In cooperative pathfinding problems, no-conflicts paths that bring several agents from their start location to their destination need to be planned. This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than the classical algorithms, such as Optimal Anytime(OA), in sparse environments. However, the implementation of this algorithm in systems with limited memory is hindered because the number of nodes in the tree grows indefinitely as the paths get optimized. This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the number of nodes stored in the tree by removing the weak nodes which are not likely on the path reaching the goal. The results show that MA-RRT*FN performs close to MA-RRT* in terms of scalability and solution quality while the memory required is much lower and fixed.

To gauge the complexity of the juggling act utility firms must perform to stay in business, consider two statistics. In the four years to 2018, the number of Britons who switched their energy supplier almost doubled to 5.9 million; at the same time, the contribution of renewables to energy firms' output mix grew from about 13% to just shy of 20%. One represents a fundamental change in consumer expectations of the service they receive while the other highlights the political and environmental pressures being brought to bear on suppliers' operations. To the list of challenges that are adding to the pressures under which utilities operate, add the tightening – and disparate – the grip of regulators, the fracturing of transmission networks and the increasing influence of activist investors. Managing these changing times can be incredibly challenging for established utilities, especially at a time when technology is enabling venture-backed start-ups to move into niche segments of their operations.

As an industry defined by the relationship between patient and carer, at first glance it may seem incongruous to nudge healthcare towards a robotic future. In fact, artificial intelligence (AI) has the potential to completely reshape the health industry, offering greater support to human capabilities and allowing healthcare organizations to deliver higher-quality services more efficiently. AI is a broad term for computer systems that can "think" and act like humans. They can sense their environment, absorb information, learn from past experience, make decisions and take action. AI has transformative power for two reasons: the explosive growth in data, coupled with huge computational advances and processing speeds.

Edge computing is helping Newcrest Mining improve throughput and reduce downtime in Australia's largest underground block cave mine, the Cadia Valley gold mine in New South Wales. Newcrest Mining won the best Primary Industry Project in our 2019 IoT Awards for the project, which uses machine learning to optimise the level of crushed ore in bins, preventing downtime. Now Microsoft and its partner Insight Enterprises have released details about the solution and its benefits. The solution is improving productivity, reducing downtime and increasing throughput, Newcrest Mining CIO Gavin Wood stated in a press release. And the company has seen a return on investment within three months of starting to use the solution.

Civil court leaders have a newly strong interest in how artificial intelligence can improve the quality and efficiency of legal services in the justice system, especially for problems that self-represented litigants face [1, 2, 3, 4, 5]. The promise is that artificial intelligence can address the fundamental crises in courts: that ordinary people are not able to use the system clearly or efficiently; that courts struggle to manage vast amounts of information; and that litigants and judicial officials often have to make complex decisions with little support. If AI is able to gather and sift through vast troves of information, identify patterns, predict optimal strategies, detect anomalies, classify issues, and draft documents, the promise is that these capabilities could be harnessed for making the civil court system more accessible to people. The question then, is how real these promises are, and how they are being implemented and evaluated. Now that early experimentation and agenda-setting have begun, the study of AI as a means for enhancing the quality of justice in the civil court system deserves greater definition.

A study by the University of London and Vanson Bourne for WP Engine has concluded that transparency, trust and humanness are key drivers to unlock value in artificial intelligence (AI). The study, which surveyed consumers and enterprise companies with 1,000 employees or more in the US, the UK and Australia, found a large proportion of businesses are already well on their way towards widespread AI implementation. Chris Brauer, director of innovation at Goldsmiths, University of London, said: "Our research shows enterprises investing in AI are already seeing astounding return on investment and performance outcomes. Consumers are demanding that innovating with AI in digital experiences clearly prioritises and expresses values around privacy, trust, and transparency. "Only by laying a solid foundation of ethics and values that guide the implementation of all facets of an AI solution will companies truly be able to fully harness the value of AI." The study reported that 85.5% of businesses ...

CHINA is selling deadly'Blowfish' drones which can decide who lives and who dies to armies in the war-torn Middle East, say reports. The unmanned war machines are capable of launching autonomous strikes with their arsenal of mortar shells, grenade launchers and machine guns. They are said to be "impossible to defend" against and the Pentagon has already made it clear it fears they will end up in the wrong hands. Some military experts fear the proposed sale of the AI mini-choppers will spark even more bloodshed in the troubled region, reports news.com. "They would be impossible to defend yourself against," warns University of New South Wales Professor of Artificial Intelligence Toby Walsh.

Our third interview is with Sid Dutta of MD APAC Kantar Profiles Division, Nuchy Tungwarapojwitan of MD Intage Thailand, and Joel Vermaas of Nature Research Australia. Watch how they collaboratively share some insightful information about the future of research and how AI can change everything. For more episodes of APACInsight, make sure to subscribe to our channel. Use this code "INNOVATION30" to save 30% on your ticket.

China is selling its most advanced "fully autonomous" military drones with fears that it could lead to a bloodbath in the Middle East. The Asian superpower is reportedly selling AI-enhanced combat drones to the region, with potentially disastrous consequences. Prof Toby Walsh, of the University of NSW, in Australia, said: "They would be impossible to defend yourself against. "Once the shooting starts, every human on the battlefield will be dead." US Defence Sec Mark Esper has said that China is selling drones programmed to decide themselves who lives or dies. He told a conference on Artificial Intelligence: "As we speak, the Chinese government is already exporting some of its most advanced military aerial drones to the Middle East as it prepares to export its next generation stealth UAVs when those come online.

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast arrayof these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information. This model incorporates sentiment information into bilingual distributional representations, byjointly optimizing them for semantics and sentiment, showing state-of-the-art performance at sentence-level when combined with machine translation. The adaptation to targeted sentiment analysis on multiple domains shows that our model outperforms other projection-based bilingual embedding methods on binary targetedsentiment tasks. Our analysis on ten languages demonstrates that the amount of unlabeled monolingual data has surprisingly little effect on the sentiment results. As expected, the choice of a annotated source language for projection to a target leads to better results for source-target language pairs which are similar. Therefore, our results suggest that more efforts should be spent on the creation of resources for less similar languages tothose which are resource-rich already. Finally, a domain mismatch leads to a decreased performance. This suggests resources in any language should ideally cover varieties of domains.