Understanding how localized changes in one variable affect others in multivariate time series is essential for diagnostics and decision-making in complex systems. Existing models often fail to capture realistic inter-feature dynamics when simulating "what-if" scenarios, leading to inaccurate or uncorrelated reconstructions. We propose CFORVAE, a variational autoencoder framework that explicitly addresses this limitation by combining temporal decomposition with frequency-domain feature correlation modeling. Our architecture uses a dual-path encoding of trend and seasonal components, each projected into attention-pooled latent spaces, and applies Fourier Neural Operators (FNO) to capture cross-feature dependencies in the spectral domain. This decomposition-correlation design enables component-specific latent manipulation and ensures that local modifications propagate realistically across correlated variables. Through extensive experiments, we show that CFORVAE outperforms state-of-the-art baselines in preserving temporal and feature-level dependencies, especially under adjustment-based reconstructions, making it a powerful tool for interpretable "what-if" analysis and diagnostics.

Abstract We revisit the computational problem of partitioning indivisibles into bundles among alternatives to maximize value (e.g., welfare). These problems have broad applications, yet many important variants are computationally hard, including well-known instances in operations research, computational economics, and artificial intelligence. To address this complexity, we analyze novel restrictions and concise representations for this problem class and establish new complexity results. Building on these findings, we present improved complexity bounds using a hypergraph-based characterization and introduce a novel “bootstrapped” dynamic programming method that significantly outperforms existing algorithms for a broad class of problems. Other findings include: polynomial-time solvability for problems with non-negative synergies and two alternatives; the problem remaining -hard even when bounding bundle sizes to two, with other instances being polynomial-time solvable; and exploration of bounds for more general cases allowing externalities and balanced (mixed) welfare, offering efficient approximation and non-trivial exponential-time algorithms for many hard cases.

In this work, we introduce a time series forecasting architecture that incorporates autocorrelation while leveraging dual encoders operating at multiple scales. Unlike traditional models that rely on predefined trend and seasonal components, our architecture applies learnable decomposition in the frequency domain and employs two separate encoders per scale: one focusing on low-pass filtering to capture trends and the other utilizing high-pass filtering to model seasonal variations. This learnable filtering allows the model to dynamically adapt and isolate trend and seasonal components directly in the frequency domain. A central and distinguishing innovation in our approach is the introduction of an integrated confidence scoring mechanism based on the reconstruction error of the same encoder backbone used for forecasting. This confidence score provides a direct, learned metric of prediction certainty, which we use to dynamically adjust the final forecasting output, effectively mitigating risks from uncertain predictions. We achieve the integration of autocorrelation by computing lagged differences in temporal processing, enabling the model to capture dependencies across time more effectively. In our architecture, each encoder processes the input through fully connected layers to handle temporal and channel interactions, and the same encoder backbone is used for both reconstruction and forecasting outputs. By combining frequency-domain filtering, autocorrelation-based temporal modeling, channel-wise transformations, and proposed confidence scoring mechanism, our model not only accurately captures long-term dependencies and fine-grained patterns, but also operates more efficiently compared to other state-of-the-art methods. Its lightweight design ensures faster processing while maintaining high precision in forecasting across diverse datasets and time horizons.

This paper introduces Fuzzy Attention Network-Based Transformer (FANTF), an approach for time series forecasting, classification, and anomaly detection. FANTF combines fuzzy logic concepts with transformer architectures through a novel fuzzy attention mechanism. The proposed mechanism is inspired by Type-2 fuzzy set theory. It uses a learnable Gaussian uncertainty perturbation to model uncertainty in attention relevance scores. This makes it possible for the model to accurately capture uncertainty in temporal relationships than normal deterministic attention. Transformers’ capacity to record complex temporal patterns and multivariate connections is enhanced by FANTF. The approach is both architecture-agnostic and task-agnostic. The proposed fuzzy attention module can be used as a direct replacement for the standard attention layer. To evaluate its effectiveness, FAN is integrated into five transformer backbones: Transformer, Informer, PatchTST, Crossformer, and iTransformer. The fuzzy attention mechanism produces softer and more diverse attention distributions. It provides uncertainty-aware interpretations through attention heatmap visualizations. Experimental evaluations on some real-life datasets reveal that FANTF enhances the forecasting performance, classification, and anomaly detection tasks over traditional transformer-based models. FANTF achieves statistically significant and competitive performance improvements over standard Transformer-based baselines on most benchmark datasets ( , Wilcoxon signed-rank test), observed under noisy and non-stationary time series conditions.

