We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using dont cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.

Machine Learning algorithms can predict patterns based on previous experiences. The overarching practice of Machine Learning includes both robotics (dealing with the real world) and the processing of data (the computer's equivalent of thinking). These algorithms find predictable, repeatable patterns that can be applied to eCommerce, Data Management, and new technologies such as driverless cars. The full impact of Machine Learning is just starting to be felt, and may significantly alter the way products are created, and the way people earn a living. Machine Learning algorithms are trained with large amounts of data, allowing the "robot" to learn and anticipate problems and patterns.

According to Clark's seminal work on common ground and grounding, participants collaborating in a joint activity rely on their shared information, known as common ground, to perform that activity successfully, and continually align and augment this information during their collaboration. Similarly, teams of human and artificial agents require common ground to successfully participate in joint activities. Indeed, without appropriate information being shared, using agent autonomy to reduce the workload on humans may actually increase workload as the humans seek to understand why the agents are behaving as they are. While many researchers have identified the importance of common ground in artificial intelligence, there is no precise definition of common ground on which to build the foundational aspects of multi-agent collaboration. In this paper, building on previously-defined modal logics of belief, we present logic definitions for four different types of common ground. We define modal logics for three existing notions of common ground and introduce a new notion of common ground, called salient common ground. Salient common ground captures the common ground of a group participating in an activity and is based on the common ground that arises from that activity as well as on the common ground they shared prior to the activity. We show that the four definitions share some properties, and our analysis suggests possible refinements of the existing informal and semi-formal definitions.

Tesla Chief Executive Elon Musk is seen in December in Australia. Tesla Chief Executive Elon Musk is seen in December in Australia. But can the electric car maker itself accelerate from producing 80,000 autos a year to 500,000 in 2018? Can it make money in the process? Here's how it works: Short sellers borrow shares in companies they think are overvalued.

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.

Scientists filming in the South Ocean off the coast of New Zealand captured this stunning footage of a blue whale eating a mass of krill. When you weigh 200 tons, even the smallest body movements require a lot of energy. That's why blue whales, Earth's largest animal, are picky eaters. Stunning new drone footage shows exactly how these massive mammals maneuver to feed on only the most nutritious patches of krill--providing insight on how they make these choices. Captured by a research team led by National Geographic Explorer Leigh Torres from Oregon State's Marine Mammal Institute, footage filmed in the Southern Ocean near New Zealand shows the moment a whale spots a patch of krill and sizes up whether it's worth expending energy.

A drone that can spot sharks and warn people has been developed by Australian researchers. The battery powered, unmanned drone uses an artificial intelligence technology to identify sharks and send out a safety warning through a megaphone. The drones will be used to patrol many main beaches in Australia from the summer of 2017 or 2018. The battery powered, unmanned drones uses an artificial intelligence technology to identify sharks and send out a safety warning through a megaphone. The drone works via real time analysis of overhead footage, and information can be relayed immediately to emergency services, beach lifeguards and beach users to help make safe decisions about getting into the water.

I have emerged, blinking, from the darkness of grant/paper writing purgatory (a.k.a December to March in Australia). It is time to get the blog going again, and to make up for the long gap in posts I'm going to start with the big one. The question I get every time I tell a colleague what I am working on, every time I give a lecture, every time I chat with someone new on social media. Over the course of the coming few blogposts, I intend to give my best answer to that question. I hope I can do it justice, because I don't really think it has been adequately explored elsewhere.

According to IBM CEO Ginni Rometty, it isn't going to be long ( 5 years-- according to the statement at ThinkForum) before every decision that is made by business is partly made by a cognitive system. These systems are touted as being systems that can learn, can understand and can help to define best practice in business. Last summer, when Rometty was speaking at Thinkforum in Sydney Australia, it didn't sound as though she considered it fiction on any level and in fact, much of what she's discussing already exists and is working to save us time and money. "Every industry has its Uber or Tesla, and many people say they are going to be a technology company of some kind. An important question is: When everyone is digital, who wins? "Digital for all has to be the foundation, but it's not the destination.

Australia's former world snooker champion Neil Robertson says an addiction to video games across his career has harmed both his professional and personal life. The 35-year-old Melburnian says longstanding obsessions with the Fifa football games, World of Warcraft and League of Legends deprived him of sleep and adversely affected his performances. "If you are a single guy and work in a normal job you can get around it," Robertson told Eurosport. "But you can't win professional snooker matches when you are tired." "The years I had the 100 centuries, I should probably have had around 120 because I got addicted like hell to Fifa 14," Robertson said. "That really affected the second half of my season.