Antony Thomas and Sunny Amatya † and Fulvio Mastrogiovanni and Marco Baglietto Abstract -- We present an integrated T ask-Motion Planning framework for robot navigation in belief space. Autonomous robots operating in real world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. T o this end, we propose a framework for integrating belief space reasoning within a hybrid task planner . The expressive power of PDDL combined with heuristic-driven semantic attachments performs the propagated and posterior belief estimates while planning. The underlying methodology for the development of the combined hybrid planner is discussed, providing suggestions for improvements and future work. I NTRODUCTION Autonomous robots operating in complex real world scenarios require different levels of planning to execute their tasks. High-level (task) planning helps break down a given set of tasks into a sequence of sub-tasks, actual execution of each of these sub-tasks would require low-level control actions to generate appropriate robot motions. In fact, the dependency between logical and geometrical aspects is pervasive in both task planning and execution. Hence, planning should be performed in the task-motion or the discrete-continuous space. In recent years, combining high-level task planning with low-level motion planning has been a subject of great interest among the Robotics and Artificial Intelligence (AI) community.

Learning-Based Video Game Development in MLP@UoM: An Overview * Ke Chen, Senior Member, IEEE Department of Computer Science, The University of Manchester, Manchester M13 9PL, U.K. Email: Ke.Chen@manchester.ac.uk Abstract --In general, video games not only prevail in entertainment but also have become an alternative methodology for knowledge learning, skill acquisition and assistance for medical treatment as well as health care in education, vocational/military training and medicine. On the other hand, video games also provide an ideal test bed for AI researches. T o a large extent, however, video game development is still a laborious yet costly process, and there are many technical challenges ranging from game generation to intelligent agent creation. Unlike traditional methodologies, in Machine Learning and Perception Lab at the University of Manchester (MLP@UoM), we advocate applying machine learning to different tasks in video game development to address several challenges systematically. In this paper, we overview the main progress made in MLP@UoM recently and have an outlook on the future research directions in learning-based video game development arising from our works. I NTRODUCTION The video games industry has drastically grown since its inception and even surpassed the size of the film industry in 2004. Nowadays, the global revenue of the video industry still rises and increases, and the widespread availability of high-end graphics hardware have resulted in a demand for more complex video games. This in turn has increased the complexity of game development. In general, video games not only prevail in entertainment but also have become an alternative methodology for knowledge learning, skill acquisition and assistance for medical treatment as well as health care in education, vocational/military training and medicine. From an academic perspective, video games also provide an ideal test bed, which allows for researching into automatic video game development and testing new AI algorithms in such a complex yet well-structured environment with ground-truth.

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi-Sugeno-Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper offers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems.

Unmanned aircraft systems can perform some more dangerous and difficult missions than manned aircraft systems. In some highly complicated and changeable tasks, such as air combat, the maneuvering decision mechanism is required to sense the combat situation accurately and make the optimal strategy in real-time. This paper presents a formulation of a 3-D one-on-one air combat maneuvering problem and an approximate dynamic programming approach for computing an optimal policy on autonomous maneuvering decision making. The aircraft learns combat strategies in a Reinforcement Leaning method, while sensing the environment, taking available maneuvering actions and getting feedback reward signals. To solve the problem of dimensional explosion in the air combat, the proposed method is implemented through feature selection, trajectory sampling, function approximation and Bellman backup operation in the air combat simulation environment. This approximate dynamic programming approach provides a fast response to a rapidly changing tactical situation, learns in long-term planning, without any explicitly coded air combat rule base.

This is a preprint of a column that has been accepted for publication as a "Viewpoint" in an upcoming issue of Communications of the ACM. According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers. AI technologies offer the possibility of removing many accessibility barriers; for example, computer vision might help people who are blind better sense the visual world, speech recognition and translation technologies might offer real time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with limited mobility. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users; however, ethical challenges such as inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered.

Early Tuesday, Hezbollah changed its description of the first drone, saying that it had been carrying 12 pounds of explosives. The group's officials had previously said the first drone was for surveillance while the second was laden with explosives. Hezbollah is a political party represented in the Lebanese government as well as the country's most formidable military force. It fought a devastating war against Israel in 2006 and has more recently sent fighters to help President Bashar al-Assad of Syria fight rebels seeking to overthrow him. Israel and Iran have long been staunch enemies, but the conflict has heated up recently as Israel has increasingly attacked armed groups that Iran backs to extend its influence in the Arab world.

Federal standards for artificial intelligence must be strict enough to prevent the tech from harming humans, yet flexible enough to encourage innovation and get the tech industry on board, according to the National Institute of Standards and Technology. However, without better standards for measuring the performance and trustworthiness of AI tools, officials said, the government could have a tough time striking that balance. On Monday, NIST released its much-anticipated guidance on how the government should approach developing technical and ethical standards for artificial intelligence. Though it doesn't include any specific regulations or policies, the plan outlines multiple initiatives that would help the government promote the responsible use of AI and lists a number of high-level principles that should inform any future standards for the tech. The strategy also stresses the need to develop technologies that would help agencies better study and assess the quality of AI-powered systems.

Imagine a scene from the near-future: You get dropped off downtown by a driverless car. You slam the door and head into your office or appointment. But then where does the autonomous vehicle go? It's a question that cities would be wise to consider now. Self-driving cars may be on the roads within the next decade or two.

The use of artificial intelligence (AI) to drive campaigns and experiences is one of the most exciting prospects for digital marketers and e-commerce professionals in the coming years, more so than virtual or augmented reality and the internet of things (IoT). The latest Digital Trends report from Econsultancy and Adobe also reveals that top-performing companies are more than twice as likely as mainstream companies to already be putting AI to use. The study is based on a survey of almost 12,800 digital marketing and e-commerce professionals from the client-side (60%) and supply-side (40%), with respondents hailing primarily from Europe (44%), followed by Asia (21%), North America (16%) and other regions. Top-performing companies are those who exceeded their business goals and believe that they're outperforming their competitors. Some 28% of these organizations say they're already using AI, compared to 12% of "mainstream companies."

Hoteliers in Hungary must be loving life right about now. In 2018, Hungary set a record of 31 million hotel nights booked by tourists. From obnoxious British stag do goers to snap-happy Chinese mainlanders, the streets of Budapest – that's Hungary's capital city for the geographically challenged – are chock full of people enjoying the "Paris of the East." It's a city built on a vast network of underground caves and springs which means there's plenty of sauna action to be found, not just for the sort of lads who cruise Soho's Sweatbox on weekday nights. We recently airdropped one of our MBAs into the city center to bathe in its healing waters and speak with a startup that's trying to heal people using artificial intelligence.