As Artificial Intelligence-driven decision-making systems become increasingly popular, ensuring fairness in their outcomes has emerged as a critical and urgent challenge. AI models, often trained on open-source datasets embedded with human and systemic biases, risk producing decisions that disadvantage certain demographics. This challenge intensifies when multiple sensitive attributes interact, leading to intersectional bias, a compounded and uniquely complex form of unfairness. Over the years, various methods have been proposed to address bias at the data and model levels. However, mitigating intersectional bias in decision-making remains an under-explored challenge. Motivated by this gap, we propose a novel framework that leverages knowledge distillation to promote intersectional fairness. Our approach proceeds in two stages: first, a teacher model is trained solely to maximize predictive accuracy, followed by a student model that inherits the teacher’s representational knowledge while incorporating intersectional fairness constraints. The student model integrates tailored loss functions that enforce parity in false positive rates and demographic distributions across intersectional groups, alongside an adversarial objective that minimizes protected attribute information within the learned representation. Empirical evaluation across multiple benchmark datasets demonstrates that we achieve a 52% increase in accuracy for multi-class classification and a 61% reduction in average false positive rate across intersectional groups and outperforms state-of-the-art models. This distillation-based methodology provides a more stable optimization opportunity than direct fairness approaches, resulting in substantially fairer representations, particularly for multiple sensitive attributes and underrepresented demographic intersections.

Explainable artificial intelligence (XAI) and fair learning have made significant strides in various application domains, including criminal recidivism predictions, healthcare settings, toxic comment detection, automatic speech detection, recommendation systems, and image segmentation. However, these two fields have largely evolved independently. Recent studies have demonstrated that incorporating explanations into decision-making processes enhances the transparency and trustworthiness of AI systems. In light of this, our objective is to conduct a systematic review of FairXAI, which explores the interplay between fairness and explainability frameworks. To commence, we propose a taxonomy of FairXAI that utilizes XAI to mitigate and evaluate bias. This taxonomy will be a base for machine learning researchers operating in diverse domains. Additionally, we will undertake an extensive review of existing articles, taking into account factors such as the purpose of the interaction, target audience, and domain and context. Moreover, we outline an interaction framework for FairXAI considering various fairness perceptions and propose a FairXAI wheel that encompasses four core properties that must be verified and evaluated. This will serve as a practical tool for researchers and practitioners, ensuring the fairness and transparency of their AI systems. Furthermore, we will identify challenges and conflicts in the interactions between fairness and explainability, which could potentially pave the way for enhancing the responsibility of AI systems. As the inaugural review of its kind, we hope that this survey will inspire scholars to address these challenges by scrutinizing current research in their respective domains.

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent’s favor. The proposed planner, Dynamic Autonomous Exploration Planner (DAEP), extends AEP to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in such settings we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.

Unmanned aerial vehicles (UAVs), known for their agile flight capabilities, require safe trajectory planning to achieve high-speed flights. This is necessary to swiftly evade obstacles and adapt trajectories under hard real-time constraints. These adjustments are essential to generate viable paths that prevent collisions while maintaining high speeds with minimal tracking errors. This paper addresses the challenge of enhancing the safety of agile trajectory planning. The proposed method combines a supervised learning approach, as teacher policy, with deep reinforcement learning (DRL), as student policy. Initially, we train the teacher policy using a path planning algorithm that prioritizes safety while minimizing jerk and flight time. Then, we use this policy to guide the learning of the student policy in various unknown environments. Testing in simulation demonstrates noteworthy advancements, including an 80% reduction in tracking error, a 31% decrease in flight time, a 19% increase in high-speed duration, and a success rate improvement from 50% to 100%, as compared to baseline methods.

