Timeline-based planning is an approach originally developed in the context of space mission planning and scheduling, where problem domains are modelled as systems made of a number of independent but interacting components, whose behaviour over time, the timelines, is governed by a set of temporal constraints. This approach is different from the action-based perspective of common PDDL-like planning languages. Timeline-based systems have been successfully deployed in a number of space missions and other domains. However, despite this practical success, a thorough theoretical understanding of the paradigm was missing. This thesis fills this gap, providing the first detailed account of formal and computational properties of the timeline-based approach to planning. In particular, we show that a particularly restricted variant of the formalism is already expressive enough to compactly capture action-based temporal planning problems. Then, finding a solution plan for a timeline-based planning problem is proved to be EXPSPACE-complete. Then, we study the problem of timeline-based planning with uncertainty, that include external components whose behaviour is not under the control of the planned system. We identify a few issues in the state-of-the-art approach based on flexible plans, proposing timeline-based games, a more general game-theoretic formulation of the problem, that addresses those issues. We show that winning strategies for such games can be found in doubly-exponential time. Then, we study the expressiveness of the formalism from a logic point of view, showing that (most of) timeline-based planning problems can be captured by Bounded TPTL with Past, a fragment of TPTL+P that, unlike the latter, keeps an EXPSPACE satisfiability problem. The logic is introduced and its satisfiabilty problem is solved by extending a recent one-pass tree-shaped tableau method for LTL.

This ebook, based on the latest ZDNet/TechRepublic special feature, offers a detailed look at how to build risk management policies to protect your critical digital assets. Security measures have increased significantly in the last several years, and malicious actors have similarly advanced their techniques to keep pace, particularly with advances in attack methods such as fileless malware. Likewise, the security model of'serverless' computing platforms like AWS Lambda are completely different from traditional computers. These itinerant computing concepts are not effectively secured by the traditional model of checking file hashes against known malware samples. For a robust, modern defense, an adaptive monitoring solution that leverages machine learning to identify anomalous patterns indicative of an attack in its infancy is necessary to defend enterprise systems from cyberattacks.

The Pentagon's new artificial-intelligence strategy, released on Tuesday, aims to get AI out of research labs and into the hands of troops and employees across the Defense Department. But truly transforming the Defense Department into an "AI First" institution will require help from tech companies -- and the military to rethink its approach to the massive data streams that AI needs to work. In a conversation with reporters on Tuesday, Dana Deasy, chief information officer of the Defense Department, and Lt. Gen. Jack Shanahan, who runs its new Joint Artificial Intelligence Center, said the JAIC will develop AI tools and programs to assist with everything the Pentagon does. That will eventually include combat operations, although both said the military won't deviate from its core doctrine that dictates how humans are to have authority over autonomous systems. They said near-term projects include efforts to predict forest fires, better spot network anomalies that can indicate cyber attacks, and, most prominently, predictive maintenance.

It's been an eventful week in tech. Amazon announced it would abandon plans to open one of its two HQ2 locations in New York City, and the company also acquired Wi-Fi mesh network startup Eero for an undisclosed sum -- a hint at Amazon's future smart home ambitions. The California Department of Motor Vehicles released reports from companies currently testing self-driving cars -- like Apple, Alphabet's Waymo, and GM Cruise. Google pledged to spend $13 billion on U.S. datacenters and offices in 24 states this year, and driverless truck startup TuSimple raised $95 million at a $1 billion valuation, joining the ranks of Aurora and Nuro as one of the best-funded companies in the autonomous vehicle industry. Nearly lost in the shuffle was President Trump's signing on Monday of an executive order establishing a program -- the American AI Initiative -- that formalizes several of the proposals made last spring during the White House's summit on AI.

It's hard to miss the dogged technological ambition pervading this sprawling desert metropolis. In Scottsdale, Axon, the maker of the Taser, is hungrily snatching talent from Silicon Valley as it embraces automation to keep up with growing demand. Start-ups in fields as varied as autonomous drones and blockchain are flocking to the area, drawn in large part by light regulation and tax incentives. Arizona State University is furiously churning out engineers. And yet for all its success in drawing and nurturing firms on the technological frontier, Phoenix cannot escape the uncomfortable pattern taking shape across the American economy: Despite all its shiny new high-tech businesses, the vast majority of new jobs are in workaday service industries, like health care, hospitality, retail and building services, where pay is mediocre.

Cardiovascular disease (CVD) is the leading cause of death worldwide. About 610,000 people die of heart attacks and strokes in the U.S. every year, according to the Center for Disease Control and Prevention, and worldwide, the number stands at about 17.9 million. CVD isn't impossible to predict, fortunately -- there's a strong risk factor in coronary artery calcium (CAC) deposits that restrict blood flow. Unfortunately, measuring CAC requires experts who can closely inspect computerized tomography (CT) scans for worsening signs and symptoms. But there's hope yet for a more automated approach.

The US Department of Defense (DoD) has released a document to summarise the 2018 strategy for artificial intelligence (AI) in response to the US President Donald Trump's signing an executive order to launch the American Artificial Intelligence Initiative. Released on 12 February the strategy states that the DoD has outlined the Joint Artificial Intelligence Center (JAIC) as the focal point for carrying out the strategy. As well as enabling consistency of approach, technology, and tools, the work of JAIC will complement the efforts of the Defense Advanced Research Projects Agency (DARPA), DoD laboratories, and other entities focused on longer-term technology creation and future AI research and development (R&D). For analysis on this article and access to all our insight content, please enquire about our subscription options at ihsmarkit.com/janes

The Pentagon said it is using its push into artificial intelligence to find ways to improve how wildfires are fought. The Pentagon has launched a program to use artificial intelligence (AI) to analyze data collected by drones to improve how wildfires are fought. The program is one of two efforts unveiled by the U.S. Department of Defense (DoD) reflecting the agency's new AI strategy to work with academia and industry to fast-track adoption of advanced data-management techniques. One project use algorithms to assess still photo and video imagery to predict the paths of wildfires and improve efforts to contain them. The second project uses data from sensors on helicopters used by special-operations forces to predict when the vehicles might require maintenance.

The Executive Order on Maintaining American Leadership in Artificial Intelligence, issued on February 11th, 2019, takes the U.S. in the right direction by directing Executive branch Agencies to consider and prioritize AI across several dimensions. It recognizes that success in Artificial Intelligence is a national security issue for the U.S., which has not always been fully acknowledged. It also highlights important data and workforce issues, which are critical prerequisites to any success in the AI domain. In general, I applaud the Office of Science and Technology Policy for making the effort and driving attention to this issue. However, recognizing that AI is a broad and deep issue and that coordinating ANY activity across the entire Federal government is complicated and fraught with challenge, I still believe the Executive Order (EO) falls well short.

IBM wants to develop next-generation artificial intelligence chips, and it's building a new AI research center and partnering with academia and other tech companies to do it. At the recently announced future AI Hardware Center at SUNY Polytechnical Institute in Albany, New York, IBM researchers will collaborate with academic researchers and tech partners to develop, prototype, and test new AI chips and systems. Initial partners include Samsung, Mellanox Technologies, Synopsis, Applied Materials, and Tokyo Electron. Related: Intel Steps Up Its Challenge to Nvidia's AI Chip Dominance, with Facebook's Help The IBM Research division, which has designed several prototypes of its Digital AI cores and Analog AI cores in recent years, will continue to develop these chips at the center, Jeff Burns, IBM Research's director of AI Compute and director of the future AI Hardware Center, said. These new processors are expected to result in a 1,000-times improvement in AI compute performance efficiency over the next 10 years.