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.

"It was customary in old times for all Authors to enter the world of letters on their knees, and with uncovered head, and a bow of charming meekness write themselves some brainless dolt's "most humble and obedient servant." In later days, the same feigned subserviency has shown itself in other forms. One desires that some will kindly pardon the weakness and imbecility of his production; for, although these faults may exist in his book, he wrote under "most adverse circumstances," as the crying of a hopeful child, the quarrels of his poultry, and other disasters of the season. Another, clothed with the mantle of the sweetest self-complacency, looks out from his Preface, like a sun-dog on the morning sky, and merely shines out the query, "Am I not a Sun?" while he secures a retreat for his self-love, in case any body should suppose he ever indulged such a singular sentiment. They hold out to the world no need of aid in laying the foundations of their fame; and, however adverse the opinions of the times may be to their claims to renownThe Oregon Territory forms the terminus of these Travels; and, as that country is an object of much interest on both sides of the Atlantic, I have thought proper to preface my wanderings there by a brief discussion of the question as to whom it belongs.

Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business.

From harnessing artificial intelligence to understanding our origins, a panel of distinguished scientists outlined the grand challenges for science in the 21st century. Held at Nanyang Technological University and moderated by independent writer and lecturer Tor Norretranders, the panel session comprised Sydney Brenner, Nobel laureate in Physiology or Medicine; W. Brian Arthur, external professor at the Santa Fe Institute; Astronomer Royal Martin Rees; Terrence Sejnowski, Francis Crick Professor at the Salk Institute for Biological Studies; and Eörs Szathmáry, director of the Parmenides Center for the Conceptual Foundations of Science. The scientific industry has seen a marked shift towards industrial science--or science that directly benefits the economy--and away from basic research, said Sydney Brenner, a senior fellow at the Agency for Science, Technology and Research in Singapore. He lamented that scientists today lack a crucial truth-seeking mentality, by accepting the results of published research without challenging assumptions. In science, where research is built upon research--which potentially leads to an accumulation of mistakes--the practice of critical evaluation is all the more pertinent, Brenner said.

This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.

In the last decade, in the Semantic Web field, knowledge bases have attracted tremendous interest from both academia and industry and many large knowledge bases are now available. However, both generation of new knowledge and population of already existing knowledge bases with new facts face several challenges. Most of the time knowledge bases have been manually built, resulting in a highly specialistic and time consuming activity. Nevertheless, sources of unstructured and semi-structured data are still growing at a much faster rate than structured ones, as such it could be desirable to exploit such a large non-structured sources to populate structured knowledge bases. In the Semantic Web, a major cornerstone of knowledge bases are ontologies and schemas that play a key role for providing common vocabularies and for describing and constructing the Web of Data.

A host of leading industry experts gathered to discuss the launch of The Drum's Cannes Lions special edition guest edited by IBM's artificial intelligence (AI) technology Watson, which used machine learning to channel the creativity of David Ogilvy, arguably the godfather of advertising. The panel session, held in association with Quantcast, saw assembled marketers listen in on the thoughts of Amber Case, a cyborg anthropologist who examines the interaction between humans and technology; Oliver Cox, solutions architect, IBM Watson ecosystem; Konrad Feldman, CEO of Quantcast; David Shing, digital prophet for AOL; plus Todd Krugmann, president of O&M Japan. IBM's Cox added that the latest issue of The Drum bore testament to this potential union of data-led machine learning, and the creative process. Meanwhile, IBM's Cox further explained how such an offering could aid brands' communication strategies: "Watson would not create a personality - it will help you create the personality that's best for your brand [with elements of human moderation]."

It was a dark, overcast day, with clouds hovering low. The room was kept at the most appropriate temperatures and humidity, as usual. Yoko sat on a couch in an untidy manner, killing time with a silly game. But she would not talk to me. So begins a short story titled "A day when a computer writes fiction."