Goal reasoning (GR) has a bright future as a foundation for the research and development of intelligent agents. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for some applications of deliberative autonomy. While studied in diverse AI sub-communities for multiple applications, our group has focused on how GR can play a key role for controlling autonomous systems. Thus, its importance is rapidly growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce GR, briefly relate it to other AI topics, summarize some of our group’s work on GR foundations and emerging applications, and describe some current and future research directions.

Russian scientists are developing an advanced automated submarine that will be powered by an external combustion engine, Igor Denisov, deputy director of the Foundation for Advanced Studies (FPI), revealed in an interview with Interfax, a Russian news agency. "We are planning to create an apparatus that will pass through the Northern Sea Route without floating up and without the use of nuclear power, including under the ice," Denisov said. "In order for this device to accomplish such a'feat,' its autonomy should be at least 90 days, which is already commensurate with the autonomy of modern submarines." The decision to forego the nuclear option to power the underwater vehicle was a conscious one, Denisov said, in order to make it increasingly safe. While a nuclear installation helps power submarines for uninterrupted movement throughout the world's oceans, it also puts its operational capabilities at risk.

To death and taxes, Benjamin Franklin's binary list of life's certainties, add the expectation that this six-note sequence: Although we ponder ways to avoid or evade Franklin's list of unavoidable events, we generally accept this more benign certainty as immutable. The penultimate note of the tune generates such strong and specific anticipation that you are likely finding it difficult to continue reading without resolving the sequence. That anxious pause is key to composition and music's power. It creates a sense of prophetic certainty that allows musicians to play against expectations by thwarting the expected. The controlled manipulation of certainty and likelihood lurks behind those magical moments in which music has caused a shiver or a tear to fall. By infusing uncertainty or surprise into the mix, musicians literally play on our emotions.

Prior to Spain's King Ferdinand and Queen Isabella financing Columbus' voyage to discover the New World, the Strait of Gibraltar was flanked by two pillars that bore the warning non plus ultra or, "Nothing further beyond," serving as a warning to Mediterranean sailors and navigators to go no further. However, Ferdinand and Isabella's grandson, Charles V, essentially reversed this line of thinking with his own motto, plus ultra, which translates to "Further beyond" or "limitless." I believe the American economy will change more in the next 10 years than it has in the 500 years since Charles V colonized the Americas. Transformative technologies like artificial intelligence, robotics, the internet of things (IoT), autonomous vehicles, virtual reality, digital traceability and blockchain will fundamentally change how we consume goods and services. These new market tools will create limitless opportunity, but they'll also create new challenges.

The map of Damascus in the first Assassin's Creed was 0.13km². Since then, the map in each successive game has been bigger than the one before. Based on that history, we can expect the map for the upcoming AC: Odyssey to be bigger than last year's brilliant AC: Origins. Dimitras Galatas put together a short video comparing maps from several of the Assassin's Creed games. The map of Greece for Odyssey is shown at 130km², a substantial increase over the tiny map of Damascus and more than 2.5 times as large as AC: Origin's 80km² map of Egypt.

Mars and the Earth are nestling closer than they have for 15 years. The phenomenon – known as a Mars approach – should allow people to see the Red Planet with the naked eye. For Nasa, it is also a perfect opportunity to take advantage of the relatively short distance and send off missions to explore Mars. That is why Mars missions tend to launch roughly every two years – since that is how often the close approaches happen. From the International Space Station, Expedition 42 Flight Engineer Terry W. Virts took this photograph of the Gulf of Mexico and U.S. Gulf Coast at sunset This image of an area on the surface of Mars, approximately 1.5 by 3 kilometers in size, shows frosted gullies on a south-facing slope within a crater.

Nasa is keeping close watch on the Opportunity rover in hope that it will wake up again. The until now unstoppable robot has run into trouble as it moves around the surface of Mars. The red planet is rapidly being encircled by a vast and unprecedented dust storm, meaning that it could be months until there is enough light to charge the rover's batteries and wake it back up again. Officials say they are hopeful the rover will make it through. But there will be no way to know until Opportunity wakes back up again – or doesn't, leaving it dead on the planet's surface.

Nasa's Opportunity rover may be dead on Mars, engineers fear. The little robot has been travelling over the planet for the last 15 years, but has lost contact with its team on Earth. They fear that the rover might never wake back up, and its mission could come to an end. Opportunity ran into problems in recent days when a Martian storm that is covering a quarter of the planet swept over the rover. That blotted out the Sun's light, leaving it unable to charge its batteries. From the International Space Station, Expedition 42 Flight Engineer Terry W. Virts took this photograph of the Gulf of Mexico and U.S. Gulf Coast at sunset This image of an area on the surface of Mars, approximately 1.5 by 3 kilometers in size, shows frosted gullies on a south-facing slope within a crater.

Abstract--In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification. In the world of a globalized economy, maritime surveillance is a vital demand. Besides, the real-time delivery of maritime situation maps is also necessary for a variety of activities: fishing activities control, smuggling detection, EEZ intrusion detection, transshipment detection, maritime pollution monitoring, etc. Over the last decades, the development of terrestrial networks and satellite constellations of Automatic Identification System (AIS) has opened a new era in maritime traffic surveillance. Every day, AIS provides on a global scale hundreds of millions of messages [1], which contain ships' identifiers, their Global Positioning System (GPS) coordinates, their speed, course, etc. The potential of this massive amount of data is clearly of interest if tools and models provide means to efficiently extract, detect and analyze relevant information from these data streams. However, current operational systems, which strongly rely on human experts, can only deal with a limited fraction of AIS data streams. Thus, the development of AIbased systems is a critical challenge.

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide. This study presents an Artificial Intelligence (AI)-based approach to identifying the primary cause of oyster norovirus outbreaks, nowcasting and forecasting the growing risk of oyster norovirus outbreaks in coastal waters. AI models were developed using Artificial Neural Networks (ANNs) and Genetic Programming (GP) methods and time series of epidemiological and environmental data. Input variable selection techniques, including Random Forests (RF) and Forwards Binary Logistic Regression (FBLR), were used to identify the significant model input variables among six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall and various combinations of the variables with different time lags. In terms of nowcasting, a risk-based GP model was developed to nowcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast, showing the true positive and negative rates of 78.53% and 88.82%, respectively.