Anomaly detection in time series data is a critical task in various domains, including finance, healthcare, cybersecurity and industry. Traditional methods, such as time series decomposition, clustering, and density estimation, have provided robust solutions for identifying anomalies that exhibit distinct patterns or significant deviations from normal data distributions. Recent advancements in machine learning and deep learning have further enhanced these capabilities. This paper introduces a novel method for anomaly detection that combines the strengths of autoencoders and recurrent neural networks (RNNs) with an reconstruction error feedback mechanism based on Mean Squared Error. We compare our method against classical techniques and recent approaches like OmniAnomaly, which leverages stochastic recurrent neural networks, and the Anomaly Transformer, which introduces association discrepancy to capture long-range dependencies and DCDetector using contrastive representation learning with multi-scale dual attention. Experimental results demonstrate that our method achieves superior overall performance in terms of precision, recall, and F1 score. The source code is available at http://github.com/mribrahim/AE-FAR

Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the existing literature primarily focuses on the quality of synthetic health data, neglecting the crucial aspect of fairness in downstream predictions. Consequently, models trained on synthetic EHR have faced criticism for producing biased outcomes in target tasks. These biases can arise from either spurious correlations between features or the failure of models to accurately represent sub-groups. To address these concerns, we present Bias-transforming Generative Adversarial Networks (Bt-GAN), a GAN-based synthetic data generator specifically designed for the healthcare domain. In order to tackle spurious correlations (i), we propose an information-constrained Data Generation Process (DGP) that enables the generator to learn a fair deterministic transformation based on a well-defined notion of algorithmic fairness. To overcome the challenge of capturing exact sub-group representations (ii), we incentivize the generator to preserve sub-group densities through score-based weighted sampling. This approach compels the generator to learn from underrepresented regions of the data manifold. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using the Medical Information Mart for Intensive Care (MIMIC-III) database. Our results demonstrate that Bt-GAN achieves state-of-the-art accuracy while significantly improving fairness and minimizing bias amplification. Furthermore, we perform an in-depth explainability analysis to provide additional evidence supporting the validity of our study. In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain. By incorporating fairness considerations and leveraging advanced techniques such as GANs, we pave the way for more reliable and unbiased predictions in healthcare applications.

Deep Generative Models (DGMs) for generating synthetic data with properties such as quality, diversity, fidelity, and privacy is an important research topic. Fairness is one particular aspect that has not received the attention it deserves. One difficulty is training DGMs with an in-process fairness objective, which can disturb the global convergence characteristics. To address this, we propose Fair Latent Deep Generative Models (FLDGMs) as enablers for more flexible and stable training of fair DGMs, by first learning a syntax-agnostic, model-agnostic fair latent representation (low dimensional) of the data. This separates the fairness optimization and data generation processes thereby boosting stability and optimization performance. Moreover, data generation in the low dimensional space enhances the accessibility of models by reducing computational demands. We conduct extensive experiments on image and tabular domains using Generative Adversarial Networks (GANs) and Diffusion Models (DMs) and compare them to the state-of-the-art in terms of fairness and utility. Our proposed FLDGMs achieve superior performance in generating high-quality, high-fidelity, and high-diversity fair synthetic data compared to the state-of-the-art fair generative models.

Imagine placing an online order on your way to the grocery store, then being able to pick the collected basket upon arrival or shortly after. Likewise, imagine placing any online retail order, made ready for pickup in minutes instead of days. In order to realize such a low-latency automatic warehouse logistics system, solvers must be made to be basket-aware. That is, it is more important that the full order (the basket) is picked timely and fast, than that any single item in the order is picked quickly. Current state-of-the-art methods are not basket-aware. Nor are they optimized for a positive customer experience, that is; to prioritize customers based on queue place and the difficulty associated with picking their order. An example of the latter is that it is preferable to prioritize a customer ordering a pack of diapers over a customer shopping a larger order, but only as long as the second customer has not already been waiting for too long. In this work we formalize the problem outlined, propose a new method that significantly outperforms the state-of-the-art, and present a new realistic simulated benchmark. The proposed method is demonstrated to work in an on-line and real-time setting, and to solve the on-demand multi-agent basket picking problem for automated shopping stores under realistic conditions.

