"An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a Course-of-Action Critiquer," by Gheorghe Tecuci, Mihai Boicu, Mike Bowman, and Dorin Marcu, describes a critiquing agent for military courses of action, a challenge problem set by the Defense Advanced Research Projects Agency's (DARPA) High-Performance Knowledge Bases Program. Murray Burke, the DARPA manager for this program, introduces the article by setting the context for the application. Ontologies also play a key role in the creation and management of a web portal developed by Steffen Staab and his colleagues at the University of Karlsruhe, discussed in their article, "Knowledge Portals: Ontologies at Work." "L As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed applications are systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are systems that are close to deployment and clearly show an innovative implementation of AI technologies. Papers submitted in this track can also describe efforts that examine the utility of different AI techniques for specific applications. All these case studies are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the technical challenges provided by real-world problems. Six deployed applications and 12 emerging application papers were presented plus 2 invited talks. Although no single theme emerges from this panoply of excellent applications, they served to demonstrate that the field continues to be fertile ground for innovation.

Cover: AI@50--We Are Golden, by James Gary, New York, New York. What Do We Know About Knowledge? Send all submissions to AI Magazine, AAAI, 445 Burgess Drive, Menlo Park, CA 94025-3442. Electronic submissions should be submitted using the web-based submissions form. Submissions information is available at aimagazine.org. Although no particular style is required for submissions, electronic submissions must be in PDF form. Authors whose work is accepted for publication, will be required to revise their work to conform reasonably to AI Magazine styles. If an article is accepted for publication, a new electronic copy will also be required. Although AI Ma ga zine generally grants great deference to an author's work the Magazine retains the right to determine the final published form of every article. Calendar items should be posted electronically (at least one month prior to the event or deadline). News items should be sent to the News Editor, AI Magazine, 445 Burgess Drive, Menlo Park, CA 94025-3442. Please do not send news releases via either email or fax, and do not send news releases to any of the other editors. Web-based job postings can be made using the job bank submissions form at aimagazine.org. Replacement copies (for current issue only) are available upon written request and a check for $10.00. Back issues are also available (cost may differ). Send replacement or back order requests to AAAI. Microform copies are available from ProQuest Information and Learning, 300 North Zeeb Road, Ann Arbor, MI 48106.

Southwest Research Institute and the U.S. Air Force Materiel Command designed and developed an automated system for the preparation of deficiency report analysis information reports ( Engineers and equipment specialists responsible for the troublesome part, or end item, review the MDR to identify the possible cause(s) of failure. In the past, engineers and equipment specialists have turned to operations research (OR) analysts to assist in item performance analysis. This analysis is usually time consuming and personnel intensive and requires information from many Air Force data systems. At the Oklahoma City Air Logistics Center (ALC), located at Tinker Air Force Base, data collection and analysis require two person-days. This document describes an item's SOURCE DATA: The data used to prepare this report came from the following sources: 1) Product Performance Subsystem (G099), 2) Supportability analysis Forecasting Evaluation (SAFE), 3) Flying Hours (G099), 4) MICAP Hours (D165B), and 5) VAMOSC (D160B).

Some application domains highlight the importance of distributed continual planning concepts; coordinating teams of unmanned ground vehicles in dynamic environments is an example of such a domain. In this article, I illustrate the ideas in, and promises of, distributed continual planning by showing how acquiring and distributing operator intent among multiple semiautonomous vehicles supports ongoing, cooperative mission elaboration and revision. It is this longer-term view that motivates the use of planning such that an agent should decide between alternative anticipated sequences of activities; otherwise, the application might be better served with simpler reactive agents that only decide on their very next actions. Second, what the agent knows about the application domain, or what the agent's objectives are, or both, can change over time. Information about the domain could be revealed incrementally or could dynamically change in ways outside the agent's control, and thus, the agent should continually reevaluate its ongoing plans and revise or elaborate them to accommodate the changes.

