The introduction of the concept of state novelty has advanced the state of the art in deterministic online planning in Atari-like problems and in planning with rewards in general, when rewards are defined on states. In classical planning, however, the success of novelty as the dichotomy between novel and non-novel states was somewhat limited. Until very recently, novelty-based methods were not able to successfully compete with state-of-the-art heuristic search based planners. In this work we adapt the concept of novelty to heuristic search planning, defining the novelty of a state with respect to its heuristic estimate. We extend the dichotomy between novel and non-novel states and quantify the novelty degree of state facts. We then show a variety of heuristics based on the concept of novelty and exploit the recently introduced best-first width search for satisficing classical planning. Finally, we empirically show the resulting planners to significantly improve the state of the art in satisficing planning.

Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to significantly improve traffic flow efficiency in complex urban road networks. However, in situations where vehicle volumes increase to the point that the physical capacity of a road network reaches or exceeds saturation, it has been observed that the effectiveness of a schedule-driven approach begins to degrade, leading to progressively higher network congestion. In essence, the traffic control problem becomes less of a scheduling problem and more of a queue management problem in this circumstance. In this paper we propose a composite approach to real-time traffic control that uses sensed information on queue lengths to influence scheduling decisions and gracefully shift the signal control strategy to queue management in high volume/high congestion settings. Specifically, queue-length information is used to establish weights for the sensed vehicle clusters that must be scheduled through a given intersection at any point, and hence bias the wait time minimization calculation. To compute these weights, we develop a model in which successive movement phases are viewed as different states of an Ising model, and parameters quantify strength of interactions. To ensure scalability, queue information is only exchanged between direct neighbors and the asynchronous nature of local intersection scheduling is preserved. We demonstrate the potential of the approach through microscopic traffic simulation of a real-world road network, showing a 60% reduction in average wait times over the baseline schedule-driven approach in heavy traffic scenarios. We also report initial field test results, which show the ability to reduce queues during heavy traffic periods.

Dynamic Controllability (DC) of a Simple Temporal Problem with Uncertainty (STPU) uses a dynamic decision strategy, rather than a fixed schedule, to tackle temporal uncertainty. We extend this concept to the Controllable Conditional Temporal Problem with Uncertainty (CCTPU), which extends the STPU by conditioning temporal constraints on the assignment of controllable discrete variables. We define dynamic controllability of a CCTPU as the existence of a strategy that decides on both the values of discrete choice variables and the scheduling of controllable time points dynamically. This contrasts with previous work, which made a static assignment of choice variables and dynamic decisions over time points only. We propose an algorithm to find such a fully dynamic strategy. The algorithm computes the ''envelope'' of outcomes of temporal uncertainty in which a particular assignment of discrete variables is feasible, and aggregates these over all choices. When an aggregated envelope covers all uncertain situations of the CCTPU, the problem is dynamically controllable. However, the algorithm is not complete. Experiments on an existing set of CCTPU benchmarks show that there are cases in which making both discrete and temporal decisions dynamically it is feasible to satisfy the problem constraints, while assigning the discrete variables statically it is not.

We investigate compositional bounding of transition system diameters, with application in bounding the lengths of plans. We establish usefully-tight bounds by exploiting acyclicity in state-spaces. We provide mechanised proofs in HOL4 of the validity of our approach. Evaluating our bounds in a range of benchmarks, we demonstrate exponentially tighter upper bounds compared to existing methods. Treating both solvable and unsolvable benchmark problems, we also demonstrate the utility of our bounds in boosting planner performance. We enhance an existing planning procedure to use our bounds, and demonstrate significant coverage improvements, both compared to the base planner, and also in comparisons with state-of-the-art systems.

An Australian startup revealed its flagship product, an earpiece that can interpret 8 different languages in real-time, at a United Nations event in Switzerland on Friday. Lingmo International, a startup based in West Gosford north of Sydney, launched its TranslateOne2One earpiece at the UN's Artificial Intelligence for Good Summit in Geneva, revealing that IBM Watson machine learning technology had been used for its algorithms. Traditionally, converting one language to another orally in real-time is called "interpreting" whereas the term "translation" is reserved for processing text across languages with some delay. Lingmo founder Danny May, however, describes his product as performing "translation in real-time". And what I mean by independent is that it doesn't require any connectivity to your phone by Bluetooth or wi-fi.

The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free. The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson.

FaceApp is currently one of Australia's most popular apps, racking up over a million downloads in its first two weeks¹. You may have seen someone share its collages on a social feed: with various versions of their face as older, younger, the opposite sex, or more, shall we say, 'sparkly'. The app is one of the latest in a string of facial augmented reality experiences – such as Snapchat and Prisma – which allow users to manipulate reality by altering an image of their face. Users go on an immersive journey, exploring visuals to catch a glimpse of their possible selves. Facial manipulation apps use a form of artificial intelligence that automatically edits pictures to show possible selves.

A few years back, cloud computing transformed customer management, giving every small and medium business access to unified data and communication platforms without the need to make heavy investments in IT infrastructure and staff. This time around, the next revolution in the space is being driven by artificial intelligence algorithms that help businesses automate customer outreach and make optimal use of data. Beneath the surface of the roiling sea of data we're generating hide exceptional business and sales opportunities. But the problem is there's now more information available than limited human resources and legacy tools can handle. Fortunately, making sense of data, both structured and unstructured, is something that artificial intelligence is becoming increasingly proficient at.

For self-driving cars and other smart transport to be successfully integrated in the real-world, the safety of passengers and pedestrians must be ensured. In the world of intelligent machines, perception answers the question: what is around me? This situational awareness is paramount for safe operation of autonomous vehicles in real-world environments. Scientists working in this field point to robotic perception as fundamental in equipping machines with a semantic understanding of the world, so that they can reliably identify objects and make informed predictions and actions. Michael Milford, Associate Professor at Queensland University of Technology (QUT), is a leading robotics researcher working to improve perception and more in autonomous vehicles, conducting his research at the intersection of robotics, neuroscience and computer vision.

NZTech CEO Graeme Muller says New Zealanders are engaging with artificial intelligence (AI) everyday, probably without even realising it. Muller made the comments on the eve of the launch of the New Zealand AI Forum in Wellington, describing AI as "the fastest growing impactful technology spreading the globe." The forum was announced in February with further details being provided in late May, including the naming of Stu Christie, investment manager at the New Zealand Venture Investment Fund, as its chairman. NZTech says dozens of New Zealand's leading tech companies are joining the forum, which has been initiated via a collaboration between NZTech, the government and AI tech leaders. According to Muller, one of the most recent examples of AI in New Zealand is the chat function on the Air New Zealand website that helps with ticket bookings: users of that function are chatting with AI not a human, Muller said.