Abstract One-step reinforcement learning explanation methods account for individual actions but fail to consider the agent’s future behavior, which can make their interpretation ambiguous. We propose to address this limitation by providing hierarchical goals as context for one-step explanations. By considering the current hierarchical goal as a context, one-step explanations can be interpreted with higher certainty, as the agent’s future behavior is more predictable. We combine reward decomposition with hierarchical reinforcement learning into a novel explainable reinforcement learning framework, which yields more interpretable, goal-contextualized one-step explanations. With a qualitative analysis of one-step reward decomposition explanations, we first show that their interpretability is indeed limited in scenarios with multiple, different optimal policies—a characteristic shared by other one-step explanation methods. Then, we show that our framework retains high interpretability in such cases, as the hierarchical goal can be considered as context for the explanation. To the best of our knowledge, our work is the first to investigate hierarchical goals not as an explanation directly but as additional context for one-step reinforcement learning explanations.

This Research Full Paper explore inclusion in programming in primary school. Education plays a crucial role in engaging a diverse group of students with different social backgrounds and interests. Therefore, this study aims to shed light upon inclusion in programming in primary school, focusing on gender to increase the knowledge regarding inclusion in programming didactics. The following research questions have guided the study: How are programming activities designed in primary school? How do pupils approach the programming tasks given? Can any gender differences be observed, and what are the consequences for the teaching practice? The theoretical framework used to analyse the empirical material is at the intersection between multimodal social semiotics [1] and a design-oriented perspective [2]. The empirical material consists of classroom video observations. Programming lessons in grades 4-8 have been observed and videos was recorded during 2019-2020. The pupils have worked on eight different programming tasks during the lessons. Analysis of these programming activities (tasks, instructions and resources used) focusing on gender has been made. Findings show two aspects 1) interest and position and 2) representations of knowledge. Regarding interest and position, the study of programming activities shows both similarities and differences between girls’ and boys’ approach to the task. Similarities are shown regarding the learning activities. No differences in coding strategies or creativity are observed if the task has an open design. The differences are shown in the guided tasks, where boys tend to engage in the tasks from their interests rather than following instructions and girls tend to follow the instructions given by the teacher. From a gender perspective, the boys might find programming more creative and fun, and the girls might feel less engaged as their interest falls into the background. Secondly, knowledge representations might affect who is seen as an expert within the CS field. For example, in grades 4 and 5, a male voice was represented in the video clips and a guest teacher used when presenting programming activities. The resources used in the lessons can be seen as representations of knowledge. In this case, they are always connected to a social and cultural domain [3], an environment foremost represented by males in this case.

Programming has been part of Swedish elementary school curriculum for six years and the aim of this full paper is to find out how teachers can design programming activities so that students engage and learn. A mix-methods research project with a social semiotic, multimodal theoretical framework – Designs for learning – is used to investigate teaching and learning in a class during three years. The results in this small-scale study indicate that collaboration is a successful didactic design for programming lessons in school. Computational thinking is prevalent and both digital skills (such as coding) and digital competencies (such as understanding the impact of technology in society) are practiced and met in programming lessons merging Science, Technology, Engineering, Arts, and Mathematics.

As a result of the increased digitalisation, many countries have introduced programming in their primary education curricula. One main objective is to give all children equal opportunities to develop the skills needed to be an active participant and producer in a digitalized society. This also addresses another important objective, that of increased diversity and broadened participation. Despite technology being a natural part in our everyday lives, stereotypical views of programming as a primarily male activity still exist. In this paper, we explore girls’ and boys’ experiences of programming at school and in their spare time. The study is situated in primary school classrooms in Sweden, where programming was introduced in a cross-curricular manner as part of digital competence in 2018. While most students reported having some programming experience, it was quite limited. The results show that, compared to the girls, boys in grades 4-9 are somewhat more positive towards programming and get more programming experience both at school and in their spare time. Similarly, boys rated their self-perceived programming skills higher than the girls. In grades 1-3, no gender disparity was found in students’ attitudes, experiences or skills. However, the gender differences in grades 4-9 were not reflected to an equally high extent in the students’ programming skills, as girls and boys did equally well on many skills related tasks. The analysis highlights the importance of well planned, motivating and relevant tasks in order to provide positive experiences of programming in the classroom.