A collaborative effort between the White House Office of Media Affairs, the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT), and others quickly created a workable framework for wide-scale distribution of a stream of daily documents originating from the Executive Office of the President. The document stream includes daily press briefings, speeches by the President and other officials, backgrounders, and proclamations. In addition, the stream of released information includes special documents such as the National Performance Review's reports on reinventing government, the proposed healthcare reform legislation, and the yearly budgets. The Intelligent Information Infrastructure Project at the MIT Artificial Intelligence Laboratory created an information distribution server that functions as the focal point of the distribution chain. Documents are released from the Executive Office of the President through this system; they are sent from this system to a variety of archiving and retrieval systems around the country, most online services (for example, Compuserve, America Online), about 4000 direct subscribers to the MIT server, and a variety of other servers that further redistribute the documents.

It is a pleasure to acknowledge the ovel all guidance and suppol t for development of the demonstration work station by Daniel Wiener of the Joint, Tactical Fusion Progl am Office Danicl Vcntimiglia of the Rome Air Development Ccntel provided guidance and support fol the work station demonst,ration, and Capt,ain Richard Radcliffe of the Joint Tactical Fusion I'roglam OIlice provided thr dcvclopmcnt, and testing of the correlation tuner The processor aut omatically merges reports and stores descriptions of each detected item Operat,ols query the files constructed by the correlation processor, and the displays respond in formats selected by the operators The operators use these displays t,o deduce t,hc nat,ure and location of significant cncmy deployments By monitoring changes in the enemy deployments they try to anticipate enemy initiatives so they can help t,hcir commander count,er them. The fusion system did what it was designed to do: It, demonstrated the correlation, query, and display capabilities in a test bed in 1981, and the European Command, 1J.S. Forces has deployed the system in a limitedoperational capabilit,y mode. What, has yet to be dcmonstrated is that, these processes enable analysts to help a commander outwit an enemy. The crucial question is no longer whet,hcr sensor reports can be rapidly correlated, but rather how well humans can sort, through large amounts of correlated sensor data t,o assess situations rapidly and accurately. The same is presumably true of all other command and control syst,em elements that depend on human skill.

We pay particular attention to the design process, highlighting the objectives and properties that are important in the design of good prediction mechanisms. Whereas game theorists ask what outcome results from a game, mechanism designers ask what game produces a desired outcome. In this sense, game theorists act like scientists and mechanism designers like engineers. In this article, we survey a number of mechanisms created to elicit predictions, many newly proposed within the last decade. We focus on the engineering questions: How do they work and why?

We argue that second-order Markov logic is ideally suited for this purpose and propose an approach based on it. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables and instantiates these formulas with predicates from the target domain. Our approach has successfully transferred learned knowledge among molecular biology, web, and social network domains. For example, Wall Street firms often hire physicists to solve finance problems. Even though these two domains have superficially nothing in common, training as a physicist provides knowledge and skills that are highly applicable in finance (for example, solving differential equations and performing Monte Carlo simulations).

China is planning to build a 13.8 billion yuan (US$2.1 billion) technology park dedicated to developing artificial intelligence (AI), state-backed news agency Xinhua reported Wednesday. The campus will be constructed within five years and situated in the suburban Mentougou district in western Beijing. It will cover 54.87 hectares, Xinhua said. The technology park will be home to around 400 businesses and is expected to create an annual output value of about 50 billion yuan. High-speed big data, cloud computing, biometrics and so-called deep learning, a strand of AI, will be the focus of the new park.

Artificial intelligence, AI, has grabbed headlines, hype, and even consternation at the beast we are unleashing. Every powerful technology can be used for good and bad, be it nuclear or biotechnology, and the same is true for AI. While much of the public discourse from the likes of Elon Musk and Stephen Hawking reflects on sci-fi like dystopian visions of overlord AI's gone wrong (a scenario certainly worth discussing), there is a much more immediate threat when it comes to AI. Long before AI goes uncontrollable or takes over jobs, there lurks a much larger danger: AI in the hands of governments and/or bad actors used to push self-interested agendas against the greater good. For background, as a technology optimist and unapologetic supporter of further development, in 2014 I wrote about the massive dislocation in society AI may cause, and while our economic metrics like GDP, growth, and productivity may look awesome as a result, it may worsen the less visible, but in my opinion, far more critical metrics around income disparity and social mobility.