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.

In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies or other factors. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research.

Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discuss the application of GIS into the Sequential Importance Resampling framework and show that Independent Multiple Try Metropolis schemes can be interpreted as a standard Metropolis-Hastings algorithm, following the GIS approach. We also introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS. The first one, named Group Metropolis Sampling method, produces a Markov chain of sets of weighted samples. All these sets are then employed for obtaining a unique global estimator. The second one is the Distributed Particle Metropolis-Hastings technique, where different parallel particle filters are jointly used to drive an MCMC algorithm. Different resampled trajectories are compared and then tested with a proper acceptance probability. The novel schemes are tested in different numerical experiments such as learning the hyperparameters of Gaussian Processes, the localization problem in a wireless sensor network and the tracking of vegetation parameters given satellite observations, where they are compared with several benchmark Monte Carlo techniques. Three illustrative Matlab demos are also provided.

Starting from a generalization of the standard axioms for a monoid we present a stepwise development of various, mutually equivalent foundational axiom systems for category theory. Our axiom sets have been formalized in the Isabelle/HOL interactive proof assistant, and this formalization utilizes a semantically correct embedding of free logic in classical higher-order logic. The modeling and formal analysis of our axiom sets has been significantly supported by series of experiments with automated reasoning tools integrated with Isabelle/HOL. We also address the relation of our axiom systems to alternative proposals from the literature, including an axiom set proposed by Freyd and Scedrov for which we reveal a technical issue (when encoded in free logic where free variables range over defined and undefined objects): either all operations, e.g. morphism composition, are total or their axiom system is inconsistent. The repair for this problem is quite straightforward, however.

Chloe Bridgewater is only 7 years old but she's already getting paid to test products for a computer company. Her new side hustle comes only a few months after she wrote a letter asking "Google boss" Sundar Pichai for a future job in tech. SEE ALSO: This 7-year-old is not giving up after Google CEO's sweet rejection letter Since her adorably honest and earnest letter went viral, Chloe and her family in Hereford, England, have gotten a lot of attention. "It's been absolutely bonkers," her mom, Julie Bridgewater, said in a video call Thursday. Chloe's letter got even more attention when Pichai responded with a signed letter and encouraged Chloe to stay interested in coding, computers, robots, and math.

The robotics work programme implements the robotics strategy developed by SPARC, the Public-Private Partnership for Robotics in Europe (see the Strategic Research Agenda). A wide variety of research and innovation themes are represented in the new projects: from healthcare via transportation, industrial- and logistics robotics to events media production using drones. Some deal with complex safety matters on the frontier where robots meet people, to ensure that no one comes to harm. Others will create a sustainable ecosystem in the robotics community, setting up common platforms supporting robotics development. The projects are either helping humans in their daily lives at home or at work, collaborating with humans to help them with difficult, strenuous tasks, or taking care of dangerous tasks, reducing the risk to humans.

On Monday 20 April, a crowd gathered in Kings Place to hear a discussion on the future of Artificial Intelligence - or AI - as part of our Brainwaves Series, supported by SEAT. How do we define human intelligence? How close are we to reaching it with machines? And what happens when these machines start taking our jobs? To discuss all this and more, Ian Sample was joined on stage was Anil Seth, professor of cognitive science and computational neuroscience from the University of Sussex, Maja Pantic, professor of affective and behavioural computing at Imperial College London, Anders Sandberg, senior research fellow at Oxford University's Future of Humanity Institute, and Alan Winfield, professor of robot ethics at UWE, Bristol.

Police in Wales plan to use facial recognition on fans during the Champions League final in Cardiff on 3 June, according to a government contract posted online. Faces will be scanned at the Principality Stadium and Cardiff's central railway station. They can then be matched against 500,000 "custody images" stored by local police forces. South Wales Police confirmed the pilot and said it was a "unique opportunity". A report on the plan was first posted by tech news site Motherboard.

How can we create robots that can carry out important tasks in dangerous environments? Machine learning is supporting advances in the field of robotics. To find out more, we talked to Dr Rustam Stolkin, Royal Society Industry Fellow for Nuclear Robotics, Professor of Robotics at the University of Birmingham, and Director at A.R.M Robotics Ltd, about his work combining machine learning and robotics to create practical solutions to nuclear problems. There are many definitions of engineering, but the one I like is "the creation of artefacts for the benefit of mankind". Engineering is a way of using science to be creative and to create novel technologies which can bring major societal and economic benefit.

L'Oréal has launched an artificial intelligence-powered Facebook Messenger bot as part of a drive to produce services for its beauty brands and learn more about consumers. In partnership with startup Automat Technologies, the beauty giant has developed a series of beauty services that will launch in the coming months on the Facebook Messenger platform. The first L'Oréal service, launching in Canada, will be a gifting service on the Messenger platform. The service looks to help consumers to find the right beauty box gift for their friends based on a series of questions that define their friend's beauty profile and help consumers select the right brand and product mix at the right budget. The company says it hopes to launch the service in the UK early 2018. Over the past two years, L'Oréal has embarked on a journey of digital transformation, in which it has aimed to build more services and products for its brands.