Coverage Path Planning is the work horse of contemporary service task automation, powering autonomous floor cleaning robots and lawn mowers in households and office sites. While steady progress has been made on indoor cleaning and outdoor mowing, these environments are small and with simple geometry compared to general urban environments such as city parking garages, highway bridges or city crossings. To pave the way for autonomous road sweeping robots to operate in such difficult and complex environments, a benchmark suite with three large-scale 3D environments representative of this task is presented. On this benchmark we evaluate a new Coverage Path Planning method in comparison with previous well performing algorithms, and demonstrate state-of-the-art performance of the proposed method. Part of the success, for all evaluated algorithms, is the usage of automated domain adaptation by in-the-loop parameter optimization using Bayesian Optimization. Apart from improving the performance, tedious and bias-prone manual tuning is made obsolete, which makes the evaluation more robust and the results even stronger.

Abstract Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.

Abstract The recent paper “Reward is Enough” by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and artificial, and provides a suitable basis for the creation of artificial general intelligence. We contest the underlying assumption of Silver et al. that such reward can be scalar-valued. In this paper we explain why scalar rewards are insufficient to account for some aspects of both biological and computational intelligence, and argue in favour of explicitly multi-objective models of reward maximisation. Furthermore, we contend that even if scalar reward functions can trigger intelligent behaviour in specific cases, this type of reward is insufficient for the development of human-aligned artificial general intelligence due to unacceptable risks of unsafe or unethical behaviour.

Optimal simultaneous coalition structure generation and assignment is computationally hard. The state-of-the-art can only compute solutions to problems with severely limited input sizes, and no effective approximation algorithms that are guaranteed to yield high-quality solutions are expected to exist. Real-world optimization problems, however, are often characterized by large-scale inputs and the need for generating feasible solutions of high quality in limited time. In light of this, and to make it possible to generate better feasible solutions for difficult large-scale problems efficiently, we present and benchmark several different anytime algorithms that use general-purpose heuristics and Monte Carlo techniques to guide search. We evaluate our methods using synthetic problem sets of varying distribution and complexity. Our results show that the presented algorithms are superior to previous methods at quickly generating near-optimal solutions for small-scale problems, and greatly superior for efficiently finding high-quality solutions for large-scale problems. For example, for problems with a thousand agents and values generated with a uniform distribution, our best approach generates solutions 99.5% of the expected optimal within seconds. For these problems, the state-of-the-art solvers fail to find any feasible solutions at all.

Simulation-based training has the potential to significantly improve training value in the air combat domain. However, synthetic opponents must be controlled by high-quality behavior models, in order to exhibit human-like behavior. Building such models by hand is recognized as a very challenging task. In this work, we study how multi-agent deep reinforcement learning can be used to construct behavior models for synthetic pilots in air combat simulation. We empirically evaluate a number of approaches in two air combat scenarios, and demonstrate that curriculum learning is a promising approach for handling the high-dimensional state space of the air combat domain, and that multi-objective learning can produce synthetic agents with diverse characteristics, which can stimulate human pilots in training.

Abstract An important research problem in artificial intelligence is how to organize multiple agents, and coordinate them, so that they can work together to solve problems. Coordinating agents in a multi-agent system can significantly affect the system’s performance—the agents can, in many instances, be organized so that they can solve tasks more efficiently, and consequently benefit collectively and individually. Central to this endeavor is coalition formation—the process by which heterogeneous agents organize and form disjoint groups (coalitions). Coalition formation often involves finding a coalition structure (an exhaustive set of disjoint coalitions) that maximizes the system’s potential performance (e.g., social welfare) through coalition structure generation. However, coalition structure generation typically has no notion of goals. In cooperative settings, where coordination of multiple coalitions is important, this may generate suboptimal teams for achieving and accomplishing the tasks and goals at hand. With this in mind, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent alternatives (e.g., tasks/goals), and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This combinatorial optimization problem has many real-world applications, including forming goal-oriented teams. To evaluate the presented algorithm’s performance, we present five methods for synthetic problem set generation, and benchmark the algorithm against the industry-grade solver CPLEX using randomized data sets of varying distribution and complexity. To test its anytime-performance, we compare the quality of its interim solutions against those generated by a greedy algorithm and pure random search. Finally, we also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that it can be used in game-playing to coordinate smaller sets of agents in real-time.

