultaneous Localisation and Mapping (SLAM) with sparse features. This paper presents a combination of methods in solving the SLAM problem in a constricted indoor environment using small baseline stereo vision. Main contributions include a feature selection and tracking algorithm, a stereo noise filter, a robust feature validation algorithm and a multiple hypotheses adaptive window positioning method in 'closing the loop'. These methods take a novel approach in that information from the image processing and robotic navigation domains are used in tandem to augment each other. Experimental results including a real-time implementation in an office-like environment are also presented. © 2006 IEEE

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Building bridges for better machines : from machine ethics to machine explainability and back

Be it nursing robots in Japan, self-driving buses in Germany or automated hiring systems in the USA, complex artificial computing systems have become an indispensable part of our everyday lives. Two major challenges arise from this development: machine ethics and machine explainability. Machine ethics deals with behavioral constraints on systems to ensure restricted, morally acceptable behavior; machine explainability affords the means to satisfactorily explain the actions and decisions of systems so that human users can understand these systems and, thus, be assured of their socially beneficial effects. Machine ethics and explainability prove to be particularly efficient only in symbiosis. In this context, this thesis will demonstrate how machine ethics requires machine explainability and how machine explainability includes machine ethics. We develop these two facets using examples from the scenarios above. Based on these examples, we argue for a specific view of machine ethics and suggest how it can be formalized in a theoretical framework. In terms of machine explainability, we will outline how our proposed framework, by using an argumentation-based approach for decision making, can provide a foundation for machine explanations. Beyond the framework, we will also clarify the notion of machine explainability as a research area, charting its diverse and often confusing literature. To this end, we will outline what, exactly, machine explainability research aims to accomplish. Finally, we will use all these considerations as a starting point for developing evaluation criteria for good explanations, such as comprehensibility, assessability, and fidelity. Evaluating our framework using these criteria shows that it is a promising approach and augurs to outperform many other explainability approaches that have been developed so far.DFG: CRC 248: Center for Perspicuous Computing; VolkswagenStiftung: Explainable Intelligent System