Abstract-- Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy. I. INTRODUCTION Robot technology has improved tremendously over the last decade. Consequently, autonomous robot systems have been able to operate in increasingly complex environments and for increasingly long periods of time, i.e. weeks, months, or years. When a fully modelled robot is deployed in a completely known, static environment, the challenge of long-term autonomy (LTA) reduces to one of robustness, i.e. enabling the robot to remain operational for as long as possible. Without these simplifying assumptions autonomous robots face a number of interrelated challenges. The first refers to the application requirements, e.g., the robot platform (hardware and software), environment and tasks to be performed.

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.

Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled. Herein, we review three state-of-the-art approaches in deep learning to combat this challenge. The first topic is transfer learning, in which some aspects of one domain, e.g., features, are transferred to another domain. The next is unsupervised learning, e.g., autoencoders, which operate on unlabeled data. The last is generative adversarial networks, which can generate realistic looking data that can fool the likes of both a deep learning network and human. The aim of this article is to raise awareness of this dilemma, to direct the reader to existing work and to highlight current gaps that need solving.

We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters $k$, and for each $1 \leq k \leq k_\mathrm{max}$, a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this ``learning to cluster'' and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.

Nowadays, huge amounts of tracking data in the mobility domain are being generated by Global Positioning System (GPS) enabled devices and collected in data repositories; tracked moving entities could be pedestrians, cars, vessels, planes, animals, robots, etc. These datasets constitute a rich source for inferring mobility patterns and characteristics for a wide spectrum of novel applications and services, from social networking applications [5][46] to aviation traffic monitoring [61][67]. During the recent years, this kind of information has attracted great interest by data scientists, both in industry and in academia, and is being used in order to extract useful knowledge about what, how and for how long the moving entities are conducting individual activities related with specific circumstances. The most challenging task is to make this information actionable, by means of exploiting historical mobility patterns in order to gauge how the moving entities may evolve in short-or long-term, whether the individual forecasted movement is typical or anomalous, whether there exists a high probability for congestion in the near future, etc. As a consequence, predictive analytics over mobility data has become increasingly important and turns out to be a'hot' field in several application domains [4][74][111]. The problem of predictive analytics over mobility data finds two broad categories of application scenarios. The first scenario involves cases where the moving entities are traced in real-time to produce analytics and compute short-term predictions, which are time-critical and need immediate response. The prediction includes either location-or trajectory-related tasks.

Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to {\it symbols}. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems.

Though yet to become a standard in schools, artificial intelligence in education has been "a thing" since AI's uptick in the 1980s. In many ways, the two seem made for each other. We use education as a means to develop minds capable of expanding and leveraging the knowledge pool, while AI provides tools for developing a more accurate and detailed picture of how the human mind works. AI's digital, dynamic nature also offers opportunities for student engagement that cannot be found in often out-dated textbooks or in the fixed environment of the typical four-walled classroom. In synergistic fashion, they each have the potential to propel the other forward and accelerate the discovery of new learning frontiers and the creation of innovative technologies.

The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and

The mean-median-mode trio involves the three most frequently used measures of central tendency of a dataset. They are taught within the very first classes of any course on basic Statistics. However, they do not share the same degree of importance: the sample mean (or average) is normally well understood and employed in everyday situations, the sample median is also useful and easy to visualize, but the mode, usually defined as the value of the dataset having the highest frequency of appearance, looks like a more bizarre measure of location. This uneven treatment was already noted by Dalenius (1965), but it keeps being present as of today, to some extent. Indeed, when the dataset consists of realizations from a continuous random variable then all the observed values are different with probability one and, therefore, the mode does not even make much sense.

Technology continues to change society at a rapid pace, and accounting and auditing are by no means immune. New technologies are increasingly able to mimic human activity, taking on repetitive tasks more quickly and accurately than people can. The authors provide an overview of the ways in which robotic process automation may change how the profession operates, with a particular focus on the area of revenue audits. Auditing has historically incorporated many computer-dependent tools and processes, which were often interlinked by many manual steps and keystrokes. A new set of overlay software has emerged, however, that combines these disparate actions into a single smooth automated process. Robotic process automation (RPA) uses these new software tools, such as those offered by Blue Prism or UiPath, to transform a still somewhat handmade audit process into a more assembly-line audit process.