Robot safety is of growing concern given recent developments in intelligent autonomous systems. For complex agents operating in uncertain, complex and rapidly-changing environments it is difficult to guarantee safety without imposing unrealistic assumptions and restrictions. It is therefore necessary to complement traditional formal verification with monitoring of the running system after deployment. Runtime verification can be used to monitor that an agent behaves according to a formal specification. The specification can contain safety-related requirements and assumptions about the environment, environment-agent interactions and agent-agent interactions. A key problem is the uncertain and changing nature of the environment. This necessitates requirements on how probable a certain outcome is and on predictions of future states. We propose Probabilistic Signal Temporal Logic (ProbSTL) by extending Signal Temporal Logic with a sub-language to allow statements over probabilities, observations and predictions. We further introduce and prove the correctness of the incremental stream reasoning technique progression over well-formed formulas in ProbSTL. Experimental evaluations demonstrate the applicability and benefits of ProbSTL for robot safety.

Stream reasoning can be defined as incremental reasoning over incrementally-available information. The formula progression procedure for Metric Temporal Logic (MTL) makes use of syntactic formula rewritings to incrementally evaluate formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robotics applications. In those cases, there may be uncertainty as to which state out of a set of possible states represents the ‘true’ state. The main contribution of this paper is therefore an extension of the progression procedure that efficiently keeps track of all consistent hypotheses. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise progression under uncertainty. The proposed approach is empirically evaluated by considering the time and space requirements, as well as the impact of permitting varying degrees of uncertainty.

Conventional trajectory-based vehicular traffic analysis approaches work well in simple environments such as a single crossing but they do not scale to more structurally complex environments such as networks of interconnected crossings (e.g., urban road networks). Local trajectory models are necessary to cope with the multi-modality of such structures, which in turn introduces new challenges. These larger and more complex environments increase the occurrences of lack of motion and self-overlaps in observed trajectories which impose further challenges. In this paper we consider the problem of motion pattern recognition in the setting of sequential local motion pattern models. That is, classifying sub-trajectories from observed trajectories in accordance with which motion pattern that best explains it. We introduce a Gaussian process (GP) based modeling approach which outperforms the state-of-the-art GP based motion pattern approaches at this task. We investigate the impact of varying local model overlap and the length of the observed trajectory trace on the classification quality. We further show that introducing a pre-processing step filtering out stops from the training data significantly improves the classification performance. The approach is evaluated using real GPS position data from city buses driving in urban areas.

The Swedish government has recently introduced digital competence including programming in the Swedish K-9 curriculum starting no later than fall 2018. This means that 100 000 teachers need to learn programming and digital competence in less than a year. In this paper we report on our experience working with professional teacher training in Sweden's fifth largest city. The city has about 150 000 inhabitants and about 50 schools with about 14 000 students in primary education. The project has been carried out in close cooperation with the municipality.

Modern robotic systems often consist of a growing set of information-producing components that need to be appropriately connected for the system to function properly. This is commonly done manually or through relatively simple scripts by specifying explicitly which components to connect. However, this process is cumbersome and error-prone, does not scale well as more components are introduced, and lacks flexibility and robustness at run-time. This paper presents an algorithm for setting up and maintaining implicit subscriptions to information through its semantics rather than its source, which we call semantic subscriptions. The proposed algorithm automatically reconfigures the system when necessary in response to changes at run-time, making the semantic subscriptions adaptive to changing circumstances. To illustrate the effectiveness of adaptive semantic subscriptions, we present a case study with two SoftBank Robotics NAO robots for handling the cases when a component stops working and when new components, in this case a second robot, become available. The solution has been implemented as part of a stream reasoning framework integrated with the Robot Operating System (ROS).

