Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.

Mathematically characterizing the implicit regularization induced by gradient-based optimization is a longstanding pursuit in the theory of deep learning. A widespread hope is that a characterization based on minimization of norms may apply, and a standard test-bed for studying this prospect is matrix factorization (matrix completion via linear neural networks). It is an open question whether norms can explain the implicit regularization in matrix factorization. The current paper resolves this open question in the negative, by proving that there exist natural matrix factorization problems on which the implicit regularization drives all norms (and quasi-norms) towards infinity. Our results suggest that, rather than perceiving the implicit regularization via norms, a potentially more useful interpretation is minimization of rank. We demonstrate empirically that this interpretation extends to a certain class of non-linear neural networks, and hypothesize that it may be key to explaining generalization in deep learning.

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. We aim to provide a review that demystifies dynamic networks, introduces dynamic graph neural networks (DGNNs) and appeals to researchers with a background in either network science or data science. We contribute: (i) a comprehensive dynamic network taxonomy, (ii) a survey of dynamic graph neural networks and (iii) an overview of how dynamic graph neural networks can be used for dynamic link prediction.

We consider approaches to the classical problem of establishing a statistical ranking on a given set of items from incomplete and noisy pairwise comparisons, and propose spectral algorithms able to leverage available covariate information about the items. We give a comprehensive study of several ways such side information can be useful in spectral ranking. We establish connections of the resulting algorithms to reproducing kernel Hilbert spaces and associated dependence measures, along with an extension to fair ranking using statistical parity. We present an extensive set of numerical experiments showcasing the competitiveness of the proposed algorithms with state-of-the-art methods.

Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP). With the burst of deep neural networks and the evolution of contextualized language models (CLMs), the research of MRC has experienced two significant breakthroughs. MRC and CLM, as a phenomenon, have a great impact on the NLP community. In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with a particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches. We propose a full-view categorization and new taxonomies on these topics. The primary views we have arrived at are that 1) MRC boosts the progress from language processing to understanding; 2) the rapid improvement of MRC systems greatly benefits from the development of CLMs; 3) the theme of MRC is gradually moving from shallow text matching to cognitive reasoning.

Software systems nowadays are complex and difficult to maintain due to continuous changes and bad design choices. To handle the complexity of systems, software products are, in general, decomposed in terms of packages/modules containing classes that are dependent. However, it is challenging to automatically remodularize systems to improve their maintainability. The majority of existing remodularization work mainly satisfy one objective which is improving the structure of packages by optimizing coupling and cohesion. In addition, most of existing studies are limited to only few operation types such as move class and split packages. Many other objectives, such as the design semantics, reducing the number of changes and maximizing the consistency with development change history, are important to improve the quality of the software by remodularizing it. In this paper, we propose a novel many-objective search-based approach using NSGA-III. The process aims at finding the optimal remodularization solutions that improve the structure of packages, minimize the number of changes, preserve semantics coherence, and re-use the history of changes. We evaluate the efficiency of our approach using four different open-source systems and one automotive industry project, provided by our industrial partner, through a quantitative and qualitative study conducted with software engineers.

We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset, and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.

RIA's research editor and communications manager, Fazila Farouk, talked to Caledon FM's Annette Jahnel on her Future Perfect show about the Fourth Industrial Revolution, artificial intelligence and its impact on white collar workers in South Africa. A major challenge she highlights is the fact that the jobs, which have traditionally allowed people entry into the middle class, are now disappearing. This is a major setback for a country ravaged by economic inequality.

The UK's job retention scheme, which pays 80 per cent of furloughed employees' wages up to £2500 a month, will be extended for four months until October. Rishi Sunak, the chancellor of the exchequer, said that from August employees will be allowed to work part-time while furloughed, but the government will require companies to shoulder some of the costs of furlough payments. The scheme currently covers the salaries of 7.5 million workers, a quarter of the UK's workforce, and costs the UK government about £14 billion a month. Head teachers have warned that the government's plan to reopen schools for some year groups in England on 1 June is not feasible. Paul Whiteman, head of the National Association for Head Teachers, told MPs that it wouldn't be possible to comply with the government's new guidance recommending a maximum class size of 15 pupils. Northern Ireland has unveiled a five-stage plan for easing coronavirus restrictions, which includes advice for specific job sectors and is ...

People in England can return to work if they can't work from home Restrictions to curb the spread of coronavirus are being eased slightly in England this week, but many have criticised the government for creating confusion with a new slogan telling people to "stay alert", which replaces previous advice to "stay at home." In a video message broadcast on Sunday evening, prime minister Boris Johnson announced the following changes to the government's policy in England, which are listed in full online and will come into effect from Wednesday 13 May: These new policies mean that social distancing rules in England are now different from the advice given to UK citizens in Scotland, Wales and Northern Ireland. Scotland's first minister Nicola Sturgeon said people should continue to "stay at home", and Northern Ireland's first minister Arlene Foster also rejected the new slogan. Some London Underground platforms were packed with passengers this morning following last night's announcement.