This close relationship between AI Magazine and IAAI is no coincidence. Much of our society knows about and interacts with AI mainly through its applications. Applications are the source of many problems that AI research seeks to address, and we often measure our progress through successful applications. Applications are how AI makes an impact on the world. Hence, as "the journal of record for the AI community," it is only logical for AI Magazine to cover AI applications extensively.

February 9, 2016 -- Apcera, the leader in enterprise container management, today announced its presence at GigaOm AI, taking place February 15-16 in San Francisco. Mark Thiele, Apcera's chief strategy officer, will be moderating an industry panel titled, "Customer Experiences in AI," to be held on Thursday, February 16. The panel will also feature executives from Comcast, Cybric and Talla. Thiele is a globally recognized speaker and visionary on the topics of AI, cloud, IoT, data center, DevOps and IT leadership. Connect with Apcera at GigaOm AI To schedule one-on-one meetings with Apcera at the event, send email to press@apcera.com.

This position paper describes an AI application for game spectators, e.g., those watching Twitch. The aim of this application is to automatically generate game plays by nonplayer characters -- not human players -- and recommend those plays to spectators. The generation part leads to development of a new field: procedural play generation (PPG). The recommendation part requires new techniques in recommender systems (RS) for incorporation of play content into RS to obtain promising recommendation results. Rather than proposing solutions to all relevant topics, this paper aims at drawing attention to this new field and serves as a seed for discussion and collaboration among the readers, workshop participants, and authors.

A major challenge for using data to make predictions is distinguishing what is meaningful from noise. The image represents one approach that visually indicates the complexity of the problem by highlighting some links in a network and deleting other possible links, with the hole indicating the more meaningful information. We have tried to predict the future since ancient times when shamans looked for patterns in smoking entrails. As this special section explores, prediction is now a developing science. Essays probe such questions as how to allocate limited resources, whether a country will descend into conflict, and who will likely win an election or publish a high-impact paper, as well as looking at how standards should develop in this emerging field.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines.

During the last 36 years of its illustrious history, ince its inception in 1980, AI Magazine has played an the magazine has gone through several transformations. Now the magazine is going through another transition: David Leake, the longtime editor-in-chief is moving on after 17 years of distinguished service, though fortunately he will continue to advise us as editor emeritus. I am honored and delighted to follow David. I have been a member of the Editorial Board of AI Magazine for several years, associate editor since August 2015, and editor elect since February 2016; my tenure as editor-in-chief starts with this winter 2016 issue. I thank David, Managing Editor Mike Hamilton, former AAAI President Tom Dietterich, and AAAI for recruiting me for this challenge....

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

This editorial introduces answer set programming, a vibrant research area in computational knowledge representation and declarative programming. We give a brief overview of the articles that form this special issue on answer set programming and of the main topics they discuss.

What distinguishes ASP from other declarative paradigms, like satisfiability (SAT) or constraint solving (CSP), is its underlying modeling language and the semantics involved. Problems are specified using logic programminglike rules, with some convenient extensions facilitating compact and readable problem descriptions. Sets of such rules, or answer set programs, come with an intuitive, well-defined and, by now, well-accepted semantics. This semantics has its roots in research in knowledge representation, in particular nonmonotonic reasoning, and avoids the pitfalls of earlier attempts such as the procedural semantics of Prolog based on negation as finite failure. This semantics was originally called the stable-model semantics and was defined for normal logic programs only, that is, programs consisting of rules with a single atom in the head and any finite number of atoms, possibly preceded by default negation, not, in the body. Stable models were later generalized to broader classes of programs, where the semantics can no longer be defined in terms of sets of atoms, which is a natural representation of classical models. Instead, it was defined by means of some sets of literals. For this reason the term answer set was adopted as more adequate (although answer sets also have a straightforward interpretation as models, albeit three-valued ones). Over the last decade or so, ASP has evolved into a vibrant and active research area that produced not only theoretical insights, but also highly effective and useful software tools and interesting and promising applications.