Qualitative spatio-temporal reasoning is an active research area in Artificial Intelligence. In many situations there is a need to reason about intertemporal qualitative spatial relations, i.e. qualitative relations between spatial regions at different time-points. However, these relations can never be explicitly observed since they are between regions at different time-points. In applications where the qualitative spatial relations are partly acquired by for example a robotic system it is therefore necessary to infer these relations. This problem has, to the best of our knowledge, not been explicitly studied before. The contribution presented in this paper is two-fold. First, we present a spatio-temporal logic MSTL, which allows for spatio-temporal stream reasoning. Second, we define the concept of a landmark as a region that does not change between time-points and use these landmarks to infer qualitative spatio-temporal relations between non-landmark regions at different time-points. The qualitative spatial reasoning is done in RCC-8, but the approach is general and can be applied to any similar qualitative spatial formalism.

Computer science is becoming ever increasingly important to our society. Computer science content has, however, not traditionally been considered a natural part of curricula for primary and secondary education. Computer science has traditionally been primarily a university level discipline and there are no widely accepted general standards for what computer science at K-12 level entails. Also, as the interest in this area is rather new, the amount of research conducted in the field is still limited. In this paper we review how 10 different countries have approached introducing computer science into their K-12 education. The countries are Australia, England, Estonia, Finland, New Zealand, Norway, Sweden, South Korea, Poland and USA. The studied countries either emphasize digital competencies together with programming or the broader subject of computer science or computing. Computational thinking is rarely mentioned explicitly, but the ideas are often included in some form. The most common model is to make computer science content compulsory in primary school and elective in secondary school. A few countries have made it compulsory in both, while some countries have only introduced it in secondary school.

We have investigated competitive elements and different forms of feedback through automated assessment in a Data Structures and Algorithms course. It is given at the start of the second year and has about 140 students. In 2011 we investigated the effects of introducing competitive elements utilizing automated assessment. In 2012 we investigated how feedback through automated assessment on the labs influences student's ways of working, their performance, and their relations to the examining staff. The students get immediate feedback concerning correctness and efficiency. When judged correct the assistants make sure the program also fulfill requirements such as being well structured. After the course, we investigated the students attitudes to, and experiences from, using automated assessment via a questionnaire. 80% of the students are positive and it positively influenced their ways of working. 50% said they put in more effort because of automated judging. Moreover, assessment is seen as more objective as it is executed in the exact same manner for everyone. Both of these statements are confirmed by assessing the labs from 2011 using the same automated tool as was used in 2012. Our conclusions are that feedback through automated assessment gives wanted positive effects and is perceived as positive by the students.

Traditionally, theoretical foundations in data structures and algorithms (DSA) courses have been covered through lectures followed by tutorials, where students practise their understanding on pen-and-paper tasks. In this paper, we present findings from a pilot study on using the interactive e-book OpenDSA as the main material in a DSA course. The goal was to redesign an already existing course by building on active learning and continuous examination through the use of OpenDSA. In addition to presenting the study setting, we describe findings from four data sources: final exam, OpenDSA log data, pre and post questionnaires as well as an observation study. The results indicate that students performed better on the exam than during previous years. Students preferred OpenDSA over traditional textbooks and worked actively with the material, although a large proportion of them put off the work until the due date approaches.

Integrating logical and probabilistic reasoning and integrating reasoning over observations and predictions are two important challenges in AI. In this paper we propose P-MTL as an extension to Metric Temporal Logic supporting temporal logical reasoning over probabilistic and predicted states. The contributions are (1) reasoning over uncertain states at single time points, (2) reasoning over uncertain states between time points, (3) reasoning over uncertain predictions of future and past states and (4) a computational environment formalism that ground the uncertainty in observations of the physical world. Concrete robot soccer examples are given.

For the past decade, robotics has mostly focused on low-level sensing and control tasks such as sensor fusion, path planning, and manipulator design and control. At the same time, the field of Cogn...

Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.

