In the travelling thief problem (TTP), a thief undertakes a cyclic tour through a set of cities, and according to a picking plan, picks a subset of available items into a rented knapsack with limited capacity. The overall aim is to maximise profit while minimising renting cost. TTP combines two interdependent components: the travelling salesman problem (TSP) and the knapsack problem (KP). Existing TTP approaches typically solve the TSP and KP components in an interleaved fashion: the solution of one component is fixed while the solution of the other is changed. This indicates poor coordination between solving the two components, which may lead to poor quality TTP solutions. The 2-OPT heuristic is often used for solving the TSP component, which reverses a segment in the tour. Within the TTP context, 2-OPT does not take into account the picking plan, which can result in a lower objective value. This in turn can result in the tour modification to be rejected by a solver. To address this, we propose an extended form of 2-OPT in order to change the picking plan in coordination with modifying the tour. Items deemed as less profitable and picked in cities earlier in the reversed segment are replaced by items that tend to be equally or more profitable and not picked in cities later in the reversed segment. The picking plan is further changed through a modified form of the bit-flip search, where changes in the picking state are only permitted for boundary items, which are defined as lowest profitable picked items or highest profitable unpicked items. This restriction reduces the amount of time spent on the KP component, allowing more tours to be evaluated by the TSP component within a given time budget. The two modified heuristics form the basis of a new cooperative coordination solver, which is shown to outperform several state-of-the-art TTP solvers on a broad range of benchmark TTP instances.

And while we don't always know the full impact of such technological advancement, with Artificial Intelligence (AI) it's becoming increasingly clear that AI will disrupt nearly every industry in one way or another. While many people fear a future in which robots have eliminated human employees from the workforce, AI technology is not nearly that sophisticated yet. Sure, an autonomous train is already helping a company automate its mining operations in Australia, Google is making music with AI and a robot named Flippy has a job flipping burgers in Southern California, but the technology has quite a ways to go before it'll free us from all mundane and repetitive tasks. While experts argue whether AI will ultimately create more jobs than it destroys, we know it is already being used to help humans do their jobs better and augment the number of tasks they can handle concurrently. The technology has been around longer than most people realize.

Developing a master plan to transform John F. Kennedy International Airport in New York. Keeping beachgoers safe from polluted waters in New Zealand with advanced analytics. Those are just a few of the thousands of complex projects delivered each year by Mott MacDonald, a global engineering, management and development consulting firm headquartered in London. With 180 principal offices in 50 countries, the company helps solve some of the world's most urgent social, environmental and economic challenges. Because Mott MacDonald doesn't create physical products, its success relies on the knowledge and expertise of its 16,000 employees.

Artificial Intelligence in an open banking environment has revolutionised the way customers approach and leverage their financial information. The face of banking is changing across Australia and around the globe. With the launch of open banking and the Australian Consumer Data Right, the latest digital technologies and an ever-changing regulatory landscape, customers are demanding enhanced experiences and increased value for money. Artificial Intelligence (AI) is just one technology that is enabling banks and financiers to provide increased efficiencies behind the scenes, new product lines upfront, and to ensure a heightened customer experience for all. The customer experience has become the significant driver of any brand's growth and often their point of differentiation.

The implications of climate change are considered by many to be the greatest crisis confronting us globally. Most focus on the doomsday scenarios of melting glaciers, rising flood waters engulfing coastal cities, and species becoming extinct. The other challenge is feeding the world's growing populace. There are currently about 7.7 billion people living on Earth, and that number is projected to increase to almost 10 billion by 2050. Estimates put the number of those people who are chronically hungry at almost 1 billion even now.

Autonomous vehicles might be a double-edged sword for the driving sector, and they're well on the way - people with personal interest across the board are already sponsoring legislation to speed up the process of getting them onto our roads. With well-developed local autonomous driving laws already taking shape across various parts of Australia, law-makers are on track to see to a smooth transition that takes everyone on board. There are a number of predicted outcomes for how autonomous vehicles could impact real human drivers. In the worst-case scenario, autonomous vehicles might drain away jobs, taking millions from the sector. In a zero-sum scenario, there might be a decrease in certain types of driving-related jobs and an increase in others. And in the best case, we'll see an increase in desirable jobs and a reduction in more physically demanding jobs in the sector.

Pramod, 40, a software engineer with a large IT company, is having a mid-life crisis. Bored of writing hundreds of lines of code every day, he wants to quit the monotonous job and shift to a more challenging role that also gives him a good package. However, he is clueless on the right skill sets he needs to acquire. Pramod is not alone in this predicament. There are thousands of such mid-level software professionals who either want to make a course correction or have been told by their organisation to acquire new skills if they are to remain relevant.

Artificial intelligence should respect human rights, diversity and privacy -- while being a far cry from Terminator-style robots -- according to new federal ethics guidelines. Technology Minister Karen Andrews will today release an eight-point guidance she wants companies to adopt in a bid to prevent people from being exploited. The guidelines stipulate all AI should benefit individuals, society and the environment. It should prevent discrimination, respect privacy and only operate in accordance with their intended purpose. The guidelines also recommend human oversight of AI always be enabled and there should be timely processes to allow people to challenge the use or output of information.

Some of the biggest businesses in Australia will trial a series of eight principles around artificial intelligence, developed as part of the Morrison Government's AI Ethics Framework. NAB, Commonwealth Bank, Telstra, Microsoft and Flamingo AI have signed up to test the principles to ensure they deliver practical benefits and translate into real world solutions. Minister for Industry, Science and Technology Karen Andrews said AI is a powerful technology that can create jobs, boost the economy and improve our quality of life and is an important part of the Government's economic plan. "The Morrison Government is determined to create an environment where AI helps the economy and everyday Australians to thrive. The eight AI ethics principles are just one part of this vision," Minister Andrews said.

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load responses over traditional load models in various stability studies and contingency analyses. However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters. Enabled by the wide deployment of PMUs and advanced deep learning algorithms, proposed here is a double deep Q-learning network (DDQN)-based, two-stage load modeling framework for the WECC CLM. This two-stage method decomposes the complicated WECC CLM for more efficient identification and does not require explicit model details. In the first stage, the DDQN agent determines an accurate load composition. In the second stage, the parameters of the WECC CLM are selected from a group of Monte-Carlo simulations. The set of selected load parameters is expected to best approximate the true transient responses. The proposed framework is verified using an IEEE 39-bus test system on commercial simulation platforms.