Reasoning about time and space is essential for many applications, especially for robots and other autonomous systems that act in the real world and need to reason about it. In this paper we present a pragmatic approach to spatio-temporal stream reasoning integrated in the Robot Operating System through the DyKnow framework. The temporal reasoning is done in Metric Temporal Logic and the spatial reasoning in the Region Connection Calculus RCC-8. Progression is used to evaluate spatio-temporal formulas over incrementally available streams of states. To handle incomplete information the underlying first-order logic is extended to a three-valued logic. When incomplete spatial information is received, the algebraic closure of the known information is computed. Since the algebraic closure might have to be re-computed every time step, we separate the spatial variables into static and dynamic variables and reuse the algebraic closure of the static variables, which reduces the time to compute the full algebraic closure. The end result is an efficient and useful approach to spatio-temporal reasoning over streaming information with incomplete spatial information.

In 2013 Linköping University started the first 5-year engineering program in Computer Science and Software Engineering in Sweden. The goals of the program are to provide a holistic perspective on modern large scale software development, to provide a deep and broad understanding of computer science and computational thinking, and encourage innovation and entrepreneurship. The student response has been very good with more than 600 applicants to the 30 slots, of which more than 130 had this program as their first choice among all programs in Sweden. In this paper we present the goals, the design principles, and the resulting program. The ACM/IEEE CS Curricula has been used to make sure that the program provides a solid foundation in Computer Science. Three pedagogical ideas that we have used are (1) project courses to integrate theory and practice as well as provide experience with the most common form of working in industry; (2) courses that cover multiple programming paradigms and languages as well as multiple software development methodologies so that the students are prepared to take on the continual changes we know will come; and (3) a special course in engineering professionalism with groups of students from the first three years together reflecting on topics related to being a professional engineer. The paper concludes with a discussion about some important aspects such as computational thinking and the relation to the ACM/IEEE CS Curricula.

High level reasoning is becoming essential to autonomous systems such as robots. Both the information available to and the reasoning required for such autonomous systems is fundamentally incremental in nature. A stream is a flow of incrementally available information and reasoning over streams is called stream reasoning. Incremental reasoning over streaming information is necessary to support a number of important robotics functionalities such as situation awareness, execution monitoring, and decision making. This paper presents a practical framework for semantically grounded temporal stream reasoning called DyKnow. Incremental reasoning over streams is achieved through efficient progression of temporal logical formulas. The reasoning is semantically grounded through a common ontology and a specification of the semantic content of streams relative to the ontology. This allows the finding of relevant streams through semantic matching. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning framework is integrated in the Robot Operating System (ROS) thereby extending it with a stream reasoning capability.

As unmanned aerial vehicle (UAV) applications are becoming more complex and covering larger physical areas there is an increasing need for multiple UAVs to cooperatively solve problems. To produce more complete and accurate information about the environment we present the DyKnow Federation framework for distributed fusion among collaborative UAVs. A federation is created and maintained using a multi-agent delegation framework which allows high-level specification and reasoning about resource bounded cooperative problem solving. When the federation is set up, local information is transparently shared between the agents according to specification. The work is presented in the context of a multi-UAV traffic monitoring scenario.

Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols labeling physical objects and the sensor data being collected about them, a process called anchoring. In this paper we present a stream-based hierarchical anchoring framework extending the DyKnow knowledge processing middleware. A classification hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of classification, permitting symbolic reasoning on different levels of abstraction. The approach has been applied to a traffic monitoring application where an unmanned aerial vehicle collects information about a small urban area in order to detect traffic violations.

An implemented system for achieving high level situation awareness about traffic situations in an urban area is described. It takes as input sequences of color and thermal images which are used to construct and maintain qualitative object structures and to recognize the traffic behavior of the tracked vehicles in real time. The system is tested both in simulation and on data collected during test flights. To facilitate the signal to symbol transformation and the easy integration of the streams of data from the sensors with the GIS and the chronicle recognition system, DyKnow, a stream-based knowledge processing middleware, is used. It handles the processing of streams, including the temporal aspects of merging and synchronizing streams, and provides suitable abstractions to allow high level reasoning and narrow the sense reasoning gap.